{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "wdeKOEkv1Fe8" }, "source": [ "##### Copyright 2021 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2024-08-02T09:25:14.084574Z", "iopub.status.busy": "2024-08-02T09:25:14.083960Z", "iopub.status.idle": "2024-08-02T09:25:14.087750Z", "shell.execute_reply": "2024-08-02T09:25:14.087159Z" }, "id": "c2jyGuiG1gHr" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "23R0Z9RojXYW" }, "source": [ "# TFX Keras Component Tutorial\n", "\n", "***A Component-by-Component Introduction to TensorFlow Extended (TFX)***" ] }, { "cell_type": "markdown", "metadata": { "id": "LidV2qsXm4XC" }, "source": [ "Note: We recommend running this tutorial in a Colab notebook, with no setup required! Just click \"Run in Google Colab\".\n", "\n", "
\n", "\n", "\n", "\n", "\n", "
\n", "View on TensorFlow.org\n", "Run in Google Colab\n", "View source on GitHub\n", "Download notebook
" ] }, { "cell_type": "markdown", "metadata": { "id": "KAD1tLoTm_QS" }, "source": [ "\n", "This Colab-based tutorial will interactively walk through each built-in component of TensorFlow Extended (TFX).\n", "\n", "It covers every step in an end-to-end machine learning pipeline, from data ingestion to pushing a model to serving.\n", "\n", "When you're done, the contents of this notebook can be automatically exported as TFX pipeline source code, which you can orchestrate with Apache Airflow and Apache Beam.\n", "\n", "Note: This notebook demonstrates the use of native Keras models in TFX pipelines. **TFX only supports the TensorFlow 2 version of Keras**." ] }, { "cell_type": "markdown", "metadata": { "id": "sfSQ-kX-MLEr" }, "source": [ "## Background\n", "This notebook demonstrates how to use TFX in a Jupyter/Colab environment. Here, we walk through the Chicago Taxi example in an interactive notebook.\n", "\n", "Working in an interactive notebook is a useful way to become familiar with the structure of a TFX pipeline. It's also useful when doing development of your own pipelines as a lightweight development environment, but you should be aware that there are differences in the way interactive notebooks are orchestrated, and how they access metadata artifacts.\n", "\n", "### Orchestration\n", "\n", "In a production deployment of TFX, you will use an orchestrator such as Apache Airflow, Kubeflow Pipelines, or Apache Beam to orchestrate a pre-defined pipeline graph of TFX components. In an interactive notebook, the notebook itself is the orchestrator, running each TFX component as you execute the notebook cells.\n", "\n", "### Metadata\n", "\n", "In a production deployment of TFX, you will access metadata through the ML Metadata (MLMD) API. MLMD stores metadata properties in a database such as MySQL or SQLite, and stores the metadata payloads in a persistent store such as on your filesystem. In an interactive notebook, both properties and payloads are stored in an ephemeral SQLite database in the `/tmp` directory on the Jupyter notebook or Colab server." ] }, { "cell_type": "markdown", "metadata": { "id": "2GivNBNYjb3b" }, "source": [ "## Setup\n", "First, we install and import the necessary packages, set up paths, and download data." ] }, { "cell_type": "markdown", "metadata": { "id": "Fmgi8ZvQkScg" }, "source": [ "### Upgrade Pip\n", "\n", "To avoid upgrading Pip in a system when running locally, check to make sure that we're running in Colab. Local systems can of course be upgraded separately." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:14.091901Z", "iopub.status.busy": "2024-08-02T09:25:14.091345Z", "iopub.status.idle": "2024-08-02T09:25:14.099121Z", "shell.execute_reply": "2024-08-02T09:25:14.098554Z" }, "id": "as4OTe2ukSqm" }, "outputs": [], "source": [ "import sys\n", "if 'google.colab' in sys.modules:\n", " !pip install --upgrade pip" ] }, { "cell_type": "markdown", "metadata": { "id": "MZOYTt1RW4TK" }, "source": [ "### Install TFX\n", "\n", "**Note: In Google Colab, because of package updates, the first time you run this cell you must restart the runtime (Runtime > Restart runtime ...).**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:14.102628Z", "iopub.status.busy": "2024-08-02T09:25:14.102016Z", "iopub.status.idle": "2024-08-02T09:25:16.513236Z", "shell.execute_reply": "2024-08-02T09:25:16.512228Z" }, "id": "S4SQA7Q5nej3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tfx in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (1.15.1)\r\n", "Requirement already satisfied: ml-pipelines-sdk==1.15.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.15.1)\r\n", "Requirement already satisfied: absl-py<2.0.0,>=0.9 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.4.0)\r\n", "Requirement already satisfied: ml-metadata<1.16.0,>=1.15.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.15.0)\r\n", "Requirement already satisfied: packaging>=22 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (24.1)\r\n", "Requirement already satisfied: portpicker<2,>=1.3.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.6.0)\r\n", "Requirement already satisfied: protobuf<5,>=3.20.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (3.20.3)\r\n", "Requirement already satisfied: docker<5,>=4.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (4.4.4)\r\n", "Requirement already satisfied: google-apitools<1,>=0.5 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (0.5.31)\r\n", "Requirement already satisfied: google-api-python-client<2,>=1.8 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.12.11)\r\n", "Requirement already satisfied: jinja2<4,>=2.7.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (3.1.4)\r\n", "Requirement already satisfied: typing-extensions<5,>=3.10.0.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (4.12.2)\r\n", "Requirement already satisfied: apache-beam<3,>=2.47 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.57.0)\r\n", "Requirement already satisfied: attrs<24,>=19.3.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (23.2.0)\r\n", "Requirement already satisfied: click<9,>=7 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (8.1.7)\r\n", "Requirement already satisfied: google-api-core<3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (2.19.1)\r\n", "Requirement already satisfied: google-cloud-aiplatform<2,>=1.6.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.60.0)\r\n", "Requirement already satisfied: google-cloud-bigquery<4,>=3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (3.25.0)\r\n", "Requirement already satisfied: grpcio<2,>=1.28.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.65.2)\r\n", "Requirement already satisfied: keras-tuner!=1.4.0,!=1.4.1,<2,>=1.0.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.4.7)\r\n", "Requirement already satisfied: kubernetes<13,>=10.0.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (12.0.1)\r\n", "Requirement already satisfied: numpy<2,>=1.16 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.26.4)\r\n", "Requirement already satisfied: pyarrow<11,>=10 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (10.0.1)\r\n", "Requirement already satisfied: scipy<1.13 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.12.0)\r\n", "Requirement already satisfied: pyyaml<7,>=6 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (6.0.1)\r\n", "Requirement already satisfied: tensorflow<2.16,>=2.15.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (2.15.1)\r\n", "Requirement already satisfied: tensorflow-hub<0.16,>=0.15.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (0.15.0)\r\n", "Requirement already satisfied: tensorflow-data-validation<1.16.0,>=1.15.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.15.1)\r\n", "Requirement already satisfied: tensorflow-model-analysis<0.47.0,>=0.46.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (0.46.0)\r\n", "Requirement already satisfied: tensorflow-serving-api<2.16,>=2.15 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (2.15.1)\r\n", "Requirement already satisfied: tensorflow-transform<1.16.0,>=1.15.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.15.0)\r\n", "Requirement already satisfied: tfx-bsl<1.16.0,>=1.15.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tfx) (1.15.1)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: crcmod<2.0,>=1.7 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (1.7)\r\n", "Requirement already satisfied: orjson<4,>=3.9.7 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (3.10.6)\r\n", "Requirement already satisfied: dill<0.3.2,>=0.3.1.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.3.1.1)\r\n", "Requirement already satisfied: cloudpickle~=2.2.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (2.2.1)\r\n", "Requirement already satisfied: fastavro<2,>=0.23.6 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (1.9.5)\r\n", "Requirement already satisfied: fasteners<1.0,>=0.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.19)\r\n", "Requirement already satisfied: hdfs<3.0.0,>=2.1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (2.7.3)\r\n", "Requirement already satisfied: httplib2<0.23.0,>=0.8 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.22.0)\r\n", "Requirement already satisfied: jsonschema<5.0.0,>=4.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (4.23.0)\r\n", "Requirement already satisfied: jsonpickle<4.0.0,>=3.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (3.2.2)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: objsize<0.8.0,>=0.6.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.7.0)\r\n", "Requirement already satisfied: pymongo<5.0.0,>=3.8.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (4.8.0)\r\n", "Requirement already satisfied: proto-plus<2,>=1.7.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (1.24.0)\r\n", "Requirement already satisfied: pydot<2,>=1.2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (1.4.2)\r\n", "Requirement already satisfied: python-dateutil<3,>=2.8.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (2.9.0.post0)\r\n", "Requirement already satisfied: pytz>=2018.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (2024.1)\r\n", "Requirement already satisfied: redis<6,>=5.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (5.0.8)\r\n", "Requirement already satisfied: regex>=2020.6.8 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (2024.7.24)\r\n", "Requirement already satisfied: requests!=2.32.*,<3.0.0,>=2.24.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (2.31.0)\r\n", "Requirement already satisfied: zstandard<1,>=0.18.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.23.0)\r\n", "Requirement already satisfied: pyarrow-hotfix<1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.6)\r\n", "Requirement already satisfied: js2py<1,>=0.74 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.74)\r\n", "Requirement already satisfied: cachetools<6,>=3.1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (5.4.0)\r\n", "Requirement already satisfied: google-auth<3,>=1.18.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.32.0)\r\n", "Requirement already satisfied: google-auth-httplib2<0.3.0,>=0.1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (0.2.0)\r\n", "Requirement already satisfied: google-cloud-datastore<3,>=2.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.19.0)\r\n", "Requirement already satisfied: google-cloud-pubsub<3,>=2.1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.23.0)\r\n", "Requirement already satisfied: google-cloud-pubsublite<2,>=1.2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (1.11.1)\r\n", "Requirement already satisfied: google-cloud-storage<3,>=2.16.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.18.0)\r\n", "Requirement already satisfied: google-cloud-bigquery-storage<3,>=2.6.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.25.0)\r\n", "Requirement already satisfied: google-cloud-core<3,>=2.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.4.1)\r\n", "Requirement already satisfied: google-cloud-bigtable<3,>=2.19.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.25.0)\r\n", "Requirement already satisfied: google-cloud-spanner<4,>=3.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (3.48.0)\r\n", "Requirement already satisfied: google-cloud-dlp<4,>=3.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (3.20.0)\r\n", "Requirement already satisfied: google-cloud-language<3,>=2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.14.0)\r\n", "Requirement already satisfied: google-cloud-videointelligence<3,>=2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (2.13.5)\r\n", "Requirement already satisfied: google-cloud-vision<4,>=2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (3.7.4)\r\n", "Requirement already satisfied: google-cloud-recommendations-ai<0.11.0,>=0.1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from apache-beam[gcp]<3,>=2.47->tfx) (0.10.12)\r\n", "Requirement already satisfied: six>=1.4.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from docker<5,>=4.1->tfx) (1.16.0)\r\n", "Requirement already satisfied: websocket-client>=0.32.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from docker<5,>=4.1->tfx) (1.8.0)\r\n", "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-api-core<3->tfx) (1.63.2)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-api-python-client<2,>=1.8->tfx) (3.0.1)\r\n", "Requirement already satisfied: oauth2client>=1.4.12 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-apitools<1,>=0.5->tfx) (4.1.3)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: google-cloud-resource-manager<3.0.0dev,>=1.3.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-aiplatform<2,>=1.6.2->tfx) (1.12.5)\r\n", "Requirement already satisfied: shapely<3.0.0dev in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-aiplatform<2,>=1.6.2->tfx) (2.0.5)\r\n", "Requirement already satisfied: pydantic<3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-aiplatform<2,>=1.6.2->tfx) (1.10.17)\r\n", "Requirement already satisfied: docstring-parser<1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-aiplatform<2,>=1.6.2->tfx) (0.16)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: google-resumable-media<3.0dev,>=0.6.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-bigquery<4,>=3->tfx) (2.7.1)\r\n", "Requirement already satisfied: MarkupSafe>=2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jinja2<4,>=2.7.3->tfx) (2.1.5)\r\n", "Requirement already satisfied: keras in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from keras-tuner!=1.4.0,!=1.4.1,<2,>=1.0.4->tfx) (2.15.0)\r\n", "Requirement already satisfied: kt-legacy in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from keras-tuner!=1.4.0,!=1.4.1,<2,>=1.0.4->tfx) (1.0.5)\r\n", "Requirement already satisfied: certifi>=14.05.14 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from kubernetes<13,>=10.0.1->tfx) (2024.7.4)\r\n", "Requirement already satisfied: setuptools>=21.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from kubernetes<13,>=10.0.1->tfx) (72.1.0)\r\n", "Requirement already satisfied: requests-oauthlib in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from kubernetes<13,>=10.0.1->tfx) (2.0.0)\r\n", "Requirement already satisfied: urllib3>=1.24.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from kubernetes<13,>=10.0.1->tfx) (1.26.19)\r\n", "Requirement already satisfied: psutil in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from portpicker<2,>=1.3.1->tfx) (6.0.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: astunparse>=1.6.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (1.6.3)\r\n", "Requirement already satisfied: flatbuffers>=23.5.26 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (24.3.25)\r\n", "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (0.6.0)\r\n", "Requirement already satisfied: google-pasta>=0.1.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (0.2.0)\r\n", "Requirement already satisfied: h5py>=2.9.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (3.11.0)\r\n", "Requirement already satisfied: libclang>=13.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (18.1.1)\r\n", "Requirement already satisfied: ml-dtypes~=0.3.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (0.3.2)\r\n", "Requirement already satisfied: opt-einsum>=2.3.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (3.3.0)\r\n", "Requirement already satisfied: termcolor>=1.1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (2.4.0)\r\n", "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (1.14.1)\r\n", "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (0.37.1)\r\n", "Requirement already satisfied: tensorboard<2.16,>=2.15 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (2.15.2)\r\n", "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow<2.16,>=2.15.0->tfx) (2.15.0)\r\n", "Requirement already satisfied: joblib>=1.2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-data-validation<1.16.0,>=1.15.1->tfx) (1.4.2)\r\n", "Requirement already satisfied: pandas<2,>=1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-data-validation<1.16.0,>=1.15.1->tfx) (1.5.3)\r\n", "Requirement already satisfied: pyfarmhash<0.4,>=0.2.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-data-validation<1.16.0,>=1.15.1->tfx) (0.3.2)\r\n", "Requirement already satisfied: tensorflow-metadata<1.16,>=1.15.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-data-validation<1.16.0,>=1.15.1->tfx) (1.15.0)\r\n", "Requirement already satisfied: ipython<8,>=7 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (7.34.0)\r\n", "Requirement already satisfied: ipywidgets<8,>=7 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (7.8.3)\r\n", "Requirement already satisfied: pillow>=9.4.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (10.4.0)\r\n", "Requirement already satisfied: rouge-score<2,>=0.1.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.1.2)\r\n", "Requirement already satisfied: sacrebleu<4,>=2.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.4.2)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: wheel<1.0,>=0.23.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from astunparse>=1.6.0->tensorflow<2.16,>=2.15.0->tfx) (0.43.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: grpcio-status<2.0.dev0,>=1.33.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<3.0.0dev,>=1.34.1->google-cloud-aiplatform<2,>=1.6.2->tfx) (1.48.2)\r\n", "Requirement already satisfied: pyasn1-modules>=0.2.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-auth<3,>=1.18.0->apache-beam[gcp]<3,>=2.47->tfx) (0.4.0)\r\n", "Requirement already satisfied: rsa<5,>=3.1.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-auth<3,>=1.18.0->apache-beam[gcp]<3,>=2.47->tfx) (4.9)\r\n", "Requirement already satisfied: grpc-google-iam-v1<1.0.0dev,>=0.12.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-bigtable<3,>=2.19.0->apache-beam[gcp]<3,>=2.47->tfx) (0.13.1)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: overrides<8.0.0,>=6.0.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-pubsublite<2,>=1.2.0->apache-beam[gcp]<3,>=2.47->tfx) (7.7.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: sqlparse>=0.4.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-spanner<4,>=3.0.0->apache-beam[gcp]<3,>=2.47->tfx) (0.5.1)\r\n", "Requirement already satisfied: grpc-interceptor>=0.15.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-spanner<4,>=3.0.0->apache-beam[gcp]<3,>=2.47->tfx) (0.15.4)\r\n", "Requirement already satisfied: google-crc32c<2.0dev,>=1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from google-cloud-storage<3,>=2.16.0->apache-beam[gcp]<3,>=2.47->tfx) (1.5.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: docopt in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from hdfs<3.0.0,>=2.1.0->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.6.2)\r\n", "Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from httplib2<0.23.0,>=0.8->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (3.1.2)\r\n", "Requirement already satisfied: jedi>=0.16 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.19.1)\r\n", "Requirement already satisfied: decorator in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (5.1.1)\r\n", "Requirement already satisfied: pickleshare in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.7.5)\r\n", "Requirement already satisfied: traitlets>=4.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (5.14.3)\r\n", "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (3.0.47)\r\n", "Requirement already satisfied: pygments in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.18.0)\r\n", "Requirement already satisfied: backcall in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.2.0)\r\n", "Requirement already satisfied: matplotlib-inline in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.1.7)\r\n", "Requirement already satisfied: pexpect>4.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (4.9.0)\r\n", "Requirement already satisfied: comm>=0.1.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.2.2)\r\n", "Requirement already satisfied: ipython-genutils~=0.2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.2.0)\r\n", "Requirement already satisfied: widgetsnbextension~=3.6.8 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (3.6.8)\r\n", "Requirement already satisfied: jupyterlab-widgets<3,>=1.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.1.9)\r\n", "Requirement already satisfied: tzlocal>=1.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from js2py<1,>=0.74->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (5.2)\r\n", "Requirement already satisfied: pyjsparser>=2.5.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from js2py<1,>=0.74->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (2.7.1)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jsonschema<5.0.0,>=4.0.0->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (2023.12.1)\r\n", "Requirement already satisfied: referencing>=0.28.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jsonschema<5.0.0,>=4.0.0->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.35.1)\r\n", "Requirement already satisfied: rpds-py>=0.7.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jsonschema<5.0.0,>=4.0.0->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (0.19.1)\r\n", "Requirement already satisfied: pyasn1>=0.1.7 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from oauth2client>=1.4.12->google-apitools<1,>=0.5->tfx) (0.6.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: dnspython<3.0.0,>=1.16.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from pymongo<5.0.0,>=3.8.0->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (2.6.1)\r\n", "Requirement already satisfied: async-timeout>=4.0.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from redis<6,>=5.0.0->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (4.0.3)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: charset-normalizer<4,>=2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from requests!=2.32.*,<3.0.0,>=2.24.0->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (3.3.2)\r\n", "Requirement already satisfied: idna<4,>=2.5 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from requests!=2.32.*,<3.0.0,>=2.24.0->apache-beam<3,>=2.47->apache-beam[gcp]<3,>=2.47->tfx) (3.7)\r\n", "Requirement already satisfied: nltk in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from rouge-score<2,>=0.1.2->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (3.8.1)\r\n", "Requirement already satisfied: portalocker in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from sacrebleu<4,>=2.3->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.10.1)\r\n", "Requirement already satisfied: tabulate>=0.8.9 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from sacrebleu<4,>=2.3->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.9.0)\r\n", "Requirement already satisfied: colorama in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from sacrebleu<4,>=2.3->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.4.6)\r\n", "Requirement already satisfied: lxml in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from sacrebleu<4,>=2.3->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (5.2.2)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow<2.16,>=2.15.0->tfx) (1.2.1)\r\n", "Requirement already satisfied: markdown>=2.6.8 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow<2.16,>=2.15.0->tfx) (3.6)\r\n", "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow<2.16,>=2.15.0->tfx) (0.7.2)\r\n", "Requirement already satisfied: werkzeug>=1.0.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorboard<2.16,>=2.15->tensorflow<2.16,>=2.15.0->tfx) (3.0.3)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: oauthlib>=3.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from requests-oauthlib->kubernetes<13,>=10.0.1->tfx) (3.2.2)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jedi>=0.16->ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.8.4)\r\n", "Requirement already satisfied: importlib-metadata>=4.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from markdown>=2.6.8->tensorboard<2.16,>=2.15->tensorflow<2.16,>=2.15.0->tfx) (8.2.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: ptyprocess>=0.5 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from pexpect>4.3->ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.7.0)\r\n", "Requirement already satisfied: wcwidth in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.2.13)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: notebook>=4.4.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (7.2.1)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tqdm in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nltk->rouge-score<2,>=0.1.2->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (4.66.4)\r\n", "Requirement already satisfied: zipp>=0.5 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.16,>=2.15->tensorflow<2.16,>=2.15.0->tfx) (3.19.2)\r\n", "Requirement already satisfied: jupyter-server<3,>=2.4.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.14.2)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: jupyterlab-server<3,>=2.27.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.27.3)\r\n", "Requirement already satisfied: jupyterlab<4.3,>=4.2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (4.2.4)\r\n", "Requirement already satisfied: notebook-shim<0.3,>=0.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.2.4)\r\n", "Requirement already satisfied: tornado>=6.2.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (6.4.1)\r\n", "Requirement already satisfied: anyio>=3.1.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (4.4.0)\r\n", "Requirement already satisfied: argon2-cffi>=21.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (23.1.0)\r\n", "Requirement already satisfied: jupyter-client>=7.4.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (8.6.2)\r\n", "Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (5.7.2)\r\n", "Requirement already satisfied: jupyter-events>=0.9.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.10.0)\r\n", "Requirement already satisfied: jupyter-server-terminals>=0.4.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.5.3)\r\n", "Requirement already satisfied: nbconvert>=6.4.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (7.16.4)\r\n", "Requirement already satisfied: nbformat>=5.3.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (5.10.4)\r\n", "Requirement already satisfied: prometheus-client>=0.9 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.20.0)\r\n", "Requirement already satisfied: pyzmq>=24 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (26.0.3)\r\n", "Requirement already satisfied: send2trash>=1.8.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.8.3)\r\n", "Requirement already satisfied: terminado>=0.8.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.18.1)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: async-lru>=1.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyterlab<4.3,>=4.2.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.0.4)\r\n", "Requirement already satisfied: httpx>=0.25.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyterlab<4.3,>=4.2.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.27.0)\r\n", "Requirement already satisfied: ipykernel>=6.5.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyterlab<4.3,>=4.2.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (6.29.5)\r\n", "Requirement already satisfied: jupyter-lsp>=2.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyterlab<4.3,>=4.2.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.2.5)\r\n", "Requirement already satisfied: tomli>=1.2.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyterlab<4.3,>=4.2.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.0.1)\r\n", "Requirement already satisfied: babel>=2.10 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.15.0)\r\n", "Requirement already satisfied: json5>=0.9.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.9.25)\r\n", "Requirement already satisfied: sniffio>=1.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from anyio>=3.1.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.3.1)\r\n", "Requirement already satisfied: exceptiongroup>=1.0.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from anyio>=3.1.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.2.2)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: argon2-cffi-bindings in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (21.2.0)\r\n", "Requirement already satisfied: httpcore==1.* in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from httpx>=0.25.0->jupyterlab<4.3,>=4.2.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.0.5)\r\n", "Requirement already satisfied: h11<0.15,>=0.13 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from httpcore==1.*->httpx>=0.25.0->jupyterlab<4.3,>=4.2.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.14.0)\r\n", "Requirement already satisfied: debugpy>=1.6.5 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipykernel>=6.5.0->jupyterlab<4.3,>=4.2.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.8.2)\r\n", "Requirement already satisfied: nest-asyncio in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from ipykernel>=6.5.0->jupyterlab<4.3,>=4.2.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.6.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: platformdirs>=2.5 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-core!=5.0.*,>=4.12->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (4.2.2)\r\n", "Requirement already satisfied: python-json-logger>=2.0.4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.0.7)\r\n", "Requirement already satisfied: rfc3339-validator in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.1.4)\r\n", "Requirement already satisfied: rfc3986-validator>=0.1.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.1.1)\r\n", "Requirement already satisfied: beautifulsoup4 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (4.12.3)\r\n", "Requirement already satisfied: bleach!=5.0.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (6.1.0)\r\n", "Requirement already satisfied: defusedxml in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.7.1)\r\n", "Requirement already satisfied: jupyterlab-pygments in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.3.0)\r\n", "Requirement already satisfied: mistune<4,>=2.0.3 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (3.0.2)\r\n", "Requirement already satisfied: nbclient>=0.5.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.10.0)\r\n", "Requirement already satisfied: pandocfilters>=1.4.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.5.1)\r\n", "Requirement already satisfied: tinycss2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.3.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: fastjsonschema>=2.15 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from nbformat>=5.3.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.20.0)\r\n", "Requirement already satisfied: webencodings in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from bleach!=5.0.0->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (0.5.1)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: fqdn in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.5.1)\r\n", "Requirement already satisfied: isoduration in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (20.11.0)\r\n", "Requirement already satisfied: jsonpointer>1.13 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (3.0.0)\r\n", "Requirement already satisfied: uri-template in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.3.0)\r\n", "Requirement already satisfied: webcolors>=24.6.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (24.6.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: cffi>=1.0.1 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from argon2-cffi-bindings->argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.16.0)\r\n", "Requirement already satisfied: soupsieve>1.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from beautifulsoup4->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.5)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pycparser in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.22)\r\n", "Requirement already satisfied: arrow>=0.15.0 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (1.3.0)\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: types-python-dateutil>=2.8.10 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.8->ipywidgets<8,>=7->tensorflow-model-analysis<0.47.0,>=0.46.0->tfx) (2.9.0.20240316)\r\n" ] } ], "source": [ "!pip install tfx" ] }, { "cell_type": "markdown", "metadata": { "id": "EwT0nov5QO1M" }, "source": [ "## Did you restart the runtime?\n", "\n", "If you are using Google Colab, the first time that you run the cell above, you must restart the runtime (Runtime > Restart runtime ...). This is because of the way that Colab loads packages." ] }, { "cell_type": "markdown", "metadata": { "id": "N-ePgV0Lj68Q" }, "source": [ "### Import packages\n", "We import necessary packages, including standard TFX component classes." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:16.517959Z", "iopub.status.busy": "2024-08-02T09:25:16.517700Z", "iopub.status.idle": "2024-08-02T09:25:22.497477Z", "shell.execute_reply": "2024-08-02T09:25:22.496725Z" }, "id": "YIqpWK9efviJ" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-08-02 09:25:16.953664: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2024-08-02 09:25:16.953708: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2024-08-02 09:25:16.955358: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] } ], "source": [ "import os\n", "import pprint\n", "import tempfile\n", "import urllib\n", "\n", "import absl\n", "import tensorflow as tf\n", "import tensorflow_model_analysis as tfma\n", "tf.get_logger().propagate = False\n", "pp = pprint.PrettyPrinter()\n", "\n", "from tfx import v1 as tfx\n", "from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext\n", "\n", "%load_ext tfx.orchestration.experimental.interactive.notebook_extensions.skip" ] }, { "cell_type": "markdown", "metadata": { "id": "wCZTHRy0N1D6" }, "source": [ "Let's check the library versions." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:22.501750Z", "iopub.status.busy": "2024-08-02T09:25:22.501357Z", "iopub.status.idle": "2024-08-02T09:25:22.505460Z", "shell.execute_reply": "2024-08-02T09:25:22.504750Z" }, "id": "eZ4K18_DN2D8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow version: 2.15.1\n", "TFX version: 1.15.1\n" ] } ], "source": [ "print('TensorFlow version: {}'.format(tf.__version__))\n", "print('TFX version: {}'.format(tfx.__version__))" ] }, { "cell_type": "markdown", "metadata": { "id": "ufJKQ6OvkJlY" }, "source": [ "### Set up pipeline paths" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:22.509378Z", "iopub.status.busy": "2024-08-02T09:25:22.508758Z", "iopub.status.idle": "2024-08-02T09:25:22.513264Z", "shell.execute_reply": "2024-08-02T09:25:22.512531Z" }, "id": "ad5JLpKbf6sN" }, "outputs": [], "source": [ "# This is the root directory for your TFX pip package installation.\n", "_tfx_root = tfx.__path__[0]\n", "\n", "# This is the directory containing the TFX Chicago Taxi Pipeline example.\n", "_taxi_root = os.path.join(_tfx_root, 'examples/chicago_taxi_pipeline')\n", "\n", "# This is the path where your model will be pushed for serving.\n", "_serving_model_dir = os.path.join(\n", " tempfile.mkdtemp(), 'serving_model/taxi_simple')\n", "\n", "# Set up logging.\n", "absl.logging.set_verbosity(absl.logging.INFO)" ] }, { "cell_type": "markdown", "metadata": { "id": "n2cMMAbSkGfX" }, "source": [ "### Download example data\n", "We download the example dataset for use in our TFX pipeline.\n", "\n", "The dataset we're using is the [Taxi Trips dataset](https://data.cityofchicago.org/Transportation/Taxi-Trips/wrvz-psew) released by the City of Chicago. The columns in this dataset are:\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
pickup_community_areafaretrip_start_month
trip_start_hourtrip_start_daytrip_start_timestamp
pickup_latitudepickup_longitudedropoff_latitude
dropoff_longitudetrip_milespickup_census_tract
dropoff_census_tractpayment_typecompany
trip_secondsdropoff_community_areatips
\n", "\n", "With this dataset, we will build a model that predicts the `tips` of a trip." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:22.516582Z", "iopub.status.busy": "2024-08-02T09:25:22.516169Z", "iopub.status.idle": "2024-08-02T09:25:22.739796Z", "shell.execute_reply": "2024-08-02T09:25:22.739180Z" }, "id": "BywX6OUEhAqn" }, "outputs": [ { "data": { "text/plain": [ "('/tmpfs/tmp/tfx-datajp95savt/data.csv',\n", " )" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_data_root = tempfile.mkdtemp(prefix='tfx-data')\n", "DATA_PATH = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/chicago_taxi_pipeline/data/simple/data.csv'\n", "_data_filepath = os.path.join(_data_root, \"data.csv\")\n", "urllib.request.urlretrieve(DATA_PATH, _data_filepath)" ] }, { "cell_type": "markdown", "metadata": { "id": "blZC1sIQOWfH" }, "source": [ "Take a quick look at the CSV file." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:22.743193Z", "iopub.status.busy": "2024-08-02T09:25:22.742566Z", "iopub.status.idle": "2024-08-02T09:25:22.879282Z", "shell.execute_reply": "2024-08-02T09:25:22.878465Z" }, "id": "c5YPeLPFOXaD" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pickup_community_area,fare,trip_start_month,trip_start_hour,trip_start_day,trip_start_timestamp,pickup_latitude,pickup_longitude,dropoff_latitude,dropoff_longitude,trip_miles,pickup_census_tract,dropoff_census_tract,payment_type,company,trip_seconds,dropoff_community_area,tips\r\n", ",12.45,5,19,6,1400269500,,,,,0.0,,,Credit Card,Chicago Elite Cab Corp. (Chicago Carriag,0,,0.0\r\n", ",0,3,19,5,1362683700,,,,,0,,,Unknown,Chicago Elite Cab Corp.,300,,0\r\n", "60,27.05,10,2,3,1380593700,41.836150155,-87.648787952,,,12.6,,,Cash,Taxi Affiliation Services,1380,,0.0\r\n", "10,5.85,10,1,2,1382319000,41.985015101,-87.804532006,,,0.0,,,Cash,Taxi Affiliation Services,180,,0.0\r\n", "14,16.65,5,7,5,1369897200,41.968069,-87.721559063,,,0.0,,,Cash,Dispatch Taxi Affiliation,1080,,0.0\r\n", "13,16.45,11,12,3,1446554700,41.983636307,-87.723583185,,,6.9,,,Cash,,780,,0.0\r\n", "16,32.05,12,1,1,1417916700,41.953582125,-87.72345239,,,15.4,,,Cash,,1200,,0.0\r\n", "30,38.45,10,10,5,1444301100,41.839086906,-87.714003807,,,14.6,,,Cash,,2580,,0.0\r\n", "11,14.65,1,1,3,1358213400,41.978829526,-87.771166703,,,5.81,,,Cash,,1080,,0.0\r\n" ] } ], "source": [ "!head {_data_filepath}" ] }, { "cell_type": "markdown", "metadata": { "id": "QioyhunCImwE" }, "source": [ "*Disclaimer: This site provides applications using data that has been modified for use from its original source, www.cityofchicago.org, the official website of the City of Chicago. The City of Chicago makes no claims as to the content, accuracy, timeliness, or completeness of any of the data provided at this site. The data provided at this site is subject to change at any time. It is understood that the data provided at this site is being used at one’s own risk.*" ] }, { "cell_type": "markdown", "metadata": { "id": "8ONIE_hdkPS4" }, "source": [ "### Create the InteractiveContext\n", "Last, we create an InteractiveContext, which will allow us to run TFX components interactively in this notebook." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:22.883608Z", "iopub.status.busy": "2024-08-02T09:25:22.882836Z", "iopub.status.idle": "2024-08-02T09:25:22.889214Z", "shell.execute_reply": "2024-08-02T09:25:22.888646Z" }, "id": "0Rh6K5sUf9dd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:InteractiveContext pipeline_root argument not provided: using temporary directory /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle as root for pipeline outputs.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/metadata.sqlite.\n" ] } ], "source": [ "# Here, we create an InteractiveContext using default parameters. This will\n", "# use a temporary directory with an ephemeral ML Metadata database instance.\n", "# To use your own pipeline root or database, the optional properties\n", "# `pipeline_root` and `metadata_connection_config` may be passed to\n", "# InteractiveContext. Calls to InteractiveContext are no-ops outside of the\n", "# notebook.\n", "context = InteractiveContext()" ] }, { "cell_type": "markdown", "metadata": { "id": "HdQWxfsVkzdJ" }, "source": [ "## Run TFX components interactively\n", "In the cells that follow, we create TFX components one-by-one, run each of them, and visualize their output artifacts." ] }, { "cell_type": "markdown", "metadata": { "id": "L9fwt9gQk3BR" }, "source": [ "### ExampleGen\n", "\n", "The `ExampleGen` component is usually at the start of a TFX pipeline. It will:\n", "\n", "1. Split data into training and evaluation sets (by default, 2/3 training + 1/3 eval)\n", "2. Convert data into the `tf.Example` format (learn more [here](https://www.tensorflow.org/tutorials/load_data/tfrecord))\n", "3. Copy data into the `_tfx_root` directory for other components to access\n", "\n", "`ExampleGen` takes as input the path to your data source. In our case, this is the `_data_root` path that contains the downloaded CSV.\n", "\n", "Note: In this notebook, we can instantiate components one-by-one and run them with `InteractiveContext.run()`. By contrast, in a production setting, we would specify all the components upfront in a `Pipeline` to pass to the orchestrator (see the [Building a TFX Pipeline Guide](https://www.tensorflow.org/tfx/guide/build_tfx_pipeline)).\n", "\n", "#### Enabling the Cache\n", "When using the `InteractiveContext` in a notebook to develop a pipeline you can control when individual components will cache their outputs. Set `enable_cache` to `True` when you want to reuse the previous output artifacts that the component generated. Set `enable_cache` to `False` when you want to recompute the output artifacts for a component, if you are making changes to the code for example." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:22.892367Z", "iopub.status.busy": "2024-08-02T09:25:22.891956Z", "iopub.status.idle": "2024-08-02T09:25:29.118487Z", "shell.execute_reply": "2024-08-02T09:25:29.117895Z" }, "id": "PyXjuMt8f-9u" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running driver for CsvExampleGen\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:select span and version = (0, None)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:latest span and version = (0, None)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running executor for CsvExampleGen\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Generating examples.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.\n" ] }, { "data": { "application/javascript": [ "\n", " if (typeof window.interactive_beam_jquery == 'undefined') {\n", " var jqueryScript = document.createElement('script');\n", " jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n", " jqueryScript.type = 'text/javascript';\n", " jqueryScript.onload = function() {\n", " var datatableScript = document.createElement('script');\n", " datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n", " datatableScript.type = 'text/javascript';\n", " datatableScript.onload = function() {\n", " window.interactive_beam_jquery = jQuery.noConflict(true);\n", " window.interactive_beam_jquery(document).ready(function($){\n", " \n", " });\n", " }\n", " document.head.appendChild(datatableScript);\n", " };\n", " document.head.appendChild(jqueryScript);\n", " } else {\n", " window.interactive_beam_jquery(document).ready(function($){\n", " \n", " });\n", " }" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Processing input csv data /tmpfs/tmp/tfx-datajp95savt/* to TFExample.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Examples generated.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running publisher for CsvExampleGen\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "data": { "text/html": [ "\n", "\n", "
ExecutionResult at 0x7fac1c3b6070
.execution_id1
.component\n", "\n", "
CsvExampleGen at 0x7fac1c3b6d60
.inputs{}
.outputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7faad4550820
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1) at 0x7fac1c3a6070
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1
.span0
.split_names["train", "eval"]
.version0
.exec_properties
['input_base']/tmpfs/tmp/tfx-datajp95savt
['input_config']{\n", " "splits": [\n", " {\n", " "name": "single_split",\n", " "pattern": "*"\n", " }\n", " ]\n", "}
['output_config']{\n", " "split_config": {\n", " "splits": [\n", " {\n", " "hash_buckets": 2,\n", " "name": "train"\n", " },\n", " {\n", " "hash_buckets": 1,\n", " "name": "eval"\n", " }\n", " ]\n", " }\n", "}
['output_data_format']6
['output_file_format']5
['custom_config']None
['range_config']None
['span']0
['version']None
['input_fingerprint']split:single_split,num_files:1,total_bytes:1922812,xor_checksum:1722590722,sum_checksum:1722590722
.component.inputs{}
.component.outputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7faad4550820
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1) at 0x7fac1c3a6070
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1
.span0
.split_names["train", "eval"]
.version0
" ], "text/plain": [ "ExecutionResult(\n", " component_id: CsvExampleGen\n", " execution_id: 1\n", " outputs:\n", " examples: OutputChannel(artifact_type=Examples, producer_component_id=CsvExampleGen, output_key=examples, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False))" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_gen = tfx.components.CsvExampleGen(input_base=_data_root)\n", "context.run(example_gen, enable_cache=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "OqCoZh7KPUm9" }, "source": [ "Let's examine the output artifacts of `ExampleGen`. This component produces two artifacts, training examples and evaluation examples:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:29.122237Z", "iopub.status.busy": "2024-08-02T09:25:29.121792Z", "iopub.status.idle": "2024-08-02T09:25:29.125762Z", "shell.execute_reply": "2024-08-02T09:25:29.125144Z" }, "id": "880KkTAkPeUg" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\"train\", \"eval\"] /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1\n" ] } ], "source": [ "artifact = example_gen.outputs['examples'].get()[0]\n", "print(artifact.split_names, artifact.uri)" ] }, { "cell_type": "markdown", "metadata": { "id": "J6vcbW_wPqvl" }, "source": [ "We can also take a look at the first three training examples:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:29.129046Z", "iopub.status.busy": "2024-08-02T09:25:29.128561Z", "iopub.status.idle": "2024-08-02T09:25:31.412480Z", "shell.execute_reply": "2024-08-02T09:25:31.411674Z" }, "id": "H4XIXjiCPwzQ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "features {\n", " feature {\n", " key: \"company\"\n", " value {\n", " bytes_list {\n", " value: \"Chicago Elite Cab Corp. (Chicago Carriag\"\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_census_tract\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_community_area\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_latitude\"\n", " value {\n", " float_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_longitude\"\n", " value {\n", " float_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"fare\"\n", " value {\n", " float_list {\n", " value: 12.449999809265137\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"payment_type\"\n", " value {\n", " bytes_list {\n", " value: \"Credit Card\"\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_census_tract\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_community_area\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_latitude\"\n", " value {\n", " float_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_longitude\"\n", " value {\n", " float_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"tips\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_miles\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_seconds\"\n", " value {\n", " int64_list {\n", " value: 0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_day\"\n", " value {\n", " int64_list {\n", " value: 6\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_hour\"\n", " value {\n", " int64_list {\n", " value: 19\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_month\"\n", " value {\n", " int64_list {\n", " value: 5\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_timestamp\"\n", " value {\n", " int64_list {\n", " value: 1400269500\n", " }\n", " }\n", " }\n", "}\n", "\n", "features {\n", " feature {\n", " key: \"company\"\n", " value {\n", " bytes_list {\n", " value: \"Taxi Affiliation Services\"\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_census_tract\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_community_area\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_latitude\"\n", " value {\n", " float_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_longitude\"\n", " value {\n", " float_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"fare\"\n", " value {\n", " float_list {\n", " value: 27.049999237060547\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"payment_type\"\n", " value {\n", " bytes_list {\n", " value: \"Cash\"\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_census_tract\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_community_area\"\n", " value {\n", " int64_list {\n", " value: 60\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_latitude\"\n", " value {\n", " float_list {\n", " value: 41.836151123046875\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_longitude\"\n", " value {\n", " float_list {\n", " value: -87.64878845214844\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"tips\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_miles\"\n", " value {\n", " float_list {\n", " value: 12.600000381469727\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_seconds\"\n", " value {\n", " int64_list {\n", " value: 1380\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_day\"\n", " value {\n", " int64_list {\n", " value: 3\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_hour\"\n", " value {\n", " int64_list {\n", " value: 2\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_month\"\n", " value {\n", " int64_list {\n", " value: 10\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_timestamp\"\n", " value {\n", " int64_list {\n", " value: 1380593700\n", " }\n", " }\n", " }\n", "}\n", "\n", "features {\n", " feature {\n", " key: \"company\"\n", " value {\n", " bytes_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_census_tract\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_community_area\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_latitude\"\n", " value {\n", " float_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_longitude\"\n", " value {\n", " float_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"fare\"\n", " value {\n", " float_list {\n", " value: 16.450000762939453\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"payment_type\"\n", " value {\n", " bytes_list {\n", " value: \"Cash\"\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_census_tract\"\n", " value {\n", " int64_list {\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_community_area\"\n", " value {\n", " int64_list {\n", " value: 13\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_latitude\"\n", " value {\n", " float_list {\n", " value: 41.98363494873047\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_longitude\"\n", " value {\n", " float_list {\n", " value: -87.72357940673828\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"tips\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_miles\"\n", " value {\n", " float_list {\n", " value: 6.900000095367432\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_seconds\"\n", " value {\n", " int64_list {\n", " value: 780\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_day\"\n", " value {\n", " int64_list {\n", " value: 3\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_hour\"\n", " value {\n", " int64_list {\n", " value: 12\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_month\"\n", " value {\n", " int64_list {\n", " value: 11\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_timestamp\"\n", " value {\n", " int64_list {\n", " value: 1446554700\n", " }\n", " }\n", " }\n", "}\n", "\n" ] } ], "source": [ "# Get the URI of the output artifact representing the training examples, which is a directory\n", "train_uri = os.path.join(example_gen.outputs['examples'].get()[0].uri, 'Split-train')\n", "\n", "# Get the list of files in this directory (all compressed TFRecord files)\n", "tfrecord_filenames = [os.path.join(train_uri, name)\n", " for name in os.listdir(train_uri)]\n", "\n", "# Create a `TFRecordDataset` to read these files\n", "dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type=\"GZIP\")\n", "\n", "# Iterate over the first 3 records and decode them.\n", "for tfrecord in dataset.take(3):\n", " serialized_example = tfrecord.numpy()\n", " example = tf.train.Example()\n", " example.ParseFromString(serialized_example)\n", " pp.pprint(example)" ] }, { "cell_type": "markdown", "metadata": { "id": "2gluYjccf-IP" }, "source": [ "Now that `ExampleGen` has finished ingesting the data, the next step is data analysis." ] }, { "cell_type": "markdown", "metadata": { "id": "csM6BFhtk5Aa" }, "source": [ "### StatisticsGen\n", "The `StatisticsGen` component computes statistics over your dataset for data analysis, as well as for use in downstream components. It uses the [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started) library.\n", "\n", "`StatisticsGen` takes as input the dataset we just ingested using `ExampleGen`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:31.416340Z", "iopub.status.busy": "2024-08-02T09:25:31.415663Z", "iopub.status.idle": "2024-08-02T09:25:35.104024Z", "shell.execute_reply": "2024-08-02T09:25:35.103345Z" }, "id": "MAscCCYWgA-9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Excluding no splits because exclude_splits is not set.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running driver for StatisticsGen\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running executor for StatisticsGen\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Generating statistics for split train.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Statistics for split train written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2/Split-train.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Generating statistics for split eval.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Statistics for split eval written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2/Split-eval.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running publisher for StatisticsGen\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "data": { "text/html": [ "\n", "\n", "
ExecutionResult at 0x7fac1c3a6400
.execution_id2
.component\n", "\n", "
StatisticsGen at 0x7fac0050f820
.inputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7faad4550820
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1) at 0x7fac1c3a6070
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1
.span0
.split_names["train", "eval"]
.version0
.outputs
['statistics']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fac0050f520
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2) at 0x7fac00504700
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2
.span0
.split_names["train", "eval"]
.exec_properties
['stats_options_json']None
['exclude_splits'][]
.component.inputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7faad4550820
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1) at 0x7fac1c3a6070
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1
.span0
.split_names["train", "eval"]
.version0
.component.outputs
['statistics']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fac0050f520
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2) at 0x7fac00504700
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2
.span0
.split_names["train", "eval"]
" ], "text/plain": [ "ExecutionResult(\n", " component_id: StatisticsGen\n", " execution_id: 2\n", " outputs:\n", " statistics: OutputChannel(artifact_type=ExampleStatistics, producer_component_id=StatisticsGen, output_key=statistics, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "statistics_gen = tfx.components.StatisticsGen(\n", " examples=example_gen.outputs['examples'])\n", "context.run(statistics_gen, enable_cache=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "HLI6cb_5WugZ" }, "source": [ "After `StatisticsGen` finishes running, we can visualize the outputted statistics. Try playing with the different plots!" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.107301Z", "iopub.status.busy": "2024-08-02T09:25:35.107028Z", "iopub.status.idle": "2024-08-02T09:25:35.121307Z", "shell.execute_reply": "2024-08-02T09:25:35.120712Z" }, "id": "tLjXy7K6Tp_G" }, "outputs": [ { "data": { "text/html": [ "Artifact at /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
'train' split:

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
'eval' split:

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "context.show(statistics_gen.outputs['statistics'])" ] }, { "cell_type": "markdown", "metadata": { "id": "HLKLTO9Nk60p" }, "source": [ "### SchemaGen\n", "\n", "The `SchemaGen` component generates a schema based on your data statistics. (A schema defines the expected bounds, types, and properties of the features in your dataset.) It also uses the [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started) library.\n", "\n", "Note: The generated schema is best-effort and only tries to infer basic properties of the data. It is expected that you review and modify it as needed.\n", "\n", "`SchemaGen` will take as input the statistics that we generated with `StatisticsGen`, looking at the training split by default." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.124313Z", "iopub.status.busy": "2024-08-02T09:25:35.124077Z", "iopub.status.idle": "2024-08-02T09:25:35.169279Z", "shell.execute_reply": "2024-08-02T09:25:35.168682Z" }, "id": "ygQvZ6hsiQ_J" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Excluding no splits because exclude_splits is not set.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running driver for SchemaGen\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running executor for SchemaGen\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Processing schema from statistics for split train.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Processing schema from statistics for split eval.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Schema written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3/schema.pbtxt.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running publisher for SchemaGen\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "data": { "text/html": [ "\n", "\n", "
ExecutionResult at 0x7fac00541490
.execution_id3
.component\n", "\n", "
SchemaGen at 0x7fabe917a550
.inputs
['statistics']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fac0050f520
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2) at 0x7fac00504700
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2
.span0
.split_names["train", "eval"]
.outputs
['schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fac1cbe04c0
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3) at 0x7fac0081e0a0
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3
.exec_properties
['infer_feature_shape']0
['exclude_splits'][]
.component.inputs
['statistics']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fac0050f520
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2) at 0x7fac00504700
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2
.span0
.split_names["train", "eval"]
.component.outputs
['schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fac1cbe04c0
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3) at 0x7fac0081e0a0
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3
" ], "text/plain": [ "ExecutionResult(\n", " component_id: SchemaGen\n", " execution_id: 3\n", " outputs:\n", " schema: OutputChannel(artifact_type=Schema, producer_component_id=SchemaGen, output_key=schema, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False))" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "schema_gen = tfx.components.SchemaGen(\n", " statistics=statistics_gen.outputs['statistics'],\n", " infer_feature_shape=False)\n", "context.run(schema_gen, enable_cache=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "zi6TxTUKXM6b" }, "source": [ "After `SchemaGen` finishes running, we can visualize the generated schema as a table." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.172445Z", "iopub.status.busy": "2024-08-02T09:25:35.171849Z", "iopub.status.idle": "2024-08-02T09:25:35.192249Z", "shell.execute_reply": "2024-08-02T09:25:35.191663Z" }, "id": "Ec9vqDXpXeMb" }, "outputs": [ { "data": { "text/html": [ "Artifact at /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TypePresenceValencyDomain
Feature name
'company'STRINGrequired'company'
'dropoff_census_tract'INTrequired-
'dropoff_community_area'INTrequired-
'dropoff_latitude'FLOATrequired-
'dropoff_longitude'FLOATrequired-
'fare'FLOATrequiredsingle-
'payment_type'STRINGrequiredsingle'payment_type'
'pickup_census_tract'INTrequired-
'pickup_community_area'INTrequired-
'pickup_latitude'FLOATrequired-
'pickup_longitude'FLOATrequired-
'tips'FLOATrequiredsingle-
'trip_miles'FLOATrequiredsingle-
'trip_seconds'INTrequired-
'trip_start_day'INTrequiredsingle-
'trip_start_hour'INTrequiredsingle-
'trip_start_month'INTrequiredsingle-
'trip_start_timestamp'INTrequiredsingle-
\n", "
" ], "text/plain": [ " Type Presence Valency Domain\n", "Feature name \n", "'company' STRING required 'company'\n", "'dropoff_census_tract' INT required -\n", "'dropoff_community_area' INT required -\n", "'dropoff_latitude' FLOAT required -\n", "'dropoff_longitude' FLOAT required -\n", "'fare' FLOAT required single -\n", "'payment_type' STRING required single 'payment_type'\n", "'pickup_census_tract' INT required -\n", "'pickup_community_area' INT required -\n", "'pickup_latitude' FLOAT required -\n", "'pickup_longitude' FLOAT required -\n", "'tips' FLOAT required single -\n", "'trip_miles' FLOAT required single -\n", "'trip_seconds' INT required -\n", "'trip_start_day' INT required single -\n", "'trip_start_hour' INT required single -\n", "'trip_start_month' INT required single -\n", "'trip_start_timestamp' INT required single -" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Values
Domain
'company''0118 - 42111 Godfrey S.Awir', '1085 - 72312 N and W Cab Co', '2192 - 73487 Zeymane Corp', '2733 - 74600 Benny Jona', '3011 - 66308 JBL Cab Inc.', '3152 - 97284 Crystal Abernathy', '3201 - C&D Cab Co Inc', '3201 - CID Cab Co Inc', '3253 - 91138 Gaither Cab Co.', '3319 - CD Cab Co', '3385 - 23210 Eman Cab', '3385 - Eman Cab', '3623 - 72222 Arrington Enterprises', '3897 - 57856 Ilie Malec', '4053 - 40193 Adwar H. Nikola', '4197 - 41842 Royal Star', '4197 - Royal Star', '4615 - 83503 Tyrone Henderson', '4615 - Tyrone Henderson', '4623 - Jay Kim', '5006 - 39261 Salifu Bawa', '5074 - 54002 Ahzmi Inc', '5074 - Ahzmi Inc', '5129 - 87128', '5129 - 98755 Mengisti Taxi', '585 - 88805 Valley Cab Co', '5864 - Thomas Owusu', '5874 - 73628 Sergey Cab Corp.', '5874 - Sergey Cab Corp.', '5997 - 65283 AW Services Inc.', '6488 - 83287 Zuha Taxi', '6574 - Babylon Express Inc.', '6742 - 83735 Tasha ride inc', 'Blue Ribbon Taxi Association Inc.', 'C & D Cab Co Inc', 'Chicago Elite Cab Corp.', 'Chicago Elite Cab Corp. (Chicago Carriag', 'Chicago Medallion Leasing INC', 'Chicago Medallion Management', 'Choice Taxi Association', 'Dispatch Taxi Affiliation', 'KOAM Taxi Association', 'Northwest Management LLC', 'Taxi Affiliation Services', 'Top Cab Affiliation', '0694 - 59280 Chinesco Trans Inc', '2092 - 61288 Sbeih company', '2192 - Zeymane Corp', '2809 - 95474 C & D Cab Co Inc.', '2823 - 73307 Seung Lee', '3094 - 24059 G.L.B. Cab Co', '3897 - Ilie Malec', '4053 - Adwar H. Nikola', '5006 - Salifu Bawa', '5129 - Mengisti Taxi', '5724 - KYVI Cab Inc', '585 - Valley Cab Co', '5864 - 73614 Thomas Owusu', '5997 - AW Services Inc.', '6057 - 24657 Richard Addo', '6743 - Luhak Corp'
'payment_type''Cash', 'Credit Card', 'Dispute', 'No Charge', 'Pcard', 'Unknown', 'Prcard'
\n", "
" ], "text/plain": [ " Values\n", "Domain \n", "'company' '0118 - 42111 Godfrey S.Awir', '1085 - 72312 N and W Cab Co', '2192 - 73487 Zeymane Corp', '2733 - 74600 Benny Jona', '3011 - 66308 JBL Cab Inc.', '3152 - 97284 Crystal Abernathy', '3201 - C&D Cab Co Inc', '3201 - CID Cab Co Inc', '3253 - 91138 Gaither Cab Co.', '3319 - CD Cab Co', '3385 - 23210 Eman Cab', '3385 - Eman Cab', '3623 - 72222 Arrington Enterprises', '3897 - 57856 Ilie Malec', '4053 - 40193 Adwar H. Nikola', '4197 - 41842 Royal Star', '4197 - Royal Star', '4615 - 83503 Tyrone Henderson', '4615 - Tyrone Henderson', '4623 - Jay Kim', '5006 - 39261 Salifu Bawa', '5074 - 54002 Ahzmi Inc', '5074 - Ahzmi Inc', '5129 - 87128', '5129 - 98755 Mengisti Taxi', '585 - 88805 Valley Cab Co', '5864 - Thomas Owusu', '5874 - 73628 Sergey Cab Corp.', '5874 - Sergey Cab Corp.', '5997 - 65283 AW Services Inc.', '6488 - 83287 Zuha Taxi', '6574 - Babylon Express Inc.', '6742 - 83735 Tasha ride inc', 'Blue Ribbon Taxi Association Inc.', 'C & D Cab Co Inc', 'Chicago Elite Cab Corp.', 'Chicago Elite Cab Corp. (Chicago Carriag', 'Chicago Medallion Leasing INC', 'Chicago Medallion Management', 'Choice Taxi Association', 'Dispatch Taxi Affiliation', 'KOAM Taxi Association', 'Northwest Management LLC', 'Taxi Affiliation Services', 'Top Cab Affiliation', '0694 - 59280 Chinesco Trans Inc', '2092 - 61288 Sbeih company', '2192 - Zeymane Corp', '2809 - 95474 C & D Cab Co Inc.', '2823 - 73307 Seung Lee', '3094 - 24059 G.L.B. Cab Co', '3897 - Ilie Malec', '4053 - Adwar H. Nikola', '5006 - Salifu Bawa', '5129 - Mengisti Taxi', '5724 - KYVI Cab Inc', '585 - Valley Cab Co', '5864 - 73614 Thomas Owusu', '5997 - AW Services Inc.', '6057 - 24657 Richard Addo', '6743 - Luhak Corp'\n", "'payment_type' 'Cash', 'Credit Card', 'Dispute', 'No Charge', 'Pcard', 'Unknown', 'Prcard'" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "context.show(schema_gen.outputs['schema'])" ] }, { "cell_type": "markdown", "metadata": { "id": "kZWWdbA-m7zp" }, "source": [ "Each feature in your dataset shows up as a row in the schema table, alongside its properties. The schema also captures all the values that a categorical feature takes on, denoted as its domain.\n", "\n", "To learn more about schemas, see [the SchemaGen documentation](https://www.tensorflow.org/tfx/guide/schemagen)." ] }, { "cell_type": "markdown", "metadata": { "id": "V1qcUuO9k9f8" }, "source": [ "### ExampleValidator\n", "The `ExampleValidator` component detects anomalies in your data, based on the expectations defined by the schema. It also uses the [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started) library.\n", "\n", "`ExampleValidator` will take as input the statistics from `StatisticsGen`, and the schema from `SchemaGen`." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.195470Z", "iopub.status.busy": "2024-08-02T09:25:35.195067Z", "iopub.status.idle": "2024-08-02T09:25:35.246198Z", "shell.execute_reply": "2024-08-02T09:25:35.245554Z" }, "id": "XRlRUuGgiXks" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Excluding no splits because exclude_splits is not set.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running driver for ExampleValidator\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running executor for ExampleValidator\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Validating schema against the computed statistics for split train.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Anomalies alerts created for split train.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Validation complete for split train. Anomalies written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/ExampleValidator/anomalies/4/Split-train.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Validating schema against the computed statistics for split eval.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Anomalies alerts created for split eval.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Validation complete for split eval. Anomalies written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/ExampleValidator/anomalies/4/Split-eval.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running publisher for ExampleValidator\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "data": { "text/html": [ "\n", "\n", "
ExecutionResult at 0x7fac00547100
.execution_id4
.component\n", "\n", "
ExampleValidator at 0x7fabe90f7af0
.inputs
['statistics']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fac0050f520
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2) at 0x7fac00504700
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2
.span0
.split_names["train", "eval"]
['schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fac1cbe04c0
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3) at 0x7fac0081e0a0
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3
.outputs
['anomalies']\n", "\n", "
Channel of type 'ExampleAnomalies' (1 artifact) at 0x7fabe90f7700
.type_nameExampleAnomalies
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleAnomalies' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/ExampleValidator/anomalies/4) at 0x7fac00504e80
.type<class 'tfx.types.standard_artifacts.ExampleAnomalies'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/ExampleValidator/anomalies/4
.span0
.split_names["train", "eval"]
.exec_properties
['exclude_splits'][]
['custom_validation_config']None
.component.inputs
['statistics']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fac0050f520
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2) at 0x7fac00504700
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/StatisticsGen/statistics/2
.span0
.split_names["train", "eval"]
['schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fac1cbe04c0
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3) at 0x7fac0081e0a0
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3
.component.outputs
['anomalies']\n", "\n", "
Channel of type 'ExampleAnomalies' (1 artifact) at 0x7fabe90f7700
.type_nameExampleAnomalies
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleAnomalies' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/ExampleValidator/anomalies/4) at 0x7fac00504e80
.type<class 'tfx.types.standard_artifacts.ExampleAnomalies'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/ExampleValidator/anomalies/4
.span0
.split_names["train", "eval"]
" ], "text/plain": [ "ExecutionResult(\n", " component_id: ExampleValidator\n", " execution_id: 4\n", " outputs:\n", " anomalies: OutputChannel(artifact_type=ExampleAnomalies, producer_component_id=ExampleValidator, output_key=anomalies, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False))" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_validator = tfx.components.ExampleValidator(\n", " statistics=statistics_gen.outputs['statistics'],\n", " schema=schema_gen.outputs['schema'])\n", "context.run(example_validator, enable_cache=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "855mrHgJcoer" }, "source": [ "After `ExampleValidator` finishes running, we can visualize the anomalies as a table." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.249686Z", "iopub.status.busy": "2024-08-02T09:25:35.249433Z", "iopub.status.idle": "2024-08-02T09:25:35.263556Z", "shell.execute_reply": "2024-08-02T09:25:35.262945Z" }, "id": "TDyAAozQcrk3" }, "outputs": [ { "data": { "text/html": [ "Artifact at /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/ExampleValidator/anomalies/4

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
'train' split:

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

No anomalies found.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
'eval' split:

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

No anomalies found.

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "context.show(example_validator.outputs['anomalies'])" ] }, { "cell_type": "markdown", "metadata": { "id": "znMoJj60ybZx" }, "source": [ "In the anomalies table, we can see that there are no anomalies. This is what we'd expect, since this the first dataset that we've analyzed and the schema is tailored to it. You should review this schema -- anything unexpected means an anomaly in the data. Once reviewed, the schema can be used to guard future data, and anomalies produced here can be used to debug model performance, understand how your data evolves over time, and identify data errors." ] }, { "cell_type": "markdown", "metadata": { "id": "JPViEz5RlA36" }, "source": [ "### Transform\n", "The `Transform` component performs feature engineering for both training and serving. It uses the [TensorFlow Transform](https://www.tensorflow.org/tfx/transform/get_started) library.\n", "\n", "`Transform` will take as input the data from `ExampleGen`, the schema from `SchemaGen`, as well as a module that contains user-defined Transform code.\n", "\n", "Let's see an example of user-defined Transform code below (for an introduction to the TensorFlow Transform APIs, [see the tutorial](https://www.tensorflow.org/tfx/tutorials/transform/simple)). First, we define a few constants for feature engineering:\n", "\n", "Note: The `%%writefile` cell magic will save the contents of the cell as a `.py` file on disk. This allows the `Transform` component to load your code as a module.\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.266948Z", "iopub.status.busy": "2024-08-02T09:25:35.266393Z", "iopub.status.idle": "2024-08-02T09:25:35.269608Z", "shell.execute_reply": "2024-08-02T09:25:35.268968Z" }, "id": "PuNSiUKb4YJf" }, "outputs": [], "source": [ "_taxi_constants_module_file = 'taxi_constants.py'" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.272668Z", "iopub.status.busy": "2024-08-02T09:25:35.272107Z", "iopub.status.idle": "2024-08-02T09:25:35.276948Z", "shell.execute_reply": "2024-08-02T09:25:35.276282Z" }, "id": "HPjhXuIF4YJh" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing taxi_constants.py\n" ] } ], "source": [ "%%writefile {_taxi_constants_module_file}\n", "\n", "NUMERICAL_FEATURES = ['trip_miles', 'fare', 'trip_seconds']\n", "\n", "BUCKET_FEATURES = [\n", " 'pickup_latitude', 'pickup_longitude', 'dropoff_latitude',\n", " 'dropoff_longitude'\n", "]\n", "# Number of buckets used by tf.transform for encoding each feature.\n", "FEATURE_BUCKET_COUNT = 10\n", "\n", "CATEGORICAL_NUMERICAL_FEATURES = [\n", " 'trip_start_hour', 'trip_start_day', 'trip_start_month',\n", " 'pickup_census_tract', 'dropoff_census_tract', 'pickup_community_area',\n", " 'dropoff_community_area'\n", "]\n", "\n", "CATEGORICAL_STRING_FEATURES = [\n", " 'payment_type',\n", " 'company',\n", "]\n", "\n", "# Number of vocabulary terms used for encoding categorical features.\n", "VOCAB_SIZE = 1000\n", "\n", "# Count of out-of-vocab buckets in which unrecognized categorical are hashed.\n", "OOV_SIZE = 10\n", "\n", "# Keys\n", "LABEL_KEY = 'tips'\n", "FARE_KEY = 'fare'\n", "\n", "def t_name(key):\n", " \"\"\"\n", " Rename the feature keys so that they don't clash with the raw keys when\n", " running the Evaluator component.\n", " Args:\n", " key: The original feature key\n", " Returns:\n", " key with '_xf' appended\n", " \"\"\"\n", " return key + '_xf'" ] }, { "cell_type": "markdown", "metadata": { "id": "Duj2Ax5z4YJl" }, "source": [ "Next, we write a `preprocessing_fn` that takes in raw data as input, and returns transformed features that our model can train on:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.279898Z", "iopub.status.busy": "2024-08-02T09:25:35.279666Z", "iopub.status.idle": "2024-08-02T09:25:35.282489Z", "shell.execute_reply": "2024-08-02T09:25:35.281942Z" }, "id": "4AJ9hBs94YJm" }, "outputs": [], "source": [ "_taxi_transform_module_file = 'taxi_transform.py'" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.285607Z", "iopub.status.busy": "2024-08-02T09:25:35.285328Z", "iopub.status.idle": "2024-08-02T09:25:35.290877Z", "shell.execute_reply": "2024-08-02T09:25:35.290271Z" }, "id": "MYmxxx9A4YJn" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing taxi_transform.py\n" ] } ], "source": [ "%%writefile {_taxi_transform_module_file}\n", "\n", "import tensorflow as tf\n", "import tensorflow_transform as tft\n", "\n", "# Imported files such as taxi_constants are normally cached, so changes are\n", "# not honored after the first import. Normally this is good for efficiency, but\n", "# during development when we may be iterating code it can be a problem. To\n", "# avoid this problem during development, reload the file.\n", "import taxi_constants\n", "import sys\n", "if 'google.colab' in sys.modules: # Testing to see if we're doing development\n", " import importlib\n", " importlib.reload(taxi_constants)\n", "\n", "_NUMERICAL_FEATURES = taxi_constants.NUMERICAL_FEATURES\n", "_BUCKET_FEATURES = taxi_constants.BUCKET_FEATURES\n", "_FEATURE_BUCKET_COUNT = taxi_constants.FEATURE_BUCKET_COUNT\n", "_CATEGORICAL_NUMERICAL_FEATURES = taxi_constants.CATEGORICAL_NUMERICAL_FEATURES\n", "_CATEGORICAL_STRING_FEATURES = taxi_constants.CATEGORICAL_STRING_FEATURES\n", "_VOCAB_SIZE = taxi_constants.VOCAB_SIZE\n", "_OOV_SIZE = taxi_constants.OOV_SIZE\n", "_FARE_KEY = taxi_constants.FARE_KEY\n", "_LABEL_KEY = taxi_constants.LABEL_KEY\n", "\n", "\n", "def _make_one_hot(x, key):\n", " \"\"\"Make a one-hot tensor to encode categorical features.\n", " Args:\n", " X: A dense tensor\n", " key: A string key for the feature in the input\n", " Returns:\n", " A dense one-hot tensor as a float list\n", " \"\"\"\n", " integerized = tft.compute_and_apply_vocabulary(x,\n", " top_k=_VOCAB_SIZE,\n", " num_oov_buckets=_OOV_SIZE,\n", " vocab_filename=key, name=key)\n", " depth = (\n", " tft.experimental.get_vocabulary_size_by_name(key) + _OOV_SIZE)\n", " one_hot_encoded = tf.one_hot(\n", " integerized,\n", " depth=tf.cast(depth, tf.int32),\n", " on_value=1.0,\n", " off_value=0.0)\n", " return tf.reshape(one_hot_encoded, [-1, depth])\n", "\n", "\n", "def _fill_in_missing(x):\n", " \"\"\"Replace missing values in a SparseTensor.\n", " Fills in missing values of `x` with '' or 0, and converts to a dense tensor.\n", " Args:\n", " x: A `SparseTensor` of rank 2. Its dense shape should have size at most 1\n", " in the second dimension.\n", " Returns:\n", " A rank 1 tensor where missing values of `x` have been filled in.\n", " \"\"\"\n", " if not isinstance(x, tf.sparse.SparseTensor):\n", " return x\n", "\n", " default_value = '' if x.dtype == tf.string else 0\n", " return tf.squeeze(\n", " tf.sparse.to_dense(\n", " tf.SparseTensor(x.indices, x.values, [x.dense_shape[0], 1]),\n", " default_value),\n", " axis=1)\n", "\n", "\n", "def preprocessing_fn(inputs):\n", " \"\"\"tf.transform's callback function for preprocessing inputs.\n", " Args:\n", " inputs: map from feature keys to raw not-yet-transformed features.\n", " Returns:\n", " Map from string feature key to transformed feature operations.\n", " \"\"\"\n", " outputs = {}\n", " for key in _NUMERICAL_FEATURES:\n", " # If sparse make it dense, setting nan's to 0 or '', and apply zscore.\n", " outputs[taxi_constants.t_name(key)] = tft.scale_to_z_score(\n", " _fill_in_missing(inputs[key]), name=key)\n", "\n", " for key in _BUCKET_FEATURES:\n", " outputs[taxi_constants.t_name(key)] = tf.cast(tft.bucketize(\n", " _fill_in_missing(inputs[key]), _FEATURE_BUCKET_COUNT, name=key),\n", " dtype=tf.float32)\n", "\n", " for key in _CATEGORICAL_STRING_FEATURES:\n", " outputs[taxi_constants.t_name(key)] = _make_one_hot(_fill_in_missing(inputs[key]), key)\n", "\n", " for key in _CATEGORICAL_NUMERICAL_FEATURES:\n", " outputs[taxi_constants.t_name(key)] = _make_one_hot(tf.strings.strip(\n", " tf.strings.as_string(_fill_in_missing(inputs[key]))), key)\n", "\n", " # Was this passenger a big tipper?\n", " taxi_fare = _fill_in_missing(inputs[_FARE_KEY])\n", " tips = _fill_in_missing(inputs[_LABEL_KEY])\n", " outputs[_LABEL_KEY] = tf.where(\n", " tf.math.is_nan(taxi_fare),\n", " tf.cast(tf.zeros_like(taxi_fare), tf.int64),\n", " # Test if the tip was > 20% of the fare.\n", " tf.cast(\n", " tf.greater(tips, tf.multiply(taxi_fare, tf.constant(0.2))), tf.int64))\n", "\n", " return outputs" ] }, { "cell_type": "markdown", "metadata": { "id": "wgbmZr3sgbWW" }, "source": [ "Now, we pass in this feature engineering code to the `Transform` component and run it to transform your data." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:25:35.293823Z", "iopub.status.busy": "2024-08-02T09:25:35.293595Z", "iopub.status.idle": "2024-08-02T09:26:18.815667Z", "shell.execute_reply": "2024-08-02T09:26:18.814857Z" }, "id": "jHfhth_GiZI9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_transform.py' (including modules: ['taxi_transform', 'taxi_constants']).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:User module package has hash fingerprint version d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmpfs/tmp/tmpu8kpouyg/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmpfs/tmp/tmp6787jn2x', '--dist-dir', '/tmpfs/tmp/tmpj98iwvji']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n", "!!\n", "\n", " ********************************************************************************\n", " Please avoid running ``setup.py`` directly.\n", " Instead, use pypa/build, pypa/installer or other\n", " standards-based tools.\n", "\n", " See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n", " ********************************************************************************\n", "\n", "!!\n", " self.initialize_options()\n", "INFO:absl:Successfully built user code wheel distribution at '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl'; target user module is 'taxi_transform'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Full user module path is 'taxi_transform@/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running driver for Transform\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "running bdist_wheel\n", "running build\n", "running build_py\n", "creating build\n", "creating build/lib\n", "copying taxi_transform.py -> build/lib\n", "copying taxi_constants.py -> build/lib\n", "installing to /tmpfs/tmp/tmp6787jn2x\n", "running install\n", "running install_lib\n", "copying build/lib/taxi_transform.py -> /tmpfs/tmp/tmp6787jn2x\n", "copying build/lib/taxi_constants.py -> /tmpfs/tmp/tmp6787jn2x\n", "running install_egg_info\n", "running egg_info\n", "creating tfx_user_code_Transform.egg-info\n", "writing tfx_user_code_Transform.egg-info/PKG-INFO\n", "writing dependency_links to tfx_user_code_Transform.egg-info/dependency_links.txt\n", "writing top-level names to tfx_user_code_Transform.egg-info/top_level.txt\n", "writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'\n", "reading manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'\n", "writing manifest file 'tfx_user_code_Transform.egg-info/SOURCES.txt'\n", "Copying tfx_user_code_Transform.egg-info to /tmpfs/tmp/tmp6787jn2x/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3.9.egg-info\n", "running install_scripts\n", "creating /tmpfs/tmp/tmp6787jn2x/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d.dist-info/WHEEL\n", "creating '/tmpfs/tmp/tmpj98iwvji/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl' and adding '/tmpfs/tmp/tmp6787jn2x' to it\n", "adding 'taxi_constants.py'\n", "adding 'taxi_transform.py'\n", "adding 'tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d.dist-info/METADATA'\n", "adding 'tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d.dist-info/WHEEL'\n", "adding 'tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d.dist-info/top_level.txt'\n", "adding 'tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d.dist-info/RECORD'\n", "removing /tmpfs/tmp/tmp6787jn2x\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running executor for Transform\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Analyze the 'train' split and transform all splits when splits_config is not set.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl', 'preprocessing_fn': None} 'preprocessing_fn'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Installing '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl' to a temporary directory.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmp_7nbz18y', '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processing /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Successfully installed '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:udf_utils.get_fn {'module_file': None, 'module_path': 'taxi_transform@/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl', 'stats_options_updater_fn': None} 'stats_options_updater_fn'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Installing '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl' to a temporary directory.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmp8mngt494', '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Installing collected packages: tfx-user-code-Transform\n", "Successfully installed tfx-user-code-Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processing /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Successfully installed '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Installing '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl' to a temporary directory.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmpe376fviy', '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Installing collected packages: tfx-user-code-Transform\n", "Successfully installed tfx-user-code-Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processing /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Successfully installed '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Installing collected packages: tfx-user-code-Transform\n", "Successfully installed tfx-user-code-Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: payment_type/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: company/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: trip_start_hour/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: trip_start_day/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: trip_start_month/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: pickup_census_tract/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: dropoff_census_tract/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: pickup_community_area/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: dropoff_community_area/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: payment_type/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: company/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: trip_start_hour/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: trip_start_day/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: trip_start_month/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: pickup_census_tract/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: dropoff_census_tract/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: pickup_community_area/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Tables initialized inside a tf.function will be re-initialized on every invocation of the function. This re-initialization can have significant impact on performance. Consider lifting them out of the graph context using `tf.init_scope`.: dropoff_community_area/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5/.temp_path/tftransform_tmp/c804114a115a454e9dc24ba23291fe73/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Writing fingerprint to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5/.temp_path/tftransform_tmp/c804114a115a454e9dc24ba23291fe73/fingerprint.pb\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:struct2tensor is not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:tensorflow_decision_forests is not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:tensorflow_text is not available.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5/.temp_path/tftransform_tmp/089f1b2bd7b8429fb4979023e5e36d13/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Writing fingerprint to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5/.temp_path/tftransform_tmp/089f1b2bd7b8429fb4979023e5e36d13/fingerprint.pb\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:If the number of unique tokens is smaller than the provided top_k or approximation error is acceptable, consider using tft.experimental.approximate_vocabulary for a potentially more efficient implementation.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company_xf has a shape dim {\n", " size: 55\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract_xf has a shape dim {\n", " size: 216\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area_xf has a shape dim {\n", " size: 79\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type_xf has a shape dim {\n", " size: 16\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract_xf has a shape dim {\n", " size: 11\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area_xf has a shape dim {\n", " size: 66\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day_xf has a shape dim {\n", " size: 17\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour_xf has a shape dim {\n", " size: 34\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month_xf has a shape dim {\n", " size: 22\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company_xf has a shape dim {\n", " size: 55\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract_xf has a shape dim {\n", " size: 216\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area_xf has a shape dim {\n", " size: 79\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type_xf has a shape dim {\n", " size: 16\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract_xf has a shape dim {\n", " size: 11\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area_xf has a shape dim {\n", " size: 66\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day_xf has a shape dim {\n", " size: 17\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour_xf has a shape dim {\n", " size: 34\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month_xf has a shape dim {\n", " size: 22\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:struct2tensor is not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:tensorflow_decision_forests is not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:tensorflow_text is not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:struct2tensor is not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:tensorflow_decision_forests is not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:tensorflow_text is not available.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running publisher for Transform\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "data": { "text/html": [ "\n", "\n", "
ExecutionResult at 0x7faae5e9e130
.execution_id5
.component\n", "\n", "
Transform at 0x7fabe917a400
.inputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7faad4550820
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1) at 0x7fac1c3a6070
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1
.span0
.split_names["train", "eval"]
.version0
['schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fac1cbe04c0
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3) at 0x7fac0081e0a0
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3
.outputs
['transform_graph']\n", "\n", "
Channel of type 'TransformGraph' (1 artifact) at 0x7fabe90f7940
.type_nameTransformGraph
._artifacts
[0]\n", "\n", "
Artifact of type 'TransformGraph' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5) at 0x7fabe90f79d0
.type<class 'tfx.types.standard_artifacts.TransformGraph'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5
['transformed_examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7fabe90f7d90
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transformed_examples/5) at 0x7fac00504ac0
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transformed_examples/5
.span0
.split_names["eval", "train"]
.version0
['updated_analyzer_cache']\n", "\n", "
Channel of type 'TransformCache' (1 artifact) at 0x7fabe90f7dc0
.type_nameTransformCache
._artifacts
[0]\n", "\n", "
Artifact of type 'TransformCache' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/updated_analyzer_cache/5) at 0x7fac00504fa0
.type<class 'tfx.types.standard_artifacts.TransformCache'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/updated_analyzer_cache/5
['pre_transform_schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fabe90f7490
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/pre_transform_schema/5) at 0x7fac00517580
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/pre_transform_schema/5
['pre_transform_stats']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fabe90f7100
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/pre_transform_stats/5) at 0x7fabe90f7430
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/pre_transform_stats/5
.span0
.split_names
['post_transform_schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fabe916c130
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_schema/5) at 0x7fabe90f7760
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_schema/5
['post_transform_stats']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fabe916c2e0
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_stats/5) at 0x7fac0050f580
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_stats/5
.span0
.split_names
['post_transform_anomalies']\n", "\n", "
Channel of type 'ExampleAnomalies' (1 artifact) at 0x7fabe916c160
.type_nameExampleAnomalies
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleAnomalies' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_anomalies/5) at 0x7fabe916cac0
.type<class 'tfx.types.standard_artifacts.ExampleAnomalies'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_anomalies/5
.span0
.split_names
.exec_properties
['module_file']None
['preprocessing_fn']None
['stats_options_updater_fn']None
['force_tf_compat_v1']0
['custom_config']null
['splits_config']None
['disable_statistics']0
['module_path']taxi_transform@/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Transform-0.0+d7f32accc04453d93cd29bae5b4d879eb83d8a54c7e01d354a58158f2f84251d-py3-none-any.whl
.component.inputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7faad4550820
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1) at 0x7fac1c3a6070
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1
.span0
.split_names["train", "eval"]
.version0
['schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fac1cbe04c0
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3) at 0x7fac0081e0a0
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3
.component.outputs
['transform_graph']\n", "\n", "
Channel of type 'TransformGraph' (1 artifact) at 0x7fabe90f7940
.type_nameTransformGraph
._artifacts
[0]\n", "\n", "
Artifact of type 'TransformGraph' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5) at 0x7fabe90f79d0
.type<class 'tfx.types.standard_artifacts.TransformGraph'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5
['transformed_examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7fabe90f7d90
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transformed_examples/5) at 0x7fac00504ac0
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transformed_examples/5
.span0
.split_names["eval", "train"]
.version0
['updated_analyzer_cache']\n", "\n", "
Channel of type 'TransformCache' (1 artifact) at 0x7fabe90f7dc0
.type_nameTransformCache
._artifacts
[0]\n", "\n", "
Artifact of type 'TransformCache' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/updated_analyzer_cache/5) at 0x7fac00504fa0
.type<class 'tfx.types.standard_artifacts.TransformCache'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/updated_analyzer_cache/5
['pre_transform_schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fabe90f7490
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/pre_transform_schema/5) at 0x7fac00517580
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/pre_transform_schema/5
['pre_transform_stats']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fabe90f7100
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/pre_transform_stats/5) at 0x7fabe90f7430
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/pre_transform_stats/5
.span0
.split_names
['post_transform_schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fabe916c130
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_schema/5) at 0x7fabe90f7760
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_schema/5
['post_transform_stats']\n", "\n", "
Channel of type 'ExampleStatistics' (1 artifact) at 0x7fabe916c2e0
.type_nameExampleStatistics
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleStatistics' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_stats/5) at 0x7fac0050f580
.type<class 'tfx.types.standard_artifacts.ExampleStatistics'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_stats/5
.span0
.split_names
['post_transform_anomalies']\n", "\n", "
Channel of type 'ExampleAnomalies' (1 artifact) at 0x7fabe916c160
.type_nameExampleAnomalies
._artifacts
[0]\n", "\n", "
Artifact of type 'ExampleAnomalies' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_anomalies/5) at 0x7fabe916cac0
.type<class 'tfx.types.standard_artifacts.ExampleAnomalies'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/post_transform_anomalies/5
.span0
.split_names
" ], "text/plain": [ "ExecutionResult(\n", " component_id: Transform\n", " execution_id: 5\n", " outputs:\n", " transform_graph: OutputChannel(artifact_type=TransformGraph, producer_component_id=Transform, output_key=transform_graph, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)\n", " transformed_examples: OutputChannel(artifact_type=Examples, producer_component_id=Transform, output_key=transformed_examples, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)\n", " updated_analyzer_cache: OutputChannel(artifact_type=TransformCache, producer_component_id=Transform, output_key=updated_analyzer_cache, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)\n", " pre_transform_schema: OutputChannel(artifact_type=Schema, producer_component_id=Transform, output_key=pre_transform_schema, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)\n", " pre_transform_stats: OutputChannel(artifact_type=ExampleStatistics, producer_component_id=Transform, output_key=pre_transform_stats, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)\n", " post_transform_schema: OutputChannel(artifact_type=Schema, producer_component_id=Transform, output_key=post_transform_schema, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)\n", " post_transform_stats: OutputChannel(artifact_type=ExampleStatistics, producer_component_id=Transform, output_key=post_transform_stats, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)\n", " post_transform_anomalies: OutputChannel(artifact_type=ExampleAnomalies, producer_component_id=Transform, output_key=post_transform_anomalies, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False))" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transform = tfx.components.Transform(\n", " examples=example_gen.outputs['examples'],\n", " schema=schema_gen.outputs['schema'],\n", " module_file=os.path.abspath(_taxi_transform_module_file))\n", "context.run(transform, enable_cache=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "fwAwb4rARRQ2" }, "source": [ "Let's examine the output artifacts of `Transform`. This component produces two types of outputs:\n", "\n", "* `transform_graph` is the graph that can perform the preprocessing operations (this graph will be included in the serving and evaluation models).\n", "* `transformed_examples` represents the preprocessed training and evaluation data." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:26:18.820586Z", "iopub.status.busy": "2024-08-02T09:26:18.819965Z", "iopub.status.idle": "2024-08-02T09:26:18.825033Z", "shell.execute_reply": "2024-08-02T09:26:18.824419Z" }, "id": "SClrAaEGR1O5" }, "outputs": [ { "data": { "text/plain": [ "{'transform_graph': OutputChannel(artifact_type=TransformGraph, producer_component_id=Transform, output_key=transform_graph, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),\n", " 'transformed_examples': OutputChannel(artifact_type=Examples, producer_component_id=Transform, output_key=transformed_examples, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),\n", " 'updated_analyzer_cache': OutputChannel(artifact_type=TransformCache, producer_component_id=Transform, output_key=updated_analyzer_cache, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),\n", " 'pre_transform_schema': OutputChannel(artifact_type=Schema, producer_component_id=Transform, output_key=pre_transform_schema, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),\n", " 'pre_transform_stats': OutputChannel(artifact_type=ExampleStatistics, producer_component_id=Transform, output_key=pre_transform_stats, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),\n", " 'post_transform_schema': OutputChannel(artifact_type=Schema, producer_component_id=Transform, output_key=post_transform_schema, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),\n", " 'post_transform_stats': OutputChannel(artifact_type=ExampleStatistics, producer_component_id=Transform, output_key=post_transform_stats, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),\n", " 'post_transform_anomalies': OutputChannel(artifact_type=ExampleAnomalies, producer_component_id=Transform, output_key=post_transform_anomalies, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transform.outputs" ] }, { "cell_type": "markdown", "metadata": { "id": "vyFkBd9AR1sy" }, "source": [ "Take a peek at the `transform_graph` artifact. It points to a directory containing three subdirectories." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:26:18.828443Z", "iopub.status.busy": "2024-08-02T09:26:18.827842Z", "iopub.status.idle": "2024-08-02T09:26:18.832709Z", "shell.execute_reply": "2024-08-02T09:26:18.832069Z" }, "id": "5tRw4DneR3i7" }, "outputs": [ { "data": { "text/plain": [ "['metadata', 'transformed_metadata', 'transform_fn']" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_uri = transform.outputs['transform_graph'].get()[0].uri\n", "os.listdir(train_uri)" ] }, { "cell_type": "markdown", "metadata": { "id": "4fqV54CIR6Pu" }, "source": [ "The `transformed_metadata` subdirectory contains the schema of the preprocessed data. The `transform_fn` subdirectory contains the actual preprocessing graph. The `metadata` subdirectory contains the schema of the original data.\n", "\n", "We can also take a look at the first three transformed examples:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:26:18.836012Z", "iopub.status.busy": "2024-08-02T09:26:18.835505Z", "iopub.status.idle": "2024-08-02T09:26:18.878204Z", "shell.execute_reply": "2024-08-02T09:26:18.877570Z" }, "id": "pwbW2zPKR_S4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "features {\n", " feature {\n", " key: \"company_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_census_tract_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_community_area_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_latitude_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_longitude_xf\"\n", " value {\n", " float_list {\n", " value: 9.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"fare_xf\"\n", " value {\n", " float_list {\n", " value: 0.061060599982738495\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"payment_type_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_census_tract_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_community_area_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_latitude_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_longitude_xf\"\n", " value {\n", " float_list {\n", " value: 9.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"tips\"\n", " value {\n", " int64_list {\n", " value: 0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_miles_xf\"\n", " value {\n", " float_list {\n", " value: -0.15886741876602173\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_seconds_xf\"\n", " value {\n", " float_list {\n", " value: -0.7118487358093262\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_day_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_hour_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_month_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", "}\n", "\n", "features {\n", " feature {\n", " key: \"company_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_census_tract_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_community_area_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_latitude_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_longitude_xf\"\n", " value {\n", " float_list {\n", " value: 9.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"fare_xf\"\n", " value {\n", " float_list {\n", " value: 1.2521240711212158\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"payment_type_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_census_tract_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_community_area_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_latitude_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_longitude_xf\"\n", " value {\n", " float_list {\n", " value: 3.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"tips\"\n", " value {\n", " int64_list {\n", " value: 0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_miles_xf\"\n", " value {\n", " float_list {\n", " value: 0.532160758972168\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_seconds_xf\"\n", " value {\n", " float_list {\n", " value: 0.5509493350982666\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_day_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_hour_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_month_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", "}\n", "\n", "features {\n", " feature {\n", " key: \"company_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_census_tract_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_community_area_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_latitude_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"dropoff_longitude_xf\"\n", " value {\n", " float_list {\n", " value: 9.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"fare_xf\"\n", " value {\n", " float_list {\n", " value: 0.3873794376850128\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"payment_type_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_census_tract_xf\"\n", " value {\n", " float_list {\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_community_area_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_latitude_xf\"\n", " value {\n", " float_list {\n", " value: 9.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"pickup_longitude_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"tips\"\n", " value {\n", " int64_list {\n", " value: 0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_miles_xf\"\n", " value {\n", " float_list {\n", " value: 0.21955277025699615\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_seconds_xf\"\n", " value {\n", " float_list {\n", " value: 0.0019067146349698305\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_day_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_hour_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"trip_start_month_xf\"\n", " value {\n", " float_list {\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 1.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " value: 0.0\n", " }\n", " }\n", " }\n", "}\n", "\n" ] } ], "source": [ "# Get the URI of the output artifact representing the transformed examples, which is a directory\n", "train_uri = os.path.join(transform.outputs['transformed_examples'].get()[0].uri, 'Split-train')\n", "\n", "# Get the list of files in this directory (all compressed TFRecord files)\n", "tfrecord_filenames = [os.path.join(train_uri, name)\n", " for name in os.listdir(train_uri)]\n", "\n", "# Create a `TFRecordDataset` to read these files\n", "dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type=\"GZIP\")\n", "\n", "# Iterate over the first 3 records and decode them.\n", "for tfrecord in dataset.take(3):\n", " serialized_example = tfrecord.numpy()\n", " example = tf.train.Example()\n", " example.ParseFromString(serialized_example)\n", " pp.pprint(example)" ] }, { "cell_type": "markdown", "metadata": { "id": "q_b_V6eN4f69" }, "source": [ "After the `Transform` component has transformed your data into features, and the next step is to train a model." ] }, { "cell_type": "markdown", "metadata": { "id": "OBJFtnl6lCg9" }, "source": [ "### Trainer\n", "The `Trainer` component will train a model that you define in TensorFlow.\n", "\n", "`Trainer` takes as input the schema from `SchemaGen`, the transformed data and graph from `Transform`, training parameters, as well as a module that contains user-defined model code.\n", "\n", "Let's see an example of user-defined model code below (for an introduction to the TensorFlow Keras APIs, [see the tutorial](https://www.tensorflow.org/guide/keras)):" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:26:18.881640Z", "iopub.status.busy": "2024-08-02T09:26:18.881207Z", "iopub.status.idle": "2024-08-02T09:26:18.884485Z", "shell.execute_reply": "2024-08-02T09:26:18.883820Z" }, "id": "N1376oq04YJt" }, "outputs": [], "source": [ "_taxi_trainer_module_file = 'taxi_trainer.py'" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:26:18.887758Z", "iopub.status.busy": "2024-08-02T09:26:18.887490Z", "iopub.status.idle": "2024-08-02T09:26:18.895683Z", "shell.execute_reply": "2024-08-02T09:26:18.894980Z" }, "id": "nf9UuNng4YJu" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing taxi_trainer.py\n" ] } ], "source": [ "%%writefile {_taxi_trainer_module_file}\n", "\n", "from typing import Dict, List, Text\n", "\n", "import os\n", "import glob\n", "from absl import logging\n", "\n", "import datetime\n", "import tensorflow as tf\n", "import tensorflow_transform as tft\n", "\n", "from tfx import v1 as tfx\n", "from tfx_bsl.public import tfxio\n", "from tensorflow_transform import TFTransformOutput\n", "\n", "# Imported files such as taxi_constants are normally cached, so changes are\n", "# not honored after the first import. Normally this is good for efficiency, but\n", "# during development when we may be iterating code it can be a problem. To\n", "# avoid this problem during development, reload the file.\n", "import taxi_constants\n", "import sys\n", "if 'google.colab' in sys.modules: # Testing to see if we're doing development\n", " import importlib\n", " importlib.reload(taxi_constants)\n", "\n", "_LABEL_KEY = taxi_constants.LABEL_KEY\n", "\n", "_BATCH_SIZE = 40\n", "\n", "\n", "def _input_fn(file_pattern: List[Text],\n", " data_accessor: tfx.components.DataAccessor,\n", " tf_transform_output: tft.TFTransformOutput,\n", " batch_size: int = 200) -> tf.data.Dataset:\n", " \"\"\"Generates features and label for tuning/training.\n", "\n", " Args:\n", " file_pattern: List of paths or patterns of input tfrecord files.\n", " data_accessor: DataAccessor for converting input to RecordBatch.\n", " tf_transform_output: A TFTransformOutput.\n", " batch_size: representing the number of consecutive elements of returned\n", " dataset to combine in a single batch\n", "\n", " Returns:\n", " A dataset that contains (features, indices) tuple where features is a\n", " dictionary of Tensors, and indices is a single Tensor of label indices.\n", " \"\"\"\n", " return data_accessor.tf_dataset_factory(\n", " file_pattern,\n", " tfxio.TensorFlowDatasetOptions(\n", " batch_size=batch_size, label_key=_LABEL_KEY),\n", " tf_transform_output.transformed_metadata.schema)\n", "\n", "def _get_tf_examples_serving_signature(model, tf_transform_output):\n", " \"\"\"Returns a serving signature that accepts `tensorflow.Example`.\"\"\"\n", "\n", " # We need to track the layers in the model in order to save it.\n", " # TODO(b/162357359): Revise once the bug is resolved.\n", " model.tft_layer_inference = tf_transform_output.transform_features_layer()\n", "\n", " @tf.function(input_signature=[\n", " tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')\n", " ])\n", " def serve_tf_examples_fn(serialized_tf_example):\n", " \"\"\"Returns the output to be used in the serving signature.\"\"\"\n", " raw_feature_spec = tf_transform_output.raw_feature_spec()\n", " # Remove label feature since these will not be present at serving time.\n", " raw_feature_spec.pop(_LABEL_KEY)\n", " raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)\n", " transformed_features = model.tft_layer_inference(raw_features)\n", " logging.info('serve_transformed_features = %s', transformed_features)\n", "\n", " outputs = model(transformed_features)\n", " # TODO(b/154085620): Convert the predicted labels from the model using a\n", " # reverse-lookup (opposite of transform.py).\n", " return {'outputs': outputs}\n", "\n", " return serve_tf_examples_fn\n", "\n", "\n", "def _get_transform_features_signature(model, tf_transform_output):\n", " \"\"\"Returns a serving signature that applies tf.Transform to features.\"\"\"\n", "\n", " # We need to track the layers in the model in order to save it.\n", " # TODO(b/162357359): Revise once the bug is resolved.\n", " model.tft_layer_eval = tf_transform_output.transform_features_layer()\n", "\n", " @tf.function(input_signature=[\n", " tf.TensorSpec(shape=[None], dtype=tf.string, name='examples')\n", " ])\n", " def transform_features_fn(serialized_tf_example):\n", " \"\"\"Returns the transformed_features to be fed as input to evaluator.\"\"\"\n", " raw_feature_spec = tf_transform_output.raw_feature_spec()\n", " raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)\n", " transformed_features = model.tft_layer_eval(raw_features)\n", " logging.info('eval_transformed_features = %s', transformed_features)\n", " return transformed_features\n", "\n", " return transform_features_fn\n", "\n", "\n", "def export_serving_model(tf_transform_output, model, output_dir):\n", " \"\"\"Exports a keras model for serving.\n", " Args:\n", " tf_transform_output: Wrapper around output of tf.Transform.\n", " model: A keras model to export for serving.\n", " output_dir: A directory where the model will be exported to.\n", " \"\"\"\n", " # The layer has to be saved to the model for keras tracking purpases.\n", " model.tft_layer = tf_transform_output.transform_features_layer()\n", "\n", " signatures = {\n", " 'serving_default':\n", " _get_tf_examples_serving_signature(model, tf_transform_output),\n", " 'transform_features':\n", " _get_transform_features_signature(model, tf_transform_output),\n", " }\n", "\n", " model.save(output_dir, save_format='tf', signatures=signatures)\n", "\n", "\n", "def _build_keras_model(tf_transform_output: TFTransformOutput\n", " ) -> tf.keras.Model:\n", " \"\"\"Creates a DNN Keras model for classifying taxi data.\n", "\n", " Args:\n", " tf_transform_output: [TFTransformOutput], the outputs from Transform\n", "\n", " Returns:\n", " A keras Model.\n", " \"\"\"\n", " feature_spec = tf_transform_output.transformed_feature_spec().copy()\n", " feature_spec.pop(_LABEL_KEY)\n", "\n", " inputs = {}\n", " for key, spec in feature_spec.items():\n", " if isinstance(spec, tf.io.VarLenFeature):\n", " inputs[key] = tf.keras.layers.Input(\n", " shape=[None], name=key, dtype=spec.dtype, sparse=True)\n", " elif isinstance(spec, tf.io.FixedLenFeature):\n", " # TODO(b/208879020): Move into schema such that spec.shape is [1] and not\n", " # [] for scalars.\n", " inputs[key] = tf.keras.layers.Input(\n", " shape=spec.shape or [1], name=key, dtype=spec.dtype)\n", " else:\n", " raise ValueError('Spec type is not supported: ', key, spec)\n", "\n", " output = tf.keras.layers.Concatenate()(tf.nest.flatten(inputs))\n", " output = tf.keras.layers.Dense(100, activation='relu')(output)\n", " output = tf.keras.layers.Dense(70, activation='relu')(output)\n", " output = tf.keras.layers.Dense(50, activation='relu')(output)\n", " output = tf.keras.layers.Dense(20, activation='relu')(output)\n", " output = tf.keras.layers.Dense(1)(output)\n", " return tf.keras.Model(inputs=inputs, outputs=output)\n", "\n", "\n", "# TFX Trainer will call this function.\n", "def run_fn(fn_args: tfx.components.FnArgs):\n", " \"\"\"Train the model based on given args.\n", "\n", " Args:\n", " fn_args: Holds args used to train the model as name/value pairs.\n", " \"\"\"\n", " tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)\n", "\n", " train_dataset = _input_fn(fn_args.train_files, fn_args.data_accessor,\n", " tf_transform_output, _BATCH_SIZE)\n", " eval_dataset = _input_fn(fn_args.eval_files, fn_args.data_accessor,\n", " tf_transform_output, _BATCH_SIZE)\n", "\n", " model = _build_keras_model(tf_transform_output)\n", "\n", " model.compile(\n", " loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n", " metrics=[tf.keras.metrics.BinaryAccuracy()])\n", "\n", " tensorboard_callback = tf.keras.callbacks.TensorBoard(\n", " log_dir=fn_args.model_run_dir, update_freq='batch')\n", "\n", " model.fit(\n", " train_dataset,\n", " steps_per_epoch=fn_args.train_steps,\n", " validation_data=eval_dataset,\n", " validation_steps=fn_args.eval_steps,\n", " callbacks=[tensorboard_callback])\n", "\n", " # Export the model.\n", " export_serving_model(tf_transform_output, model, fn_args.serving_model_dir)" ] }, { "cell_type": "markdown", "metadata": { "id": "GY4yTRaX4YJx" }, "source": [ "Now, we pass in this model code to the `Trainer` component and run it to train the model." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:26:18.898630Z", "iopub.status.busy": "2024-08-02T09:26:18.898376Z", "iopub.status.idle": "2024-08-02T09:27:47.432282Z", "shell.execute_reply": "2024-08-02T09:27:47.431606Z" }, "id": "429-vvCWibO0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Generating ephemeral wheel package for '/tmpfs/src/temp/docs/tutorials/tfx/taxi_trainer.py' (including modules: ['taxi_trainer', 'taxi_transform', 'taxi_constants']).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:User module package has hash fingerprint version c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '/tmpfs/tmp/tmpjusxyuw6/_tfx_generated_setup.py', 'bdist_wheel', '--bdist-dir', '/tmpfs/tmp/tmpv4l9jvk4', '--dist-dir', '/tmpfs/tmp/tmpd232wkwu']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n", "!!\n", "\n", " ********************************************************************************\n", " Please avoid running ``setup.py`` directly.\n", " Instead, use pypa/build, pypa/installer or other\n", " standards-based tools.\n", "\n", " See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n", " ********************************************************************************\n", "\n", "!!\n", " self.initialize_options()\n", "INFO:absl:Successfully built user code wheel distribution at '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3-none-any.whl'; target user module is 'taxi_trainer'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Full user module path is 'taxi_trainer@/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3-none-any.whl'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running driver for Trainer\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "running bdist_wheel\n", "running build\n", "running build_py\n", "creating build\n", "creating build/lib\n", "copying taxi_trainer.py -> build/lib\n", "copying taxi_transform.py -> build/lib\n", "copying taxi_constants.py -> build/lib\n", "installing to /tmpfs/tmp/tmpv4l9jvk4\n", "running install\n", "running install_lib\n", "copying build/lib/taxi_trainer.py -> /tmpfs/tmp/tmpv4l9jvk4\n", "copying build/lib/taxi_transform.py -> /tmpfs/tmp/tmpv4l9jvk4\n", "copying build/lib/taxi_constants.py -> /tmpfs/tmp/tmpv4l9jvk4\n", "running install_egg_info\n", "running egg_info\n", "creating tfx_user_code_Trainer.egg-info\n", "writing tfx_user_code_Trainer.egg-info/PKG-INFO\n", "writing dependency_links to tfx_user_code_Trainer.egg-info/dependency_links.txt\n", "writing top-level names to tfx_user_code_Trainer.egg-info/top_level.txt\n", "writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'\n", "reading manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'\n", "writing manifest file 'tfx_user_code_Trainer.egg-info/SOURCES.txt'\n", "Copying tfx_user_code_Trainer.egg-info to /tmpfs/tmp/tmpv4l9jvk4/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3.9.egg-info\n", "running install_scripts\n", "creating /tmpfs/tmp/tmpv4l9jvk4/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d.dist-info/WHEEL\n", "creating '/tmpfs/tmp/tmpd232wkwu/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3-none-any.whl' and adding '/tmpfs/tmp/tmpv4l9jvk4' to it\n", "adding 'taxi_constants.py'\n", "adding 'taxi_trainer.py'\n", "adding 'taxi_transform.py'\n", "adding 'tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d.dist-info/METADATA'\n", "adding 'tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d.dist-info/WHEEL'\n", "adding 'tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d.dist-info/top_level.txt'\n", "adding 'tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d.dist-info/RECORD'\n", "removing /tmpfs/tmp/tmpv4l9jvk4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running executor for Trainer\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Train on the 'train' split when train_args.splits is not set.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:Examples artifact does not have payload_format custom property. Falling back to FORMAT_TF_EXAMPLE\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:udf_utils.get_fn {'train_args': '{\\n \"num_steps\": 10000\\n}', 'eval_args': '{\\n \"num_steps\": 5000\\n}', 'module_file': None, 'run_fn': None, 'trainer_fn': None, 'custom_config': 'null', 'module_path': 'taxi_trainer@/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3-none-any.whl'} 'run_fn'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Installing '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3-none-any.whl' to a temporary directory.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Executing: ['/tmpfs/src/tf_docs_env/bin/python', '-m', 'pip', 'install', '--target', '/tmpfs/tmp/tmpkzpl88qp', '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3-none-any.whl']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processing /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3-none-any.whl\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Successfully installed '/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3-none-any.whl'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Training model.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company_xf has a shape dim {\n", " size: 55\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract_xf has a shape dim {\n", " size: 216\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area_xf has a shape dim {\n", " size: 79\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type_xf has a shape dim {\n", " size: 16\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract_xf has a shape dim {\n", " size: 11\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area_xf has a shape dim {\n", " size: 66\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day_xf has a shape dim {\n", " size: 17\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour_xf has a shape dim {\n", " size: 34\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month_xf has a shape dim {\n", " size: 22\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Installing collected packages: tfx-user-code-Trainer\n", "Successfully installed tfx-user-code-Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tfx_bsl/tfxio/tf_example_record.py:343: parse_example_dataset (from tensorflow.python.data.experimental.ops.parsing_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.data.Dataset.map(tf.io.parse_example(...))` instead.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company_xf has a shape dim {\n", " size: 55\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract_xf has a shape dim {\n", " size: 216\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area_xf has a shape dim {\n", " size: 79\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type_xf has a shape dim {\n", " size: 16\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract_xf has a shape dim {\n", " size: 11\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area_xf has a shape dim {\n", " size: 66\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day_xf has a shape dim {\n", " size: 17\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour_xf has a shape dim {\n", " size: 34\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month_xf has a shape dim {\n", " size: 22\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company_xf has a shape dim {\n", " size: 55\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract_xf has a shape dim {\n", " size: 216\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area_xf has a shape dim {\n", " size: 79\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type_xf has a shape dim {\n", " size: 16\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract_xf has a shape dim {\n", " size: 11\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area_xf has a shape dim {\n", " size: 66\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day_xf has a shape dim {\n", " size: 17\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour_xf has a shape dim {\n", " size: 34\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month_xf has a shape dim {\n", " size: 22\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company_xf has a shape dim {\n", " size: 55\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract_xf has a shape dim {\n", " size: 216\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area_xf has a shape dim {\n", " size: 79\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type_xf has a shape dim {\n", " size: 16\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract_xf has a shape dim {\n", " size: 11\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area_xf has a shape dim {\n", " size: 66\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day_xf has a shape dim {\n", " size: 17\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour_xf has a shape dim {\n", " size: 34\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month_xf has a shape dim {\n", " size: 22\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company_xf has a shape dim {\n", " size: 55\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract_xf has a shape dim {\n", " size: 216\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area_xf has a shape dim {\n", " size: 79\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type_xf has a shape dim {\n", " size: 16\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract_xf has a shape dim {\n", " size: 11\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area_xf has a shape dim {\n", " size: 66\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds_xf has a shape . Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day_xf has a shape dim {\n", " size: 17\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour_xf has a shape dim {\n", " size: 34\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month_xf has a shape dim {\n", " size: 22\n", "}\n", ". Setting to DenseTensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1722590785.122239 22788 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/10000 [..............................] - ETA: 8:33:22 - loss: 0.6906 - binary_accuracy: 0.9000" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9/10000 [..............................] - ETA: 1:04 - loss: 0.6063 - binary_accuracy: 0.7861 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 18/10000 [..............................] - ETA: 1:03 - loss: 0.5643 - binary_accuracy: 0.7847" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 26/10000 [..............................] - ETA: 1:02 - loss: 0.5715 - binary_accuracy: 0.7663" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 34/10000 [..............................] - ETA: 1:02 - loss: 0.5572 - binary_accuracy: 0.7684" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 43/10000 [..............................] - ETA: 1:02 - loss: 0.5380 - binary_accuracy: 0.7709" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 52/10000 [..............................] - ETA: 1:02 - loss: 0.5164 - binary_accuracy: 0.7769" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 61/10000 [..............................] - ETA: 1:02 - loss: 0.4909 - binary_accuracy: 0.7828" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 70/10000 [..............................] - ETA: 1:01 - loss: 0.4688 - binary_accuracy: 0.7832" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 79/10000 [..............................] - ETA: 1:01 - loss: 0.4477 - binary_accuracy: 0.7845" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 87/10000 [..............................] - ETA: 1:01 - loss: 0.4249 - binary_accuracy: 0.7960" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 95/10000 [..............................] - ETA: 1:01 - loss: 0.4093 - binary_accuracy: 0.8005" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 104/10000 [..............................] - ETA: 1:01 - loss: 0.3914 - binary_accuracy: 0.8075" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 113/10000 [..............................] - ETA: 1:01 - loss: 0.3801 - binary_accuracy: 0.8115" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 122/10000 [..............................] - ETA: 1:01 - loss: 0.3693 - binary_accuracy: 0.8123" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 131/10000 [..............................] - ETA: 1:01 - loss: 0.3569 - binary_accuracy: 0.8197" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 140/10000 [..............................] - ETA: 1:01 - loss: 0.3469 - binary_accuracy: 0.8216" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 148/10000 [..............................] - ETA: 1:01 - loss: 0.3389 - binary_accuracy: 0.8257" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 156/10000 [..............................] - ETA: 1:01 - loss: 0.3320 - binary_accuracy: 0.8284" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 164/10000 [..............................] - ETA: 1:01 - loss: 0.3271 - binary_accuracy: 0.8277" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 172/10000 [..............................] - ETA: 1:01 - loss: 0.3215 - binary_accuracy: 0.8314" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 180/10000 [..............................] - ETA: 1:01 - loss: 0.3165 - binary_accuracy: 0.8339" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 188/10000 [..............................] - ETA: 1:01 - loss: 0.3115 - binary_accuracy: 0.8354" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 196/10000 [..............................] - ETA: 1:01 - loss: 0.3067 - binary_accuracy: 0.8376" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 204/10000 [..............................] - ETA: 1:01 - loss: 0.3023 - binary_accuracy: 0.8400" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 212/10000 [..............................] - ETA: 1:00 - loss: 0.2988 - binary_accuracy: 0.8413" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 220/10000 [..............................] - ETA: 1:00 - loss: 0.2949 - binary_accuracy: 0.8427" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 228/10000 [..............................] - ETA: 1:00 - loss: 0.2916 - binary_accuracy: 0.8447" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 237/10000 [..............................] - ETA: 1:00 - loss: 0.2896 - binary_accuracy: 0.8443" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 246/10000 [..............................] - ETA: 1:00 - loss: 0.2866 - binary_accuracy: 0.8461" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 255/10000 [..............................] - ETA: 1:00 - loss: 0.2837 - binary_accuracy: 0.8478" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 264/10000 [..............................] - ETA: 1:00 - loss: 0.2807 - binary_accuracy: 0.8497" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 272/10000 [..............................] - ETA: 1:00 - loss: 0.2785 - binary_accuracy: 0.8507" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 281/10000 [..............................] - ETA: 1:00 - loss: 0.2749 - binary_accuracy: 0.8530" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 289/10000 [..............................] - ETA: 1:00 - loss: 0.2729 - binary_accuracy: 0.8539" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 298/10000 [..............................] - ETA: 1:00 - loss: 0.2712 - binary_accuracy: 0.8541" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 306/10000 [..............................] - ETA: 1:00 - loss: 0.2687 - binary_accuracy: 0.8556" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 315/10000 [..............................] - ETA: 1:00 - loss: 0.2666 - binary_accuracy: 0.8567" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 324/10000 [..............................] - ETA: 1:00 - loss: 0.2639 - binary_accuracy: 0.8583" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 332/10000 [..............................] - ETA: 1:00 - loss: 0.2623 - binary_accuracy: 0.8590" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 340/10000 [>.............................] - ETA: 1:00 - loss: 0.2607 - binary_accuracy: 0.8596" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 348/10000 [>.............................] - ETA: 1:00 - loss: 0.2598 - binary_accuracy: 0.8603" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 357/10000 [>.............................] - ETA: 1:00 - loss: 0.2581 - binary_accuracy: 0.8609" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 365/10000 [>.............................] - ETA: 1:00 - loss: 0.2562 - binary_accuracy: 0.8615" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 374/10000 [>.............................] - ETA: 59s - loss: 0.2545 - binary_accuracy: 0.8622 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 382/10000 [>.............................] - ETA: 59s - loss: 0.2533 - binary_accuracy: 0.8631" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 391/10000 [>.............................] - ETA: 59s - loss: 0.2523 - binary_accuracy: 0.8639" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 399/10000 [>.............................] - ETA: 59s - loss: 0.2504 - binary_accuracy: 0.8655" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 408/10000 [>.............................] - ETA: 59s - loss: 0.2494 - binary_accuracy: 0.8661" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 416/10000 [>.............................] - ETA: 59s - loss: 0.2487 - binary_accuracy: 0.8665" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 425/10000 [>.............................] - ETA: 59s - loss: 0.2476 - binary_accuracy: 0.8671" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 433/10000 [>.............................] - ETA: 59s - loss: 0.2466 - binary_accuracy: 0.8673" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 441/10000 [>.............................] - ETA: 59s - loss: 0.2456 - binary_accuracy: 0.8679" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 449/10000 [>.............................] - ETA: 59s - loss: 0.2455 - binary_accuracy: 0.8682" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 457/10000 [>.............................] - ETA: 59s - loss: 0.2448 - binary_accuracy: 0.8688" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 465/10000 [>.............................] - ETA: 59s - loss: 0.2442 - binary_accuracy: 0.8688" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 473/10000 [>.............................] - ETA: 59s - loss: 0.2437 - binary_accuracy: 0.8687" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 482/10000 [>.............................] - ETA: 59s - loss: 0.2428 - binary_accuracy: 0.8691" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 490/10000 [>.............................] - ETA: 59s - loss: 0.2423 - binary_accuracy: 0.8695" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 499/10000 [>.............................] - ETA: 59s - loss: 0.2418 - binary_accuracy: 0.8697" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 507/10000 [>.............................] - ETA: 59s - loss: 0.2403 - binary_accuracy: 0.8711" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 515/10000 [>.............................] - ETA: 59s - loss: 0.2394 - binary_accuracy: 0.8718" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 523/10000 [>.............................] - ETA: 59s - loss: 0.2383 - binary_accuracy: 0.8725" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 531/10000 [>.............................] - ETA: 59s - loss: 0.2378 - binary_accuracy: 0.8731" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 539/10000 [>.............................] - ETA: 59s - loss: 0.2372 - binary_accuracy: 0.8732" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 547/10000 [>.............................] - ETA: 59s - loss: 0.2368 - binary_accuracy: 0.8733" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 556/10000 [>.............................] - ETA: 59s - loss: 0.2361 - binary_accuracy: 0.8740" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 564/10000 [>.............................] - ETA: 58s - loss: 0.2359 - binary_accuracy: 0.8742" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 572/10000 [>.............................] - ETA: 58s - loss: 0.2350 - binary_accuracy: 0.8749" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 580/10000 [>.............................] - ETA: 58s - loss: 0.2338 - binary_accuracy: 0.8756" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 588/10000 [>.............................] - ETA: 58s - loss: 0.2333 - binary_accuracy: 0.8759" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 597/10000 [>.............................] - ETA: 58s - loss: 0.2319 - binary_accuracy: 0.8765" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 605/10000 [>.............................] - ETA: 58s - loss: 0.2318 - binary_accuracy: 0.8763" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 613/10000 [>.............................] - ETA: 58s - loss: 0.2316 - binary_accuracy: 0.8765" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 621/10000 [>.............................] - ETA: 58s - loss: 0.2311 - binary_accuracy: 0.8768" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 630/10000 [>.............................] - ETA: 58s - loss: 0.2306 - binary_accuracy: 0.8771" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 638/10000 [>.............................] - ETA: 58s - loss: 0.2302 - binary_accuracy: 0.8772" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 646/10000 [>.............................] - ETA: 58s - loss: 0.2301 - binary_accuracy: 0.8767" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 654/10000 [>.............................] - ETA: 58s - loss: 0.2296 - binary_accuracy: 0.8773" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 662/10000 [>.............................] - ETA: 58s - loss: 0.2297 - binary_accuracy: 0.8772" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 671/10000 [=>............................] - ETA: 58s - loss: 0.2291 - binary_accuracy: 0.8773" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 679/10000 [=>............................] - ETA: 58s - loss: 0.2283 - binary_accuracy: 0.8778" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 687/10000 [=>............................] - ETA: 58s - loss: 0.2278 - binary_accuracy: 0.8782" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 695/10000 [=>............................] - ETA: 58s - loss: 0.2272 - binary_accuracy: 0.8787" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 703/10000 [=>............................] - ETA: 58s - loss: 0.2269 - binary_accuracy: 0.8789" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 712/10000 [=>............................] - ETA: 58s - loss: 0.2262 - binary_accuracy: 0.8794" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 721/10000 [=>............................] - ETA: 58s - loss: 0.2258 - binary_accuracy: 0.8795" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 729/10000 [=>............................] - ETA: 58s - loss: 0.2253 - binary_accuracy: 0.8798" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 737/10000 [=>............................] - ETA: 58s - loss: 0.2251 - binary_accuracy: 0.8801" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 745/10000 [=>............................] - ETA: 58s - loss: 0.2247 - binary_accuracy: 0.8802" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 753/10000 [=>............................] - ETA: 57s - loss: 0.2243 - binary_accuracy: 0.8805" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 762/10000 [=>............................] - ETA: 57s - loss: 0.2237 - binary_accuracy: 0.8810" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 771/10000 [=>............................] - ETA: 57s - loss: 0.2232 - binary_accuracy: 0.8813" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 779/10000 [=>............................] - ETA: 57s - loss: 0.2228 - binary_accuracy: 0.8817" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 787/10000 [=>............................] - ETA: 57s - loss: 0.2223 - binary_accuracy: 0.8820" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 795/10000 [=>............................] - ETA: 57s - loss: 0.2223 - binary_accuracy: 0.8819" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 803/10000 [=>............................] - ETA: 57s - loss: 0.2219 - binary_accuracy: 0.8822" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 811/10000 [=>............................] - ETA: 57s - loss: 0.2215 - binary_accuracy: 0.8826" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 819/10000 [=>............................] - ETA: 57s - loss: 0.2213 - binary_accuracy: 0.8830" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 827/10000 [=>............................] - ETA: 57s - loss: 0.2208 - binary_accuracy: 0.8832" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 835/10000 [=>............................] - ETA: 57s - loss: 0.2205 - binary_accuracy: 0.8833" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 843/10000 [=>............................] - ETA: 57s - loss: 0.2202 - binary_accuracy: 0.8836" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 851/10000 [=>............................] - ETA: 57s - loss: 0.2202 - binary_accuracy: 0.8835" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 860/10000 [=>............................] - ETA: 57s - loss: 0.2194 - binary_accuracy: 0.8842" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 869/10000 [=>............................] - ETA: 57s - loss: 0.2198 - binary_accuracy: 0.8841" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 878/10000 [=>............................] - ETA: 57s - loss: 0.2192 - binary_accuracy: 0.8844" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 886/10000 [=>............................] - ETA: 57s - loss: 0.2186 - binary_accuracy: 0.8846" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 895/10000 [=>............................] - ETA: 57s - loss: 0.2189 - binary_accuracy: 0.8844" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 903/10000 [=>............................] - ETA: 57s - loss: 0.2186 - binary_accuracy: 0.8845" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 912/10000 [=>............................] - ETA: 56s - loss: 0.2184 - binary_accuracy: 0.8844" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 921/10000 [=>............................] - ETA: 56s - loss: 0.2181 - binary_accuracy: 0.8846" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 930/10000 [=>............................] - ETA: 56s - loss: 0.2181 - binary_accuracy: 0.8846" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 939/10000 [=>............................] - ETA: 56s - loss: 0.2175 - binary_accuracy: 0.8850" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 948/10000 [=>............................] - ETA: 56s - loss: 0.2173 - binary_accuracy: 0.8852" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 957/10000 [=>............................] - ETA: 56s - loss: 0.2170 - binary_accuracy: 0.8853" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 966/10000 [=>............................] - ETA: 56s - loss: 0.2166 - binary_accuracy: 0.8855" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 975/10000 [=>............................] - ETA: 56s - loss: 0.2162 - binary_accuracy: 0.8859" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 984/10000 [=>............................] - ETA: 56s - loss: 0.2157 - binary_accuracy: 0.8862" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 993/10000 [=>............................] - ETA: 56s - loss: 0.2157 - binary_accuracy: 0.8861" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1001/10000 [==>...........................] - ETA: 56s - loss: 0.2155 - binary_accuracy: 0.8863" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1010/10000 [==>...........................] - ETA: 56s - loss: 0.2149 - binary_accuracy: 0.8868" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1019/10000 [==>...........................] - ETA: 56s - loss: 0.2150 - binary_accuracy: 0.8867" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1028/10000 [==>...........................] - ETA: 56s - loss: 0.2147 - binary_accuracy: 0.8867" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1037/10000 [==>...........................] - ETA: 56s - loss: 0.2143 - binary_accuracy: 0.8871" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1045/10000 [==>...........................] - ETA: 56s - loss: 0.2138 - binary_accuracy: 0.8874" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1054/10000 [==>...........................] - ETA: 56s - loss: 0.2134 - binary_accuracy: 0.8878" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1063/10000 [==>...........................] - ETA: 55s - loss: 0.2131 - binary_accuracy: 0.8880" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1072/10000 [==>...........................] - ETA: 55s - loss: 0.2127 - binary_accuracy: 0.8882" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1080/10000 [==>...........................] - ETA: 55s - loss: 0.2126 - binary_accuracy: 0.8884" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1089/10000 [==>...........................] - ETA: 55s - loss: 0.2124 - binary_accuracy: 0.8885" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1098/10000 [==>...........................] - ETA: 55s - loss: 0.2124 - binary_accuracy: 0.8883" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1107/10000 [==>...........................] - ETA: 55s - loss: 0.2122 - binary_accuracy: 0.8885" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1115/10000 [==>...........................] - ETA: 55s - loss: 0.2119 - binary_accuracy: 0.8886" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1124/10000 [==>...........................] - ETA: 55s - loss: 0.2116 - binary_accuracy: 0.8886" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1133/10000 [==>...........................] - ETA: 55s - loss: 0.2112 - binary_accuracy: 0.8889" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1142/10000 [==>...........................] - ETA: 55s - loss: 0.2108 - binary_accuracy: 0.8892" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1151/10000 [==>...........................] - ETA: 55s - loss: 0.2106 - binary_accuracy: 0.8894" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1160/10000 [==>...........................] - ETA: 55s - loss: 0.2105 - binary_accuracy: 0.8893" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1169/10000 [==>...........................] - ETA: 55s - loss: 0.2102 - binary_accuracy: 0.8895" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1178/10000 [==>...........................] - ETA: 55s - loss: 0.2101 - binary_accuracy: 0.8895" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1187/10000 [==>...........................] - ETA: 55s - loss: 0.2097 - binary_accuracy: 0.8898" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1196/10000 [==>...........................] - ETA: 55s - loss: 0.2095 - binary_accuracy: 0.8898" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1205/10000 [==>...........................] - ETA: 54s - loss: 0.2092 - binary_accuracy: 0.8901" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1214/10000 [==>...........................] - ETA: 54s - loss: 0.2090 - binary_accuracy: 0.8902" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1223/10000 [==>...........................] - ETA: 54s - loss: 0.2094 - binary_accuracy: 0.8900" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1232/10000 [==>...........................] - ETA: 54s - loss: 0.2090 - binary_accuracy: 0.8902" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1241/10000 [==>...........................] - ETA: 54s - loss: 0.2087 - binary_accuracy: 0.8904" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1250/10000 [==>...........................] - ETA: 54s - loss: 0.2083 - binary_accuracy: 0.8906" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1259/10000 [==>...........................] - ETA: 54s - loss: 0.2080 - binary_accuracy: 0.8908" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1268/10000 [==>...........................] - ETA: 54s - loss: 0.2077 - binary_accuracy: 0.8909" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1277/10000 [==>...........................] - ETA: 54s - loss: 0.2073 - binary_accuracy: 0.8911" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1286/10000 [==>...........................] - ETA: 54s - loss: 0.2069 - binary_accuracy: 0.8913" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1295/10000 [==>...........................] - ETA: 54s - loss: 0.2066 - binary_accuracy: 0.8915" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1304/10000 [==>...........................] - ETA: 54s - loss: 0.2063 - binary_accuracy: 0.8918" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1313/10000 [==>...........................] - ETA: 54s - loss: 0.2061 - binary_accuracy: 0.8918" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1322/10000 [==>...........................] - ETA: 54s - loss: 0.2059 - binary_accuracy: 0.8919" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1331/10000 [==>...........................] - ETA: 53s - loss: 0.2055 - binary_accuracy: 0.8922" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1340/10000 [===>..........................] - ETA: 53s - loss: 0.2054 - binary_accuracy: 0.8922" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1349/10000 [===>..........................] - ETA: 53s - loss: 0.2051 - binary_accuracy: 0.8924" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1358/10000 [===>..........................] - ETA: 53s - loss: 0.2050 - binary_accuracy: 0.8924" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1367/10000 [===>..........................] - ETA: 53s - loss: 0.2048 - binary_accuracy: 0.8926" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1376/10000 [===>..........................] - ETA: 53s - loss: 0.2044 - binary_accuracy: 0.8928" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1385/10000 [===>..........................] - ETA: 53s - loss: 0.2042 - binary_accuracy: 0.8930" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1394/10000 [===>..........................] - ETA: 53s - loss: 0.2042 - binary_accuracy: 0.8929" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1403/10000 [===>..........................] - ETA: 53s - loss: 0.2038 - binary_accuracy: 0.8931" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1412/10000 [===>..........................] - ETA: 53s - loss: 0.2036 - binary_accuracy: 0.8933" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1421/10000 [===>..........................] - ETA: 53s - loss: 0.2037 - binary_accuracy: 0.8931" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1430/10000 [===>..........................] - ETA: 53s - loss: 0.2036 - binary_accuracy: 0.8932" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1439/10000 [===>..........................] - ETA: 53s - loss: 0.2035 - binary_accuracy: 0.8933" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1448/10000 [===>..........................] - ETA: 53s - loss: 0.2034 - binary_accuracy: 0.8932" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1457/10000 [===>..........................] - ETA: 53s - loss: 0.2032 - binary_accuracy: 0.8934" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1466/10000 [===>..........................] - ETA: 53s - loss: 0.2033 - binary_accuracy: 0.8934" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1475/10000 [===>..........................] - ETA: 52s - loss: 0.2033 - binary_accuracy: 0.8933" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1484/10000 [===>..........................] - ETA: 52s - loss: 0.2030 - binary_accuracy: 0.8935" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1493/10000 [===>..........................] - ETA: 52s - loss: 0.2029 - binary_accuracy: 0.8936" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1502/10000 [===>..........................] - ETA: 52s - loss: 0.2026 - binary_accuracy: 0.8937" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1511/10000 [===>..........................] - ETA: 52s - loss: 0.2023 - binary_accuracy: 0.8941" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1520/10000 [===>..........................] - ETA: 52s - loss: 0.2021 - binary_accuracy: 0.8942" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1529/10000 [===>..........................] - ETA: 52s - loss: 0.2018 - binary_accuracy: 0.8945" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1538/10000 [===>..........................] - ETA: 52s - loss: 0.2017 - binary_accuracy: 0.8945" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1547/10000 [===>..........................] - ETA: 52s - loss: 0.2015 - binary_accuracy: 0.8947" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1556/10000 [===>..........................] - ETA: 52s - loss: 0.2014 - binary_accuracy: 0.8947" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1564/10000 [===>..........................] - ETA: 52s - loss: 0.2013 - binary_accuracy: 0.8949" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1573/10000 [===>..........................] - ETA: 52s - loss: 0.2012 - binary_accuracy: 0.8949" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1582/10000 [===>..........................] - ETA: 52s - loss: 0.2010 - binary_accuracy: 0.8951" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1591/10000 [===>..........................] - ETA: 52s - loss: 0.2008 - binary_accuracy: 0.8954" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1600/10000 [===>..........................] - ETA: 52s - loss: 0.2005 - binary_accuracy: 0.8955" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1608/10000 [===>..........................] - ETA: 52s - loss: 0.2003 - binary_accuracy: 0.8957" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1617/10000 [===>..........................] - ETA: 52s - loss: 0.2004 - binary_accuracy: 0.8957" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1626/10000 [===>..........................] - ETA: 51s - loss: 0.2003 - binary_accuracy: 0.8958" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1635/10000 [===>..........................] - ETA: 51s - loss: 0.2001 - binary_accuracy: 0.8959" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1644/10000 [===>..........................] - ETA: 51s - loss: 0.1999 - binary_accuracy: 0.8961" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1653/10000 [===>..........................] - ETA: 51s - loss: 0.1997 - binary_accuracy: 0.8962" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1662/10000 [===>..........................] - ETA: 51s - loss: 0.1996 - binary_accuracy: 0.8962" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1671/10000 [====>.........................] - ETA: 51s - loss: 0.1993 - binary_accuracy: 0.8963" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1679/10000 [====>.........................] - ETA: 51s - loss: 0.1990 - binary_accuracy: 0.8966" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1688/10000 [====>.........................] - ETA: 51s - loss: 0.1987 - binary_accuracy: 0.8968" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1697/10000 [====>.........................] - ETA: 51s - loss: 0.1986 - binary_accuracy: 0.8969" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1705/10000 [====>.........................] - ETA: 51s - loss: 0.1984 - binary_accuracy: 0.8971" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1714/10000 [====>.........................] - ETA: 51s - loss: 0.1982 - binary_accuracy: 0.8972" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1723/10000 [====>.........................] - ETA: 51s - loss: 0.1980 - binary_accuracy: 0.8972" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1731/10000 [====>.........................] - ETA: 51s - loss: 0.1978 - binary_accuracy: 0.8974" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1740/10000 [====>.........................] - ETA: 51s - loss: 0.1978 - binary_accuracy: 0.8973" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1749/10000 [====>.........................] - ETA: 51s - loss: 0.1976 - binary_accuracy: 0.8974" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1758/10000 [====>.........................] - ETA: 51s - loss: 0.1974 - binary_accuracy: 0.8975" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1766/10000 [====>.........................] - ETA: 51s - loss: 0.1971 - binary_accuracy: 0.8977" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1774/10000 [====>.........................] - ETA: 51s - loss: 0.1967 - binary_accuracy: 0.8979" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1782/10000 [====>.........................] - ETA: 50s - loss: 0.1965 - binary_accuracy: 0.8981" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1791/10000 [====>.........................] - ETA: 50s - loss: 0.1964 - binary_accuracy: 0.8981" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1800/10000 [====>.........................] - ETA: 50s - loss: 0.1963 - binary_accuracy: 0.8982" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1809/10000 [====>.........................] - ETA: 50s - loss: 0.1960 - binary_accuracy: 0.8984" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1818/10000 [====>.........................] - ETA: 50s - loss: 0.1957 - binary_accuracy: 0.8986" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1827/10000 [====>.........................] - ETA: 50s - loss: 0.1956 - binary_accuracy: 0.8987" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1836/10000 [====>.........................] - ETA: 50s - loss: 0.1953 - binary_accuracy: 0.8990" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1844/10000 [====>.........................] - ETA: 50s - loss: 0.1949 - binary_accuracy: 0.8992" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1853/10000 [====>.........................] - ETA: 50s - loss: 0.1947 - binary_accuracy: 0.8993" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1862/10000 [====>.........................] - ETA: 50s - loss: 0.1946 - binary_accuracy: 0.8994" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1871/10000 [====>.........................] - ETA: 50s - loss: 0.1946 - binary_accuracy: 0.8994" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1880/10000 [====>.........................] - ETA: 50s - loss: 0.1945 - binary_accuracy: 0.8994" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1889/10000 [====>.........................] - ETA: 50s - loss: 0.1944 - binary_accuracy: 0.8995" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1898/10000 [====>.........................] - ETA: 50s - loss: 0.1943 - binary_accuracy: 0.8996" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1907/10000 [====>.........................] - ETA: 50s - loss: 0.1942 - binary_accuracy: 0.8997" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1915/10000 [====>.........................] - ETA: 50s - loss: 0.1942 - binary_accuracy: 0.8997" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1924/10000 [====>.........................] - ETA: 50s - loss: 0.1942 - binary_accuracy: 0.8997" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1932/10000 [====>.........................] - ETA: 50s - loss: 0.1941 - binary_accuracy: 0.8998" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1941/10000 [====>.........................] - ETA: 49s - loss: 0.1939 - binary_accuracy: 0.8999" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1950/10000 [====>.........................] - ETA: 49s - loss: 0.1939 - binary_accuracy: 0.8999" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1959/10000 [====>.........................] - ETA: 49s - loss: 0.1936 - binary_accuracy: 0.9000" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1968/10000 [====>.........................] - ETA: 49s - loss: 0.1934 - binary_accuracy: 0.9001" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1977/10000 [====>.........................] - ETA: 49s - loss: 0.1933 - binary_accuracy: 0.9002" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1986/10000 [====>.........................] - ETA: 49s - loss: 0.1932 - binary_accuracy: 0.9003" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 1995/10000 [====>.........................] - ETA: 49s - loss: 0.1931 - binary_accuracy: 0.9004" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2004/10000 [=====>........................] - ETA: 49s - loss: 0.1930 - binary_accuracy: 0.9005" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2012/10000 [=====>........................] - ETA: 49s - loss: 0.1928 - binary_accuracy: 0.9006" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2020/10000 [=====>........................] - ETA: 49s - loss: 0.1926 - binary_accuracy: 0.9007" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2028/10000 [=====>........................] - ETA: 49s - loss: 0.1923 - binary_accuracy: 0.9009" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2037/10000 [=====>........................] - ETA: 49s - loss: 0.1920 - binary_accuracy: 0.9011" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2046/10000 [=====>........................] - ETA: 49s - loss: 0.1919 - binary_accuracy: 0.9011" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2054/10000 [=====>........................] - ETA: 49s - loss: 0.1918 - binary_accuracy: 0.9012" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2062/10000 [=====>........................] - ETA: 49s - loss: 0.1916 - binary_accuracy: 0.9013" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2071/10000 [=====>........................] - ETA: 49s - loss: 0.1914 - binary_accuracy: 0.9014" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2079/10000 [=====>........................] - ETA: 49s - loss: 0.1913 - binary_accuracy: 0.9015" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2087/10000 [=====>........................] - ETA: 49s - loss: 0.1912 - binary_accuracy: 0.9015" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2096/10000 [=====>........................] - ETA: 49s - loss: 0.1911 - binary_accuracy: 0.9016" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2105/10000 [=====>........................] - ETA: 48s - loss: 0.1908 - binary_accuracy: 0.9018" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2114/10000 [=====>........................] - ETA: 48s - loss: 0.1907 - binary_accuracy: 0.9019" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2123/10000 [=====>........................] - ETA: 48s - loss: 0.1905 - binary_accuracy: 0.9020" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2132/10000 [=====>........................] - ETA: 48s - loss: 0.1903 - binary_accuracy: 0.9021" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2140/10000 [=====>........................] - ETA: 48s - loss: 0.1902 - binary_accuracy: 0.9022" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2148/10000 [=====>........................] - ETA: 48s - loss: 0.1899 - binary_accuracy: 0.9023" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2156/10000 [=====>........................] - ETA: 48s - loss: 0.1898 - binary_accuracy: 0.9024" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2164/10000 [=====>........................] - ETA: 48s - loss: 0.1896 - binary_accuracy: 0.9025" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2172/10000 [=====>........................] - ETA: 48s - loss: 0.1895 - binary_accuracy: 0.9026" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2180/10000 [=====>........................] - ETA: 48s - loss: 0.1893 - binary_accuracy: 0.9027" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2188/10000 [=====>........................] - ETA: 48s - loss: 0.1891 - binary_accuracy: 0.9028" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2196/10000 [=====>........................] - ETA: 48s - loss: 0.1890 - binary_accuracy: 0.9029" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2204/10000 [=====>........................] - ETA: 48s - loss: 0.1888 - binary_accuracy: 0.9031" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2212/10000 [=====>........................] - ETA: 48s - loss: 0.1887 - binary_accuracy: 0.9031" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2221/10000 [=====>........................] - ETA: 48s - loss: 0.1886 - binary_accuracy: 0.9031" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2229/10000 [=====>........................] - ETA: 48s - loss: 0.1887 - binary_accuracy: 0.9031" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2237/10000 [=====>........................] - ETA: 48s - loss: 0.1886 - binary_accuracy: 0.9032" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2246/10000 [=====>........................] - ETA: 48s - loss: 0.1885 - binary_accuracy: 0.9032" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2254/10000 [=====>........................] - ETA: 48s - loss: 0.1885 - binary_accuracy: 0.9031" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2262/10000 [=====>........................] - ETA: 48s - loss: 0.1883 - binary_accuracy: 0.9033" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2271/10000 [=====>........................] - ETA: 47s - loss: 0.1880 - binary_accuracy: 0.9034" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2280/10000 [=====>........................] - ETA: 47s - loss: 0.1880 - binary_accuracy: 0.9034" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2288/10000 [=====>........................] - ETA: 47s - loss: 0.1878 - binary_accuracy: 0.9035" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2297/10000 [=====>........................] - ETA: 47s - loss: 0.1875 - binary_accuracy: 0.9037" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2306/10000 [=====>........................] - ETA: 47s - loss: 0.1875 - binary_accuracy: 0.9038" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2315/10000 [=====>........................] - ETA: 47s - loss: 0.1875 - binary_accuracy: 0.9037" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2324/10000 [=====>........................] - ETA: 47s - loss: 0.1872 - binary_accuracy: 0.9039" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2332/10000 [=====>........................] - ETA: 47s - loss: 0.1871 - binary_accuracy: 0.9040" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2341/10000 [======>.......................] - ETA: 47s - loss: 0.1869 - binary_accuracy: 0.9041" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2350/10000 [======>.......................] - ETA: 47s - loss: 0.1867 - binary_accuracy: 0.9042" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2358/10000 [======>.......................] - ETA: 47s - loss: 0.1866 - binary_accuracy: 0.9043" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2367/10000 [======>.......................] - ETA: 47s - loss: 0.1864 - binary_accuracy: 0.9045" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2376/10000 [======>.......................] - ETA: 47s - loss: 0.1862 - binary_accuracy: 0.9046" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2385/10000 [======>.......................] - ETA: 47s - loss: 0.1858 - binary_accuracy: 0.9048" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2394/10000 [======>.......................] - ETA: 47s - loss: 0.1857 - binary_accuracy: 0.9049" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2402/10000 [======>.......................] - ETA: 47s - loss: 0.1855 - binary_accuracy: 0.9050" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2411/10000 [======>.......................] - ETA: 47s - loss: 0.1855 - binary_accuracy: 0.9050" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2420/10000 [======>.......................] - ETA: 47s - loss: 0.1853 - binary_accuracy: 0.9051" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2429/10000 [======>.......................] - ETA: 46s - loss: 0.1853 - binary_accuracy: 0.9051" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2438/10000 [======>.......................] - ETA: 46s - loss: 0.1851 - binary_accuracy: 0.9052" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2447/10000 [======>.......................] - ETA: 46s - loss: 0.1849 - binary_accuracy: 0.9053" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2456/10000 [======>.......................] - ETA: 46s - loss: 0.1849 - binary_accuracy: 0.9053" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2464/10000 [======>.......................] - ETA: 46s - loss: 0.1849 - binary_accuracy: 0.9053" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2472/10000 [======>.......................] - ETA: 46s - loss: 0.1847 - binary_accuracy: 0.9054" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2480/10000 [======>.......................] - ETA: 46s - loss: 0.1846 - binary_accuracy: 0.9054" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2488/10000 [======>.......................] - ETA: 46s - loss: 0.1845 - binary_accuracy: 0.9054" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2496/10000 [======>.......................] - ETA: 46s - loss: 0.1843 - binary_accuracy: 0.9056" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2504/10000 [======>.......................] - ETA: 46s - loss: 0.1843 - binary_accuracy: 0.9056" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2512/10000 [======>.......................] - ETA: 46s - loss: 0.1842 - binary_accuracy: 0.9057" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2521/10000 [======>.......................] - ETA: 46s - loss: 0.1839 - binary_accuracy: 0.9059" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2529/10000 [======>.......................] - ETA: 46s - loss: 0.1837 - binary_accuracy: 0.9060" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2538/10000 [======>.......................] - ETA: 46s - loss: 0.1835 - binary_accuracy: 0.9061" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2546/10000 [======>.......................] - ETA: 46s - loss: 0.1834 - binary_accuracy: 0.9062" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2555/10000 [======>.......................] - ETA: 46s - loss: 0.1832 - binary_accuracy: 0.9063" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2563/10000 [======>.......................] - ETA: 46s - loss: 0.1830 - binary_accuracy: 0.9064" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2571/10000 [======>.......................] - ETA: 46s - loss: 0.1829 - binary_accuracy: 0.9065" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2580/10000 [======>.......................] - ETA: 46s - loss: 0.1827 - binary_accuracy: 0.9067" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2588/10000 [======>.......................] - ETA: 46s - loss: 0.1826 - binary_accuracy: 0.9067" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2597/10000 [======>.......................] - ETA: 45s - loss: 0.1824 - binary_accuracy: 0.9067" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2606/10000 [======>.......................] - ETA: 45s - loss: 0.1823 - binary_accuracy: 0.9068" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2615/10000 [======>.......................] - ETA: 45s - loss: 0.1820 - binary_accuracy: 0.9070" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2624/10000 [======>.......................] - ETA: 45s - loss: 0.1820 - binary_accuracy: 0.9071" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2633/10000 [======>.......................] - ETA: 45s - loss: 0.1818 - binary_accuracy: 0.9071" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2642/10000 [======>.......................] - ETA: 45s - loss: 0.1816 - binary_accuracy: 0.9072" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2651/10000 [======>.......................] - ETA: 45s - loss: 0.1815 - binary_accuracy: 0.9073" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2659/10000 [======>.......................] - ETA: 45s - loss: 0.1813 - binary_accuracy: 0.9074" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2667/10000 [=======>......................] - ETA: 45s - loss: 0.1812 - binary_accuracy: 0.9075" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2676/10000 [=======>......................] - ETA: 45s - loss: 0.1811 - binary_accuracy: 0.9075" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2685/10000 [=======>......................] - ETA: 45s - loss: 0.1809 - binary_accuracy: 0.9076" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2694/10000 [=======>......................] - ETA: 45s - loss: 0.1807 - binary_accuracy: 0.9077" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2703/10000 [=======>......................] - ETA: 45s - loss: 0.1807 - binary_accuracy: 0.9078" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2712/10000 [=======>......................] - ETA: 45s - loss: 0.1806 - binary_accuracy: 0.9079" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2720/10000 [=======>......................] - ETA: 45s - loss: 0.1805 - binary_accuracy: 0.9079" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2729/10000 [=======>......................] - ETA: 45s - loss: 0.1803 - binary_accuracy: 0.9080" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2738/10000 [=======>......................] - ETA: 45s - loss: 0.1805 - binary_accuracy: 0.9080" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2747/10000 [=======>......................] - ETA: 45s - loss: 0.1803 - binary_accuracy: 0.9081" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2756/10000 [=======>......................] - ETA: 44s - loss: 0.1801 - binary_accuracy: 0.9082" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2765/10000 [=======>......................] - ETA: 44s - loss: 0.1800 - binary_accuracy: 0.9083" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2774/10000 [=======>......................] - ETA: 44s - loss: 0.1798 - binary_accuracy: 0.9084" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2783/10000 [=======>......................] - ETA: 44s - loss: 0.1796 - binary_accuracy: 0.9085" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2792/10000 [=======>......................] - ETA: 44s - loss: 0.1794 - binary_accuracy: 0.9087" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2801/10000 [=======>......................] - ETA: 44s - loss: 0.1793 - binary_accuracy: 0.9087" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2810/10000 [=======>......................] - ETA: 44s - loss: 0.1790 - binary_accuracy: 0.9089" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2819/10000 [=======>......................] - ETA: 44s - loss: 0.1789 - binary_accuracy: 0.9089" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2828/10000 [=======>......................] - ETA: 44s - loss: 0.1787 - binary_accuracy: 0.9091" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2837/10000 [=======>......................] - ETA: 44s - loss: 0.1785 - binary_accuracy: 0.9092" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2846/10000 [=======>......................] - ETA: 44s - loss: 0.1784 - binary_accuracy: 0.9092" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2855/10000 [=======>......................] - ETA: 44s - loss: 0.1785 - binary_accuracy: 0.9092" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2864/10000 [=======>......................] - ETA: 44s - loss: 0.1783 - binary_accuracy: 0.9093" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2873/10000 [=======>......................] - ETA: 44s - loss: 0.1782 - binary_accuracy: 0.9094" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2882/10000 [=======>......................] - ETA: 44s - loss: 0.1780 - binary_accuracy: 0.9095" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2891/10000 [=======>......................] - ETA: 44s - loss: 0.1778 - binary_accuracy: 0.9096" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2900/10000 [=======>......................] - ETA: 44s - loss: 0.1776 - binary_accuracy: 0.9097" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2909/10000 [=======>......................] - ETA: 43s - loss: 0.1775 - binary_accuracy: 0.9097" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2918/10000 [=======>......................] - ETA: 43s - loss: 0.1773 - binary_accuracy: 0.9099" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2926/10000 [=======>......................] - ETA: 43s - loss: 0.1772 - binary_accuracy: 0.9099" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2935/10000 [=======>......................] - ETA: 43s - loss: 0.1772 - binary_accuracy: 0.9099" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2944/10000 [=======>......................] - ETA: 43s - loss: 0.1770 - binary_accuracy: 0.9100" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2953/10000 [=======>......................] - ETA: 43s - loss: 0.1768 - binary_accuracy: 0.9101" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2962/10000 [=======>......................] - ETA: 43s - loss: 0.1767 - binary_accuracy: 0.9102" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2971/10000 [=======>......................] - ETA: 43s - loss: 0.1766 - binary_accuracy: 0.9103" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2980/10000 [=======>......................] - ETA: 43s - loss: 0.1764 - binary_accuracy: 0.9104" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2989/10000 [=======>......................] - ETA: 43s - loss: 0.1762 - binary_accuracy: 0.9105" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2998/10000 [=======>......................] - ETA: 43s - loss: 0.1761 - binary_accuracy: 0.9106" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3007/10000 [========>.....................] - ETA: 43s - loss: 0.1759 - binary_accuracy: 0.9107" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3016/10000 [========>.....................] - ETA: 43s - loss: 0.1758 - binary_accuracy: 0.9108" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3025/10000 [========>.....................] - ETA: 43s - loss: 0.1756 - binary_accuracy: 0.9109" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3034/10000 [========>.....................] - ETA: 43s - loss: 0.1754 - binary_accuracy: 0.9110" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3043/10000 [========>.....................] - ETA: 43s - loss: 0.1754 - binary_accuracy: 0.9111" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3052/10000 [========>.....................] - ETA: 43s - loss: 0.1752 - binary_accuracy: 0.9112" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3061/10000 [========>.....................] - ETA: 42s - loss: 0.1750 - binary_accuracy: 0.9113" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3070/10000 [========>.....................] - ETA: 42s - loss: 0.1748 - binary_accuracy: 0.9114" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3079/10000 [========>.....................] - ETA: 42s - loss: 0.1746 - binary_accuracy: 0.9116" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3088/10000 [========>.....................] - ETA: 42s - loss: 0.1744 - binary_accuracy: 0.9117" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3097/10000 [========>.....................] - ETA: 42s - loss: 0.1741 - binary_accuracy: 0.9119" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3106/10000 [========>.....................] - ETA: 42s - loss: 0.1741 - binary_accuracy: 0.9119" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3115/10000 [========>.....................] - ETA: 42s - loss: 0.1739 - binary_accuracy: 0.9120" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3124/10000 [========>.....................] - ETA: 42s - loss: 0.1737 - binary_accuracy: 0.9121" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3133/10000 [========>.....................] - ETA: 42s - loss: 0.1735 - binary_accuracy: 0.9122" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3142/10000 [========>.....................] - ETA: 42s - loss: 0.1734 - binary_accuracy: 0.9123" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3151/10000 [========>.....................] - ETA: 42s - loss: 0.1732 - binary_accuracy: 0.9123" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3160/10000 [========>.....................] - ETA: 42s - loss: 0.1730 - binary_accuracy: 0.9124" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3169/10000 [========>.....................] - ETA: 42s - loss: 0.1730 - binary_accuracy: 0.9125" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3178/10000 [========>.....................] - ETA: 42s - loss: 0.1728 - binary_accuracy: 0.9126" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3187/10000 [========>.....................] - ETA: 42s - loss: 0.1728 - binary_accuracy: 0.9126" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3196/10000 [========>.....................] - ETA: 42s - loss: 0.1727 - binary_accuracy: 0.9126" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3205/10000 [========>.....................] - ETA: 42s - loss: 0.1725 - binary_accuracy: 0.9127" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3214/10000 [========>.....................] - ETA: 41s - loss: 0.1724 - binary_accuracy: 0.9127" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3223/10000 [========>.....................] - ETA: 41s - loss: 0.1722 - binary_accuracy: 0.9129" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3232/10000 [========>.....................] - ETA: 41s - loss: 0.1720 - binary_accuracy: 0.9130" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3241/10000 [========>.....................] - ETA: 41s - loss: 0.1719 - binary_accuracy: 0.9131" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3250/10000 [========>.....................] - ETA: 41s - loss: 0.1717 - binary_accuracy: 0.9132" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3259/10000 [========>.....................] - ETA: 41s - loss: 0.1716 - binary_accuracy: 0.9133" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3268/10000 [========>.....................] - ETA: 41s - loss: 0.1715 - binary_accuracy: 0.9134" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3277/10000 [========>.....................] - ETA: 41s - loss: 0.1713 - binary_accuracy: 0.9135" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3286/10000 [========>.....................] - ETA: 41s - loss: 0.1712 - binary_accuracy: 0.9136" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3295/10000 [========>.....................] - ETA: 41s - loss: 0.1710 - binary_accuracy: 0.9137" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3304/10000 [========>.....................] - ETA: 41s - loss: 0.1708 - binary_accuracy: 0.9138" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3313/10000 [========>.....................] - ETA: 41s - loss: 0.1706 - binary_accuracy: 0.9140" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3322/10000 [========>.....................] - ETA: 41s - loss: 0.1704 - binary_accuracy: 0.9141" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3331/10000 [========>.....................] - ETA: 41s - loss: 0.1703 - binary_accuracy: 0.9142" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3340/10000 [=========>....................] - ETA: 41s - loss: 0.1701 - binary_accuracy: 0.9142" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3349/10000 [=========>....................] - ETA: 41s - loss: 0.1699 - binary_accuracy: 0.9144" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3358/10000 [=========>....................] - ETA: 41s - loss: 0.1698 - binary_accuracy: 0.9145" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3367/10000 [=========>....................] - ETA: 40s - loss: 0.1696 - binary_accuracy: 0.9146" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3376/10000 [=========>....................] - ETA: 40s - loss: 0.1694 - binary_accuracy: 0.9147" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3385/10000 [=========>....................] - ETA: 40s - loss: 0.1693 - binary_accuracy: 0.9147" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3394/10000 [=========>....................] - ETA: 40s - loss: 0.1692 - binary_accuracy: 0.9148" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3403/10000 [=========>....................] - ETA: 40s - loss: 0.1690 - binary_accuracy: 0.9150" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3412/10000 [=========>....................] - ETA: 40s - loss: 0.1687 - binary_accuracy: 0.9151" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3421/10000 [=========>....................] - ETA: 40s - loss: 0.1686 - binary_accuracy: 0.9152" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3430/10000 [=========>....................] - ETA: 40s - loss: 0.1685 - binary_accuracy: 0.9153" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3439/10000 [=========>....................] - ETA: 40s - loss: 0.1684 - binary_accuracy: 0.9153" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3448/10000 [=========>....................] - ETA: 40s - loss: 0.1681 - binary_accuracy: 0.9155" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3457/10000 [=========>....................] - ETA: 40s - loss: 0.1680 - binary_accuracy: 0.9156" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3466/10000 [=========>....................] - ETA: 40s - loss: 0.1678 - binary_accuracy: 0.9157" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3475/10000 [=========>....................] - ETA: 40s - loss: 0.1676 - binary_accuracy: 0.9158" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3484/10000 [=========>....................] - ETA: 40s - loss: 0.1675 - binary_accuracy: 0.9158" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3493/10000 [=========>....................] - ETA: 40s - loss: 0.1673 - binary_accuracy: 0.9159" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3502/10000 [=========>....................] - ETA: 40s - loss: 0.1672 - binary_accuracy: 0.9160" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3511/10000 [=========>....................] - ETA: 40s - loss: 0.1671 - binary_accuracy: 0.9161" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3520/10000 [=========>....................] - ETA: 39s - loss: 0.1669 - binary_accuracy: 0.9162" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3529/10000 [=========>....................] - ETA: 39s - loss: 0.1667 - binary_accuracy: 0.9163" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3538/10000 [=========>....................] - ETA: 39s - loss: 0.1666 - binary_accuracy: 0.9163" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3547/10000 [=========>....................] - ETA: 39s - loss: 0.1664 - binary_accuracy: 0.9164" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3556/10000 [=========>....................] - ETA: 39s - loss: 0.1662 - binary_accuracy: 0.9166" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3565/10000 [=========>....................] - ETA: 39s - loss: 0.1661 - binary_accuracy: 0.9167" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3574/10000 [=========>....................] - ETA: 39s - loss: 0.1659 - binary_accuracy: 0.9168" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3583/10000 [=========>....................] - ETA: 39s - loss: 0.1658 - binary_accuracy: 0.9169" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3592/10000 [=========>....................] - ETA: 39s - loss: 0.1656 - binary_accuracy: 0.9169" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3601/10000 [=========>....................] - ETA: 39s - loss: 0.1654 - binary_accuracy: 0.9171" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3610/10000 [=========>....................] - ETA: 39s - loss: 0.1652 - binary_accuracy: 0.9171" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3619/10000 [=========>....................] - ETA: 39s - loss: 0.1651 - binary_accuracy: 0.9173" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3628/10000 [=========>....................] - ETA: 39s - loss: 0.1649 - binary_accuracy: 0.9174" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3637/10000 [=========>....................] - ETA: 39s - loss: 0.1648 - binary_accuracy: 0.9174" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3646/10000 [=========>....................] - ETA: 39s - loss: 0.1646 - binary_accuracy: 0.9175" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3655/10000 [=========>....................] - ETA: 39s - loss: 0.1644 - binary_accuracy: 0.9176" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3664/10000 [=========>....................] - ETA: 39s - loss: 0.1642 - binary_accuracy: 0.9178" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3673/10000 [==========>...................] - ETA: 39s - loss: 0.1640 - binary_accuracy: 0.9179" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3682/10000 [==========>...................] - ETA: 38s - loss: 0.1638 - binary_accuracy: 0.9180" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3691/10000 [==========>...................] - ETA: 38s - loss: 0.1636 - binary_accuracy: 0.9181" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3700/10000 [==========>...................] - ETA: 38s - loss: 0.1634 - binary_accuracy: 0.9182" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3709/10000 [==========>...................] - ETA: 38s - loss: 0.1632 - binary_accuracy: 0.9183" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3718/10000 [==========>...................] - ETA: 38s - loss: 0.1631 - binary_accuracy: 0.9184" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3727/10000 [==========>...................] - ETA: 38s - loss: 0.1629 - binary_accuracy: 0.9185" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3736/10000 [==========>...................] - ETA: 38s - loss: 0.1628 - binary_accuracy: 0.9186" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3745/10000 [==========>...................] - ETA: 38s - loss: 0.1627 - binary_accuracy: 0.9186" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3754/10000 [==========>...................] - ETA: 38s - loss: 0.1626 - binary_accuracy: 0.9187" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3763/10000 [==========>...................] - ETA: 38s - loss: 0.1625 - binary_accuracy: 0.9187" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3772/10000 [==========>...................] - ETA: 38s - loss: 0.1622 - binary_accuracy: 0.9189" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3781/10000 [==========>...................] - ETA: 38s - loss: 0.1620 - binary_accuracy: 0.9190" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3790/10000 [==========>...................] - ETA: 38s - loss: 0.1618 - binary_accuracy: 0.9191" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3799/10000 [==========>...................] - ETA: 38s - loss: 0.1616 - binary_accuracy: 0.9192" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3808/10000 [==========>...................] - ETA: 38s - loss: 0.1614 - binary_accuracy: 0.9193" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3817/10000 [==========>...................] - ETA: 38s - loss: 0.1612 - binary_accuracy: 0.9194" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3826/10000 [==========>...................] - ETA: 38s - loss: 0.1610 - binary_accuracy: 0.9195" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3835/10000 [==========>...................] - ETA: 37s - loss: 0.1608 - binary_accuracy: 0.9196" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3844/10000 [==========>...................] - ETA: 37s - loss: 0.1607 - binary_accuracy: 0.9197" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3853/10000 [==========>...................] - ETA: 37s - loss: 0.1604 - binary_accuracy: 0.9199" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3862/10000 [==========>...................] - ETA: 37s - loss: 0.1603 - binary_accuracy: 0.9199" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3871/10000 [==========>...................] - ETA: 37s - loss: 0.1602 - binary_accuracy: 0.9200" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3880/10000 [==========>...................] - ETA: 37s - loss: 0.1600 - binary_accuracy: 0.9201" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3889/10000 [==========>...................] - ETA: 37s - loss: 0.1598 - binary_accuracy: 0.9202" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3898/10000 [==========>...................] - ETA: 37s - loss: 0.1597 - binary_accuracy: 0.9203" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3907/10000 [==========>...................] - ETA: 37s - loss: 0.1595 - binary_accuracy: 0.9204" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3916/10000 [==========>...................] - ETA: 37s - loss: 0.1593 - binary_accuracy: 0.9205" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3925/10000 [==========>...................] - ETA: 37s - loss: 0.1591 - binary_accuracy: 0.9206" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3934/10000 [==========>...................] - ETA: 37s - loss: 0.1589 - binary_accuracy: 0.9207" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3943/10000 [==========>...................] - ETA: 37s - loss: 0.1588 - binary_accuracy: 0.9208" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3952/10000 [==========>...................] - ETA: 37s - loss: 0.1587 - binary_accuracy: 0.9209" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3961/10000 [==========>...................] - ETA: 37s - loss: 0.1586 - binary_accuracy: 0.9209" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3970/10000 [==========>...................] - ETA: 37s - loss: 0.1585 - binary_accuracy: 0.9209" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3979/10000 [==========>...................] - ETA: 37s - loss: 0.1583 - binary_accuracy: 0.9210" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3988/10000 [==========>...................] - ETA: 36s - loss: 0.1581 - binary_accuracy: 0.9212" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3997/10000 [==========>...................] - ETA: 36s - loss: 0.1580 - binary_accuracy: 0.9213" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4006/10000 [===========>..................] - ETA: 36s - loss: 0.1579 - binary_accuracy: 0.9213" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4015/10000 [===========>..................] - ETA: 36s - loss: 0.1577 - binary_accuracy: 0.9215" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4024/10000 [===========>..................] - ETA: 36s - loss: 0.1575 - binary_accuracy: 0.9215" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4033/10000 [===========>..................] - ETA: 36s - loss: 0.1573 - binary_accuracy: 0.9216" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4042/10000 [===========>..................] - ETA: 36s - loss: 0.1571 - binary_accuracy: 0.9218" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4051/10000 [===========>..................] - ETA: 36s - loss: 0.1569 - binary_accuracy: 0.9219" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4060/10000 [===========>..................] - ETA: 36s - loss: 0.1567 - binary_accuracy: 0.9220" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4069/10000 [===========>..................] - ETA: 36s - loss: 0.1565 - binary_accuracy: 0.9221" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4078/10000 [===========>..................] - ETA: 36s - loss: 0.1563 - binary_accuracy: 0.9222" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4087/10000 [===========>..................] - ETA: 36s - loss: 0.1561 - binary_accuracy: 0.9223" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4096/10000 [===========>..................] - ETA: 36s - loss: 0.1560 - binary_accuracy: 0.9224" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4105/10000 [===========>..................] - ETA: 36s - loss: 0.1557 - binary_accuracy: 0.9225" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4114/10000 [===========>..................] - ETA: 36s - loss: 0.1556 - binary_accuracy: 0.9226" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4123/10000 [===========>..................] - ETA: 36s - loss: 0.1554 - binary_accuracy: 0.9227" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4132/10000 [===========>..................] - ETA: 36s - loss: 0.1552 - binary_accuracy: 0.9228" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4141/10000 [===========>..................] - ETA: 36s - loss: 0.1551 - binary_accuracy: 0.9228" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4150/10000 [===========>..................] - ETA: 35s - loss: 0.1549 - binary_accuracy: 0.9229" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4159/10000 [===========>..................] - ETA: 35s - loss: 0.1548 - binary_accuracy: 0.9230" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4168/10000 [===========>..................] - ETA: 35s - loss: 0.1546 - binary_accuracy: 0.9231" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4177/10000 [===========>..................] - ETA: 35s - loss: 0.1545 - binary_accuracy: 0.9232" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4186/10000 [===========>..................] - ETA: 35s - loss: 0.1543 - binary_accuracy: 0.9233" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4195/10000 [===========>..................] - ETA: 35s - loss: 0.1541 - binary_accuracy: 0.9234" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4204/10000 [===========>..................] - ETA: 35s - loss: 0.1539 - binary_accuracy: 0.9235" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4213/10000 [===========>..................] - ETA: 35s - loss: 0.1538 - binary_accuracy: 0.9236" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4222/10000 [===========>..................] - ETA: 35s - loss: 0.1536 - binary_accuracy: 0.9237" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4231/10000 [===========>..................] - ETA: 35s - loss: 0.1534 - binary_accuracy: 0.9238" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4240/10000 [===========>..................] - ETA: 35s - loss: 0.1533 - binary_accuracy: 0.9239" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4249/10000 [===========>..................] - ETA: 35s - loss: 0.1531 - binary_accuracy: 0.9240" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4258/10000 [===========>..................] - ETA: 35s - loss: 0.1530 - binary_accuracy: 0.9240" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4267/10000 [===========>..................] - ETA: 35s - loss: 0.1528 - binary_accuracy: 0.9241" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4276/10000 [===========>..................] - ETA: 35s - loss: 0.1526 - binary_accuracy: 0.9242" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4285/10000 [===========>..................] - ETA: 35s - loss: 0.1524 - binary_accuracy: 0.9243" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4294/10000 [===========>..................] - ETA: 35s - loss: 0.1523 - binary_accuracy: 0.9244" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4303/10000 [===========>..................] - ETA: 34s - loss: 0.1522 - binary_accuracy: 0.9244" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4312/10000 [===========>..................] - ETA: 34s - loss: 0.1521 - binary_accuracy: 0.9245" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4321/10000 [===========>..................] - ETA: 34s - loss: 0.1519 - binary_accuracy: 0.9246" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4330/10000 [===========>..................] - ETA: 34s - loss: 0.1518 - binary_accuracy: 0.9247" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4339/10000 [============>.................] - ETA: 34s - loss: 0.1516 - binary_accuracy: 0.9248" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4348/10000 [============>.................] - ETA: 34s - loss: 0.1514 - binary_accuracy: 0.9249" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4357/10000 [============>.................] - ETA: 34s - loss: 0.1513 - binary_accuracy: 0.9250" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4366/10000 [============>.................] - ETA: 34s - loss: 0.1511 - binary_accuracy: 0.9251" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4375/10000 [============>.................] - ETA: 34s - loss: 0.1509 - binary_accuracy: 0.9252" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4384/10000 [============>.................] - ETA: 34s - loss: 0.1507 - binary_accuracy: 0.9253" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4393/10000 [============>.................] - ETA: 34s - loss: 0.1506 - binary_accuracy: 0.9254" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4402/10000 [============>.................] - ETA: 34s - loss: 0.1504 - binary_accuracy: 0.9254" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4411/10000 [============>.................] - ETA: 34s - loss: 0.1503 - binary_accuracy: 0.9255" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4420/10000 [============>.................] - ETA: 34s - loss: 0.1502 - binary_accuracy: 0.9255" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4429/10000 [============>.................] - ETA: 34s - loss: 0.1500 - binary_accuracy: 0.9256" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4438/10000 [============>.................] - ETA: 34s - loss: 0.1498 - binary_accuracy: 0.9257" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4447/10000 [============>.................] - ETA: 34s - loss: 0.1497 - binary_accuracy: 0.9258" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4456/10000 [============>.................] - ETA: 34s - loss: 0.1495 - binary_accuracy: 0.9259" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4465/10000 [============>.................] - ETA: 33s - loss: 0.1493 - binary_accuracy: 0.9260" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4474/10000 [============>.................] - ETA: 33s - loss: 0.1491 - binary_accuracy: 0.9261" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4483/10000 [============>.................] - ETA: 33s - loss: 0.1490 - binary_accuracy: 0.9261" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4492/10000 [============>.................] - ETA: 33s - loss: 0.1488 - binary_accuracy: 0.9262" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4501/10000 [============>.................] - ETA: 33s - loss: 0.1487 - binary_accuracy: 0.9263" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4510/10000 [============>.................] - ETA: 33s - loss: 0.1485 - binary_accuracy: 0.9264" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4519/10000 [============>.................] - ETA: 33s - loss: 0.1485 - binary_accuracy: 0.9264" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4528/10000 [============>.................] - ETA: 33s - loss: 0.1484 - binary_accuracy: 0.9265" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4537/10000 [============>.................] - ETA: 33s - loss: 0.1484 - binary_accuracy: 0.9265" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4546/10000 [============>.................] - ETA: 33s - loss: 0.1482 - binary_accuracy: 0.9266" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4555/10000 [============>.................] - ETA: 33s - loss: 0.1481 - binary_accuracy: 0.9267" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4564/10000 [============>.................] - ETA: 33s - loss: 0.1481 - binary_accuracy: 0.9267" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4572/10000 [============>.................] - ETA: 33s - loss: 0.1479 - binary_accuracy: 0.9268" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4581/10000 [============>.................] - ETA: 33s - loss: 0.1478 - binary_accuracy: 0.9269" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4590/10000 [============>.................] - ETA: 33s - loss: 0.1476 - binary_accuracy: 0.9270" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4599/10000 [============>.................] - ETA: 33s - loss: 0.1475 - binary_accuracy: 0.9270" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4608/10000 [============>.................] - ETA: 33s - loss: 0.1473 - binary_accuracy: 0.9271" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4617/10000 [============>.................] - ETA: 33s - loss: 0.1472 - binary_accuracy: 0.9272" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4626/10000 [============>.................] - ETA: 32s - loss: 0.1470 - binary_accuracy: 0.9273" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4635/10000 [============>.................] - ETA: 32s - loss: 0.1469 - binary_accuracy: 0.9274" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4643/10000 [============>.................] - ETA: 32s - loss: 0.1467 - binary_accuracy: 0.9275" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4651/10000 [============>.................] - ETA: 32s - loss: 0.1466 - binary_accuracy: 0.9276" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4659/10000 [============>.................] - ETA: 32s - loss: 0.1466 - binary_accuracy: 0.9276" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4668/10000 [=============>................] - ETA: 32s - loss: 0.1465 - binary_accuracy: 0.9277" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4676/10000 [=============>................] - ETA: 32s - loss: 0.1463 - binary_accuracy: 0.9278" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4684/10000 [=============>................] - ETA: 32s - loss: 0.1462 - binary_accuracy: 0.9279" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4693/10000 [=============>................] - ETA: 32s - loss: 0.1460 - binary_accuracy: 0.9280" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4702/10000 [=============>................] - ETA: 32s - loss: 0.1459 - binary_accuracy: 0.9280" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4711/10000 [=============>................] - ETA: 32s - loss: 0.1457 - binary_accuracy: 0.9281" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4720/10000 [=============>................] - ETA: 32s - loss: 0.1455 - binary_accuracy: 0.9282" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4728/10000 [=============>................] - ETA: 32s - loss: 0.1453 - binary_accuracy: 0.9283" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4737/10000 [=============>................] - ETA: 32s - loss: 0.1452 - binary_accuracy: 0.9284" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4746/10000 [=============>................] - ETA: 32s - loss: 0.1451 - binary_accuracy: 0.9284" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4755/10000 [=============>................] - ETA: 32s - loss: 0.1449 - binary_accuracy: 0.9285" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4764/10000 [=============>................] - ETA: 32s - loss: 0.1448 - binary_accuracy: 0.9286" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4772/10000 [=============>................] - ETA: 32s - loss: 0.1446 - binary_accuracy: 0.9287" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4780/10000 [=============>................] - ETA: 32s - loss: 0.1444 - binary_accuracy: 0.9288" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4788/10000 [=============>................] - ETA: 31s - loss: 0.1443 - binary_accuracy: 0.9289" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4796/10000 [=============>................] - ETA: 31s - loss: 0.1441 - binary_accuracy: 0.9290" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4805/10000 [=============>................] - ETA: 31s - loss: 0.1439 - binary_accuracy: 0.9291" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4814/10000 [=============>................] - ETA: 31s - loss: 0.1437 - binary_accuracy: 0.9291" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4823/10000 [=============>................] - ETA: 31s - loss: 0.1436 - binary_accuracy: 0.9293" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4832/10000 [=============>................] - ETA: 31s - loss: 0.1434 - binary_accuracy: 0.9293" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4841/10000 [=============>................] - ETA: 31s - loss: 0.1432 - binary_accuracy: 0.9294" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4849/10000 [=============>................] - ETA: 31s - loss: 0.1430 - binary_accuracy: 0.9295" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4858/10000 [=============>................] - ETA: 31s - loss: 0.1428 - binary_accuracy: 0.9296" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4867/10000 [=============>................] - ETA: 31s - loss: 0.1426 - binary_accuracy: 0.9297" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4876/10000 [=============>................] - ETA: 31s - loss: 0.1425 - binary_accuracy: 0.9298" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4885/10000 [=============>................] - ETA: 31s - loss: 0.1423 - binary_accuracy: 0.9299" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4894/10000 [=============>................] - ETA: 31s - loss: 0.1422 - binary_accuracy: 0.9300" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4903/10000 [=============>................] - ETA: 31s - loss: 0.1421 - binary_accuracy: 0.9301" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4912/10000 [=============>................] - ETA: 31s - loss: 0.1419 - binary_accuracy: 0.9302" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4921/10000 [=============>................] - ETA: 31s - loss: 0.1417 - binary_accuracy: 0.9302" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4930/10000 [=============>................] - ETA: 31s - loss: 0.1416 - binary_accuracy: 0.9303" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4939/10000 [=============>................] - ETA: 31s - loss: 0.1414 - binary_accuracy: 0.9304" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4948/10000 [=============>................] - ETA: 31s - loss: 0.1413 - binary_accuracy: 0.9305" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4957/10000 [=============>................] - ETA: 30s - loss: 0.1411 - binary_accuracy: 0.9306" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4966/10000 [=============>................] - ETA: 30s - loss: 0.1410 - binary_accuracy: 0.9306" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4975/10000 [=============>................] - ETA: 30s - loss: 0.1408 - binary_accuracy: 0.9307" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4984/10000 [=============>................] - ETA: 30s - loss: 0.1407 - binary_accuracy: 0.9308" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4993/10000 [=============>................] - ETA: 30s - loss: 0.1405 - binary_accuracy: 0.9309" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5002/10000 [==============>...............] - ETA: 30s - loss: 0.1404 - binary_accuracy: 0.9310" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5011/10000 [==============>...............] - ETA: 30s - loss: 0.1402 - binary_accuracy: 0.9311" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5019/10000 [==============>...............] - ETA: 30s - loss: 0.1401 - binary_accuracy: 0.9311" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5028/10000 [==============>...............] - ETA: 30s - loss: 0.1399 - binary_accuracy: 0.9312" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5037/10000 [==============>...............] - ETA: 30s - loss: 0.1397 - binary_accuracy: 0.9313" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5046/10000 [==============>...............] - ETA: 30s - loss: 0.1396 - binary_accuracy: 0.9314" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5054/10000 [==============>...............] - ETA: 30s - loss: 0.1394 - binary_accuracy: 0.9315" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5063/10000 [==============>...............] - ETA: 30s - loss: 0.1393 - binary_accuracy: 0.9316" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5072/10000 [==============>...............] - ETA: 30s - loss: 0.1392 - binary_accuracy: 0.9316" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5081/10000 [==============>...............] - ETA: 30s - loss: 0.1390 - binary_accuracy: 0.9317" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5090/10000 [==============>...............] - ETA: 30s - loss: 0.1388 - binary_accuracy: 0.9318" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5099/10000 [==============>...............] - ETA: 30s - loss: 0.1386 - binary_accuracy: 0.9319" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5108/10000 [==============>...............] - ETA: 30s - loss: 0.1385 - binary_accuracy: 0.9320" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5117/10000 [==============>...............] - ETA: 29s - loss: 0.1383 - binary_accuracy: 0.9321" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5126/10000 [==============>...............] - ETA: 29s - loss: 0.1382 - binary_accuracy: 0.9322" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5135/10000 [==============>...............] - ETA: 29s - loss: 0.1380 - binary_accuracy: 0.9323" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5143/10000 [==============>...............] - ETA: 29s - loss: 0.1378 - binary_accuracy: 0.9324" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5152/10000 [==============>...............] - ETA: 29s - loss: 0.1376 - binary_accuracy: 0.9324" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5161/10000 [==============>...............] - ETA: 29s - loss: 0.1375 - binary_accuracy: 0.9325" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5170/10000 [==============>...............] - ETA: 29s - loss: 0.1373 - binary_accuracy: 0.9326" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5179/10000 [==============>...............] - ETA: 29s - loss: 0.1371 - binary_accuracy: 0.9327" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5188/10000 [==============>...............] - ETA: 29s - loss: 0.1370 - binary_accuracy: 0.9328" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5197/10000 [==============>...............] - ETA: 29s - loss: 0.1368 - binary_accuracy: 0.9329" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5206/10000 [==============>...............] - ETA: 29s - loss: 0.1367 - binary_accuracy: 0.9330" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5215/10000 [==============>...............] - ETA: 29s - loss: 0.1365 - binary_accuracy: 0.9330" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5224/10000 [==============>...............] - ETA: 29s - loss: 0.1363 - binary_accuracy: 0.9331" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5233/10000 [==============>...............] - ETA: 29s - loss: 0.1362 - binary_accuracy: 0.9332" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5242/10000 [==============>...............] - ETA: 29s - loss: 0.1361 - binary_accuracy: 0.9333" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5251/10000 [==============>...............] - ETA: 29s - loss: 0.1359 - binary_accuracy: 0.9334" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5259/10000 [==============>...............] - ETA: 29s - loss: 0.1358 - binary_accuracy: 0.9334" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5268/10000 [==============>...............] - ETA: 29s - loss: 0.1356 - binary_accuracy: 0.9335" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5277/10000 [==============>...............] - ETA: 29s - loss: 0.1355 - binary_accuracy: 0.9336" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5286/10000 [==============>...............] - ETA: 28s - loss: 0.1353 - binary_accuracy: 0.9337" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5295/10000 [==============>...............] - ETA: 28s - loss: 0.1351 - binary_accuracy: 0.9338" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5303/10000 [==============>...............] - ETA: 28s - loss: 0.1350 - binary_accuracy: 0.9339" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5311/10000 [==============>...............] - ETA: 28s - loss: 0.1348 - binary_accuracy: 0.9339" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5320/10000 [==============>...............] - ETA: 28s - loss: 0.1346 - binary_accuracy: 0.9340" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5329/10000 [==============>...............] - ETA: 28s - loss: 0.1345 - binary_accuracy: 0.9341" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5338/10000 [===============>..............] - ETA: 28s - loss: 0.1344 - binary_accuracy: 0.9342" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5347/10000 [===============>..............] - ETA: 28s - loss: 0.1342 - binary_accuracy: 0.9343" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5356/10000 [===============>..............] - ETA: 28s - loss: 0.1341 - binary_accuracy: 0.9343" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5364/10000 [===============>..............] - ETA: 28s - loss: 0.1340 - binary_accuracy: 0.9344" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5373/10000 [===============>..............] - ETA: 28s - loss: 0.1339 - binary_accuracy: 0.9344" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5382/10000 [===============>..............] - ETA: 28s - loss: 0.1337 - binary_accuracy: 0.9345" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5391/10000 [===============>..............] - ETA: 28s - loss: 0.1335 - binary_accuracy: 0.9346" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5400/10000 [===============>..............] - ETA: 28s - loss: 0.1334 - binary_accuracy: 0.9347" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5409/10000 [===============>..............] - ETA: 28s - loss: 0.1332 - binary_accuracy: 0.9348" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5418/10000 [===============>..............] - ETA: 28s - loss: 0.1331 - binary_accuracy: 0.9348" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5427/10000 [===============>..............] - ETA: 28s - loss: 0.1330 - binary_accuracy: 0.9349" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5436/10000 [===============>..............] - ETA: 28s - loss: 0.1329 - binary_accuracy: 0.9350" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5444/10000 [===============>..............] - ETA: 27s - loss: 0.1328 - binary_accuracy: 0.9350" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5453/10000 [===============>..............] - ETA: 27s - loss: 0.1326 - binary_accuracy: 0.9351" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5462/10000 [===============>..............] - ETA: 27s - loss: 0.1325 - binary_accuracy: 0.9352" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5471/10000 [===============>..............] - ETA: 27s - loss: 0.1324 - binary_accuracy: 0.9352" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5480/10000 [===============>..............] - ETA: 27s - loss: 0.1323 - binary_accuracy: 0.9353" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5489/10000 [===============>..............] - ETA: 27s - loss: 0.1322 - binary_accuracy: 0.9354" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5498/10000 [===============>..............] - ETA: 27s - loss: 0.1321 - binary_accuracy: 0.9354" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5506/10000 [===============>..............] - ETA: 27s - loss: 0.1320 - binary_accuracy: 0.9354" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5515/10000 [===============>..............] - ETA: 27s - loss: 0.1319 - binary_accuracy: 0.9355" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5524/10000 [===============>..............] - ETA: 27s - loss: 0.1318 - binary_accuracy: 0.9356" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5533/10000 [===============>..............] - ETA: 27s - loss: 0.1316 - binary_accuracy: 0.9356" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5542/10000 [===============>..............] - ETA: 27s - loss: 0.1315 - binary_accuracy: 0.9357" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5551/10000 [===============>..............] - ETA: 27s - loss: 0.1313 - binary_accuracy: 0.9358" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5560/10000 [===============>..............] - ETA: 27s - loss: 0.1312 - binary_accuracy: 0.9359" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5569/10000 [===============>..............] - ETA: 27s - loss: 0.1310 - binary_accuracy: 0.9360" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5578/10000 [===============>..............] - ETA: 27s - loss: 0.1309 - binary_accuracy: 0.9360" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5587/10000 [===============>..............] - ETA: 27s - loss: 0.1307 - binary_accuracy: 0.9361" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5596/10000 [===============>..............] - ETA: 27s - loss: 0.1306 - binary_accuracy: 0.9362" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5605/10000 [===============>..............] - ETA: 26s - loss: 0.1304 - binary_accuracy: 0.9362" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5614/10000 [===============>..............] - ETA: 26s - loss: 0.1303 - binary_accuracy: 0.9363" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5623/10000 [===============>..............] - ETA: 26s - loss: 0.1302 - binary_accuracy: 0.9364" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5632/10000 [===============>..............] - ETA: 26s - loss: 0.1300 - binary_accuracy: 0.9364" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5641/10000 [===============>..............] - ETA: 26s - loss: 0.1299 - binary_accuracy: 0.9365" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5650/10000 [===============>..............] - ETA: 26s - loss: 0.1297 - binary_accuracy: 0.9366" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5658/10000 [===============>..............] - ETA: 26s - loss: 0.1296 - binary_accuracy: 0.9367" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5667/10000 [================>.............] - ETA: 26s - loss: 0.1294 - binary_accuracy: 0.9367" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5676/10000 [================>.............] - ETA: 26s - loss: 0.1293 - binary_accuracy: 0.9368" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5685/10000 [================>.............] - ETA: 26s - loss: 0.1292 - binary_accuracy: 0.9369" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5694/10000 [================>.............] - ETA: 26s - loss: 0.1290 - binary_accuracy: 0.9369" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5703/10000 [================>.............] - ETA: 26s - loss: 0.1289 - binary_accuracy: 0.9370" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5711/10000 [================>.............] - ETA: 26s - loss: 0.1288 - binary_accuracy: 0.9371" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5720/10000 [================>.............] - ETA: 26s - loss: 0.1287 - binary_accuracy: 0.9371" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5728/10000 [================>.............] - ETA: 26s - loss: 0.1286 - binary_accuracy: 0.9372" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5737/10000 [================>.............] - ETA: 26s - loss: 0.1284 - binary_accuracy: 0.9373" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5746/10000 [================>.............] - ETA: 26s - loss: 0.1283 - binary_accuracy: 0.9373" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5754/10000 [================>.............] - ETA: 26s - loss: 0.1282 - binary_accuracy: 0.9374" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5762/10000 [================>.............] - ETA: 26s - loss: 0.1281 - binary_accuracy: 0.9374" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5771/10000 [================>.............] - ETA: 25s - loss: 0.1280 - binary_accuracy: 0.9375" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5780/10000 [================>.............] - ETA: 25s - loss: 0.1278 - binary_accuracy: 0.9376" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5788/10000 [================>.............] - ETA: 25s - loss: 0.1277 - binary_accuracy: 0.9377" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5797/10000 [================>.............] - ETA: 25s - loss: 0.1276 - binary_accuracy: 0.9377" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5805/10000 [================>.............] - ETA: 25s - loss: 0.1274 - binary_accuracy: 0.9378" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5814/10000 [================>.............] - ETA: 25s - loss: 0.1273 - binary_accuracy: 0.9379" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5823/10000 [================>.............] - ETA: 25s - loss: 0.1272 - binary_accuracy: 0.9380" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5832/10000 [================>.............] - ETA: 25s - loss: 0.1270 - binary_accuracy: 0.9380" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5841/10000 [================>.............] - ETA: 25s - loss: 0.1268 - binary_accuracy: 0.9381" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5850/10000 [================>.............] - ETA: 25s - loss: 0.1267 - binary_accuracy: 0.9382" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5859/10000 [================>.............] - ETA: 25s - loss: 0.1265 - binary_accuracy: 0.9383" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5867/10000 [================>.............] - ETA: 25s - loss: 0.1264 - binary_accuracy: 0.9383" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5876/10000 [================>.............] - ETA: 25s - loss: 0.1262 - binary_accuracy: 0.9384" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5884/10000 [================>.............] - ETA: 25s - loss: 0.1261 - binary_accuracy: 0.9385" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5893/10000 [================>.............] - ETA: 25s - loss: 0.1260 - binary_accuracy: 0.9386" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5902/10000 [================>.............] - ETA: 25s - loss: 0.1258 - binary_accuracy: 0.9386" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5911/10000 [================>.............] - ETA: 25s - loss: 0.1257 - binary_accuracy: 0.9387" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5920/10000 [================>.............] - ETA: 25s - loss: 0.1256 - binary_accuracy: 0.9388" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5929/10000 [================>.............] - ETA: 25s - loss: 0.1254 - binary_accuracy: 0.9388" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5938/10000 [================>.............] - ETA: 24s - loss: 0.1253 - binary_accuracy: 0.9389" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5947/10000 [================>.............] - ETA: 24s - loss: 0.1251 - binary_accuracy: 0.9390" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5956/10000 [================>.............] - ETA: 24s - loss: 0.1250 - binary_accuracy: 0.9391" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5965/10000 [================>.............] - ETA: 24s - loss: 0.1249 - binary_accuracy: 0.9391" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5974/10000 [================>.............] - ETA: 24s - loss: 0.1247 - binary_accuracy: 0.9392" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5983/10000 [================>.............] - ETA: 24s - loss: 0.1246 - binary_accuracy: 0.9393" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 5992/10000 [================>.............] - ETA: 24s - loss: 0.1245 - binary_accuracy: 0.9393" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6001/10000 [=================>............] - ETA: 24s - loss: 0.1243 - binary_accuracy: 0.9394" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6010/10000 [=================>............] - ETA: 24s - loss: 0.1242 - binary_accuracy: 0.9395" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6019/10000 [=================>............] - ETA: 24s - loss: 0.1240 - binary_accuracy: 0.9395" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6028/10000 [=================>............] - ETA: 24s - loss: 0.1239 - binary_accuracy: 0.9396" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6037/10000 [=================>............] - ETA: 24s - loss: 0.1237 - binary_accuracy: 0.9397" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6046/10000 [=================>............] - ETA: 24s - loss: 0.1236 - binary_accuracy: 0.9398" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6055/10000 [=================>............] - ETA: 24s - loss: 0.1234 - binary_accuracy: 0.9398" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6064/10000 [=================>............] - ETA: 24s - loss: 0.1233 - binary_accuracy: 0.9399" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6073/10000 [=================>............] - ETA: 24s - loss: 0.1231 - binary_accuracy: 0.9400" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6082/10000 [=================>............] - ETA: 24s - loss: 0.1230 - binary_accuracy: 0.9401" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6091/10000 [=================>............] - ETA: 23s - loss: 0.1228 - binary_accuracy: 0.9401" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6100/10000 [=================>............] - ETA: 23s - loss: 0.1227 - binary_accuracy: 0.9402" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6109/10000 [=================>............] - ETA: 23s - loss: 0.1226 - binary_accuracy: 0.9403" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6118/10000 [=================>............] - ETA: 23s - loss: 0.1224 - binary_accuracy: 0.9404" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6127/10000 [=================>............] - ETA: 23s - loss: 0.1222 - binary_accuracy: 0.9404" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6136/10000 [=================>............] - ETA: 23s - loss: 0.1221 - binary_accuracy: 0.9405" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6145/10000 [=================>............] - ETA: 23s - loss: 0.1219 - binary_accuracy: 0.9406" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6154/10000 [=================>............] - ETA: 23s - loss: 0.1218 - binary_accuracy: 0.9407" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6163/10000 [=================>............] - ETA: 23s - loss: 0.1216 - binary_accuracy: 0.9407" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6172/10000 [=================>............] - ETA: 23s - loss: 0.1215 - binary_accuracy: 0.9408" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6181/10000 [=================>............] - ETA: 23s - loss: 0.1214 - binary_accuracy: 0.9409" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6190/10000 [=================>............] - ETA: 23s - loss: 0.1212 - binary_accuracy: 0.9409" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6199/10000 [=================>............] - ETA: 23s - loss: 0.1211 - binary_accuracy: 0.9410" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6208/10000 [=================>............] - ETA: 23s - loss: 0.1210 - binary_accuracy: 0.9411" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6217/10000 [=================>............] - ETA: 23s - loss: 0.1209 - binary_accuracy: 0.9411" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6226/10000 [=================>............] - ETA: 23s - loss: 0.1208 - binary_accuracy: 0.9412" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6235/10000 [=================>............] - ETA: 23s - loss: 0.1207 - binary_accuracy: 0.9412" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6244/10000 [=================>............] - ETA: 23s - loss: 0.1206 - binary_accuracy: 0.9413" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6253/10000 [=================>............] - ETA: 22s - loss: 0.1205 - binary_accuracy: 0.9413" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6262/10000 [=================>............] - ETA: 22s - loss: 0.1204 - binary_accuracy: 0.9414" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6271/10000 [=================>............] - ETA: 22s - loss: 0.1203 - binary_accuracy: 0.9414" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6280/10000 [=================>............] - ETA: 22s - loss: 0.1202 - binary_accuracy: 0.9415" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6289/10000 [=================>............] - ETA: 22s - loss: 0.1201 - binary_accuracy: 0.9416" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6298/10000 [=================>............] - ETA: 22s - loss: 0.1199 - binary_accuracy: 0.9416" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6307/10000 [=================>............] - ETA: 22s - loss: 0.1198 - binary_accuracy: 0.9417" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6316/10000 [=================>............] - ETA: 22s - loss: 0.1196 - binary_accuracy: 0.9418" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6325/10000 [=================>............] - ETA: 22s - loss: 0.1195 - binary_accuracy: 0.9418" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6334/10000 [==================>...........] - ETA: 22s - loss: 0.1194 - binary_accuracy: 0.9419" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6343/10000 [==================>...........] - ETA: 22s - loss: 0.1192 - binary_accuracy: 0.9420" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6352/10000 [==================>...........] - ETA: 22s - loss: 0.1191 - binary_accuracy: 0.9420" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6360/10000 [==================>...........] - ETA: 22s - loss: 0.1190 - binary_accuracy: 0.9421" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6368/10000 [==================>...........] - ETA: 22s - loss: 0.1189 - binary_accuracy: 0.9422" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6377/10000 [==================>...........] - ETA: 22s - loss: 0.1188 - binary_accuracy: 0.9422" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6386/10000 [==================>...........] - ETA: 22s - loss: 0.1186 - binary_accuracy: 0.9423" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6395/10000 [==================>...........] - ETA: 22s - loss: 0.1185 - binary_accuracy: 0.9424" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6404/10000 [==================>...........] - ETA: 22s - loss: 0.1184 - binary_accuracy: 0.9424" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6412/10000 [==================>...........] - ETA: 22s - loss: 0.1183 - binary_accuracy: 0.9425" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6420/10000 [==================>...........] - ETA: 21s - loss: 0.1183 - binary_accuracy: 0.9425" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6428/10000 [==================>...........] - ETA: 21s - loss: 0.1182 - binary_accuracy: 0.9426" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6437/10000 [==================>...........] - ETA: 21s - loss: 0.1181 - binary_accuracy: 0.9426" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6445/10000 [==================>...........] - ETA: 21s - loss: 0.1180 - binary_accuracy: 0.9427" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6453/10000 [==================>...........] - ETA: 21s - loss: 0.1179 - binary_accuracy: 0.9427" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6461/10000 [==================>...........] - ETA: 21s - loss: 0.1178 - binary_accuracy: 0.9428" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6470/10000 [==================>...........] - ETA: 21s - loss: 0.1177 - binary_accuracy: 0.9428" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6478/10000 [==================>...........] - ETA: 21s - loss: 0.1176 - binary_accuracy: 0.9429" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6486/10000 [==================>...........] - ETA: 21s - loss: 0.1175 - binary_accuracy: 0.9429" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6495/10000 [==================>...........] - ETA: 21s - loss: 0.1174 - binary_accuracy: 0.9430" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6504/10000 [==================>...........] - ETA: 21s - loss: 0.1172 - binary_accuracy: 0.9430" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6513/10000 [==================>...........] - ETA: 21s - loss: 0.1171 - binary_accuracy: 0.9431" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6522/10000 [==================>...........] - ETA: 21s - loss: 0.1170 - binary_accuracy: 0.9432" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6531/10000 [==================>...........] - ETA: 21s - loss: 0.1168 - binary_accuracy: 0.9432" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6540/10000 [==================>...........] - ETA: 21s - loss: 0.1167 - binary_accuracy: 0.9433" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6549/10000 [==================>...........] - ETA: 21s - loss: 0.1166 - binary_accuracy: 0.9434" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6557/10000 [==================>...........] - ETA: 21s - loss: 0.1165 - binary_accuracy: 0.9434" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6566/10000 [==================>...........] - ETA: 21s - loss: 0.1163 - binary_accuracy: 0.9435" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6574/10000 [==================>...........] - ETA: 21s - loss: 0.1162 - binary_accuracy: 0.9436" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6583/10000 [==================>...........] - ETA: 20s - loss: 0.1161 - binary_accuracy: 0.9436" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6592/10000 [==================>...........] - ETA: 20s - loss: 0.1159 - binary_accuracy: 0.9437" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6601/10000 [==================>...........] - ETA: 20s - loss: 0.1158 - binary_accuracy: 0.9438" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6610/10000 [==================>...........] - ETA: 20s - loss: 0.1157 - binary_accuracy: 0.9438" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6619/10000 [==================>...........] - ETA: 20s - loss: 0.1155 - binary_accuracy: 0.9439" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6627/10000 [==================>...........] - ETA: 20s - loss: 0.1154 - binary_accuracy: 0.9440" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6636/10000 [==================>...........] - ETA: 20s - loss: 0.1153 - binary_accuracy: 0.9440" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6645/10000 [==================>...........] - ETA: 20s - loss: 0.1152 - binary_accuracy: 0.9441" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6654/10000 [==================>...........] - ETA: 20s - loss: 0.1150 - binary_accuracy: 0.9442" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6663/10000 [==================>...........] - ETA: 20s - loss: 0.1149 - binary_accuracy: 0.9442" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6672/10000 [===================>..........] - ETA: 20s - loss: 0.1148 - binary_accuracy: 0.9443" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6681/10000 [===================>..........] - ETA: 20s - loss: 0.1147 - binary_accuracy: 0.9443" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6690/10000 [===================>..........] - ETA: 20s - loss: 0.1146 - binary_accuracy: 0.9444" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6699/10000 [===================>..........] - ETA: 20s - loss: 0.1145 - binary_accuracy: 0.9444" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6708/10000 [===================>..........] - ETA: 20s - loss: 0.1144 - binary_accuracy: 0.9445" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6717/10000 [===================>..........] - ETA: 20s - loss: 0.1143 - binary_accuracy: 0.9445" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6726/10000 [===================>..........] - ETA: 20s - loss: 0.1142 - binary_accuracy: 0.9446" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6735/10000 [===================>..........] - ETA: 20s - loss: 0.1140 - binary_accuracy: 0.9446" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6744/10000 [===================>..........] - ETA: 19s - loss: 0.1139 - binary_accuracy: 0.9447" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6753/10000 [===================>..........] - ETA: 19s - loss: 0.1138 - binary_accuracy: 0.9448" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6762/10000 [===================>..........] - ETA: 19s - loss: 0.1137 - binary_accuracy: 0.9448" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6771/10000 [===================>..........] - ETA: 19s - loss: 0.1136 - binary_accuracy: 0.9449" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6780/10000 [===================>..........] - ETA: 19s - loss: 0.1134 - binary_accuracy: 0.9450" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6789/10000 [===================>..........] - ETA: 19s - loss: 0.1133 - binary_accuracy: 0.9450" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6798/10000 [===================>..........] - ETA: 19s - loss: 0.1132 - binary_accuracy: 0.9451" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6807/10000 [===================>..........] - ETA: 19s - loss: 0.1130 - binary_accuracy: 0.9452" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6816/10000 [===================>..........] - ETA: 19s - loss: 0.1129 - binary_accuracy: 0.9452" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6825/10000 [===================>..........] - ETA: 19s - loss: 0.1128 - binary_accuracy: 0.9453" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6834/10000 [===================>..........] - ETA: 19s - loss: 0.1126 - binary_accuracy: 0.9454" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6843/10000 [===================>..........] - ETA: 19s - loss: 0.1125 - binary_accuracy: 0.9454" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6852/10000 [===================>..........] - ETA: 19s - loss: 0.1124 - binary_accuracy: 0.9455" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6860/10000 [===================>..........] - ETA: 19s - loss: 0.1123 - binary_accuracy: 0.9455" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6869/10000 [===================>..........] - ETA: 19s - loss: 0.1122 - binary_accuracy: 0.9456" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6878/10000 [===================>..........] - ETA: 19s - loss: 0.1121 - binary_accuracy: 0.9457" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6887/10000 [===================>..........] - ETA: 19s - loss: 0.1120 - binary_accuracy: 0.9457" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6896/10000 [===================>..........] - ETA: 19s - loss: 0.1118 - binary_accuracy: 0.9458" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6905/10000 [===================>..........] - ETA: 19s - loss: 0.1117 - binary_accuracy: 0.9458" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6914/10000 [===================>..........] - ETA: 18s - loss: 0.1116 - binary_accuracy: 0.9459" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6923/10000 [===================>..........] - ETA: 18s - loss: 0.1115 - binary_accuracy: 0.9459" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6932/10000 [===================>..........] - ETA: 18s - loss: 0.1114 - binary_accuracy: 0.9460" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6941/10000 [===================>..........] - ETA: 18s - loss: 0.1113 - binary_accuracy: 0.9461" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6950/10000 [===================>..........] - ETA: 18s - loss: 0.1112 - binary_accuracy: 0.9461" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6958/10000 [===================>..........] - ETA: 18s - loss: 0.1111 - binary_accuracy: 0.9462" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6966/10000 [===================>..........] - ETA: 18s - loss: 0.1110 - binary_accuracy: 0.9462" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6974/10000 [===================>..........] - ETA: 18s - loss: 0.1109 - binary_accuracy: 0.9463" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6982/10000 [===================>..........] - ETA: 18s - loss: 0.1108 - binary_accuracy: 0.9463" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6990/10000 [===================>..........] - ETA: 18s - loss: 0.1107 - binary_accuracy: 0.9464" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 6998/10000 [===================>..........] - ETA: 18s - loss: 0.1106 - binary_accuracy: 0.9464" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7006/10000 [====================>.........] - ETA: 18s - loss: 0.1105 - binary_accuracy: 0.9465" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7014/10000 [====================>.........] - ETA: 18s - loss: 0.1104 - binary_accuracy: 0.9465" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7022/10000 [====================>.........] - ETA: 18s - loss: 0.1103 - binary_accuracy: 0.9466" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7030/10000 [====================>.........] - ETA: 18s - loss: 0.1101 - binary_accuracy: 0.9466" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7038/10000 [====================>.........] - ETA: 18s - loss: 0.1100 - binary_accuracy: 0.9467" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7047/10000 [====================>.........] - ETA: 18s - loss: 0.1099 - binary_accuracy: 0.9467" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7055/10000 [====================>.........] - ETA: 18s - loss: 0.1098 - binary_accuracy: 0.9468" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7063/10000 [====================>.........] - ETA: 18s - loss: 0.1097 - binary_accuracy: 0.9468" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7071/10000 [====================>.........] - ETA: 17s - loss: 0.1096 - binary_accuracy: 0.9469" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7079/10000 [====================>.........] - ETA: 17s - loss: 0.1095 - binary_accuracy: 0.9469" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7087/10000 [====================>.........] - ETA: 17s - loss: 0.1094 - binary_accuracy: 0.9470" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7095/10000 [====================>.........] - ETA: 17s - loss: 0.1094 - binary_accuracy: 0.9470" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7104/10000 [====================>.........] - ETA: 17s - loss: 0.1093 - binary_accuracy: 0.9471" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7112/10000 [====================>.........] - ETA: 17s - loss: 0.1092 - binary_accuracy: 0.9471" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7121/10000 [====================>.........] - ETA: 17s - loss: 0.1091 - binary_accuracy: 0.9472" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7130/10000 [====================>.........] - ETA: 17s - loss: 0.1090 - binary_accuracy: 0.9473" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7139/10000 [====================>.........] - ETA: 17s - loss: 0.1088 - binary_accuracy: 0.9473" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7147/10000 [====================>.........] - ETA: 17s - loss: 0.1088 - binary_accuracy: 0.9474" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7156/10000 [====================>.........] - ETA: 17s - loss: 0.1087 - binary_accuracy: 0.9474" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7165/10000 [====================>.........] - ETA: 17s - loss: 0.1085 - binary_accuracy: 0.9475" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7174/10000 [====================>.........] - ETA: 17s - loss: 0.1084 - binary_accuracy: 0.9475" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7183/10000 [====================>.........] - ETA: 17s - loss: 0.1083 - binary_accuracy: 0.9476" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7192/10000 [====================>.........] - ETA: 17s - loss: 0.1082 - binary_accuracy: 0.9476" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7200/10000 [====================>.........] - ETA: 17s - loss: 0.1081 - binary_accuracy: 0.9477" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7209/10000 [====================>.........] - ETA: 17s - loss: 0.1081 - binary_accuracy: 0.9477" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7217/10000 [====================>.........] - ETA: 17s - loss: 0.1080 - binary_accuracy: 0.9478" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7226/10000 [====================>.........] - ETA: 17s - loss: 0.1079 - binary_accuracy: 0.9478" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7235/10000 [====================>.........] - ETA: 16s - loss: 0.1078 - binary_accuracy: 0.9479" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7244/10000 [====================>.........] - ETA: 16s - loss: 0.1077 - binary_accuracy: 0.9479" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7253/10000 [====================>.........] - ETA: 16s - loss: 0.1076 - binary_accuracy: 0.9480" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7262/10000 [====================>.........] - ETA: 16s - loss: 0.1075 - binary_accuracy: 0.9480" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7271/10000 [====================>.........] - ETA: 16s - loss: 0.1074 - binary_accuracy: 0.9481" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7279/10000 [====================>.........] - ETA: 16s - loss: 0.1073 - binary_accuracy: 0.9481" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7287/10000 [====================>.........] - ETA: 16s - loss: 0.1072 - binary_accuracy: 0.9482" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7295/10000 [====================>.........] - ETA: 16s - loss: 0.1071 - binary_accuracy: 0.9482" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7303/10000 [====================>.........] - ETA: 16s - loss: 0.1070 - binary_accuracy: 0.9483" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7311/10000 [====================>.........] - ETA: 16s - loss: 0.1069 - binary_accuracy: 0.9483" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7320/10000 [====================>.........] - ETA: 16s - loss: 0.1067 - binary_accuracy: 0.9484" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7329/10000 [====================>.........] - ETA: 16s - loss: 0.1066 - binary_accuracy: 0.9485" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7337/10000 [=====================>........] - ETA: 16s - loss: 0.1065 - binary_accuracy: 0.9485" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7345/10000 [=====================>........] - ETA: 16s - loss: 0.1064 - binary_accuracy: 0.9486" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7354/10000 [=====================>........] - ETA: 16s - loss: 0.1063 - binary_accuracy: 0.9486" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7362/10000 [=====================>........] - ETA: 16s - loss: 0.1062 - binary_accuracy: 0.9487" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7371/10000 [=====================>........] - ETA: 16s - loss: 0.1061 - binary_accuracy: 0.9487" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7379/10000 [=====================>........] - ETA: 16s - loss: 0.1060 - binary_accuracy: 0.9487" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7387/10000 [=====================>........] - ETA: 16s - loss: 0.1059 - binary_accuracy: 0.9488" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7396/10000 [=====================>........] - ETA: 16s - loss: 0.1058 - binary_accuracy: 0.9489" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7404/10000 [=====================>........] - ETA: 15s - loss: 0.1057 - binary_accuracy: 0.9489" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7412/10000 [=====================>........] - ETA: 15s - loss: 0.1056 - binary_accuracy: 0.9490" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7420/10000 [=====================>........] - ETA: 15s - loss: 0.1055 - binary_accuracy: 0.9490" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7429/10000 [=====================>........] - ETA: 15s - loss: 0.1054 - binary_accuracy: 0.9491" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7437/10000 [=====================>........] - ETA: 15s - loss: 0.1053 - binary_accuracy: 0.9491" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7445/10000 [=====================>........] - ETA: 15s - loss: 0.1052 - binary_accuracy: 0.9492" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7454/10000 [=====================>........] - ETA: 15s - loss: 0.1051 - binary_accuracy: 0.9492" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7463/10000 [=====================>........] - ETA: 15s - loss: 0.1050 - binary_accuracy: 0.9493" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7472/10000 [=====================>........] - ETA: 15s - loss: 0.1049 - binary_accuracy: 0.9493" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7481/10000 [=====================>........] - ETA: 15s - loss: 0.1048 - binary_accuracy: 0.9494" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7489/10000 [=====================>........] - ETA: 15s - loss: 0.1047 - binary_accuracy: 0.9494" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7498/10000 [=====================>........] - ETA: 15s - loss: 0.1046 - binary_accuracy: 0.9495" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7507/10000 [=====================>........] - ETA: 15s - loss: 0.1045 - binary_accuracy: 0.9495" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7516/10000 [=====================>........] - ETA: 15s - loss: 0.1044 - binary_accuracy: 0.9496" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7525/10000 [=====================>........] - ETA: 15s - loss: 0.1042 - binary_accuracy: 0.9496" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7534/10000 [=====================>........] - ETA: 15s - loss: 0.1041 - binary_accuracy: 0.9497" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7543/10000 [=====================>........] - ETA: 15s - loss: 0.1040 - binary_accuracy: 0.9498" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7552/10000 [=====================>........] - ETA: 15s - loss: 0.1039 - binary_accuracy: 0.9498" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7561/10000 [=====================>........] - ETA: 14s - loss: 0.1038 - binary_accuracy: 0.9499" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7570/10000 [=====================>........] - ETA: 14s - loss: 0.1037 - binary_accuracy: 0.9499" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7579/10000 [=====================>........] - ETA: 14s - loss: 0.1036 - binary_accuracy: 0.9500" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7588/10000 [=====================>........] - ETA: 14s - loss: 0.1035 - binary_accuracy: 0.9500" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7597/10000 [=====================>........] - ETA: 14s - loss: 0.1033 - binary_accuracy: 0.9501" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7606/10000 [=====================>........] - ETA: 14s - loss: 0.1032 - binary_accuracy: 0.9501" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7615/10000 [=====================>........] - ETA: 14s - loss: 0.1031 - binary_accuracy: 0.9502" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7624/10000 [=====================>........] - ETA: 14s - loss: 0.1030 - binary_accuracy: 0.9503" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7632/10000 [=====================>........] - ETA: 14s - loss: 0.1029 - binary_accuracy: 0.9503" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7641/10000 [=====================>........] - ETA: 14s - loss: 0.1028 - binary_accuracy: 0.9503" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7649/10000 [=====================>........] - ETA: 14s - loss: 0.1027 - binary_accuracy: 0.9504" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7658/10000 [=====================>........] - ETA: 14s - loss: 0.1026 - binary_accuracy: 0.9505" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7666/10000 [=====================>........] - ETA: 14s - loss: 0.1025 - binary_accuracy: 0.9505" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7674/10000 [======================>.......] - ETA: 14s - loss: 0.1024 - binary_accuracy: 0.9505" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7683/10000 [======================>.......] - ETA: 14s - loss: 0.1023 - binary_accuracy: 0.9506" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7692/10000 [======================>.......] - ETA: 14s - loss: 0.1022 - binary_accuracy: 0.9507" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7700/10000 [======================>.......] - ETA: 14s - loss: 0.1021 - binary_accuracy: 0.9507" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7709/10000 [======================>.......] - ETA: 14s - loss: 0.1020 - binary_accuracy: 0.9507" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7718/10000 [======================>.......] - ETA: 14s - loss: 0.1019 - binary_accuracy: 0.9508" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7727/10000 [======================>.......] - ETA: 13s - loss: 0.1018 - binary_accuracy: 0.9508" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7736/10000 [======================>.......] - ETA: 13s - loss: 0.1017 - binary_accuracy: 0.9509" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7745/10000 [======================>.......] - ETA: 13s - loss: 0.1016 - binary_accuracy: 0.9509" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7754/10000 [======================>.......] - ETA: 13s - loss: 0.1015 - binary_accuracy: 0.9510" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7763/10000 [======================>.......] - ETA: 13s - loss: 0.1014 - binary_accuracy: 0.9510" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7771/10000 [======================>.......] - ETA: 13s - loss: 0.1013 - binary_accuracy: 0.9511" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7779/10000 [======================>.......] - ETA: 13s - loss: 0.1012 - binary_accuracy: 0.9511" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7788/10000 [======================>.......] - ETA: 13s - loss: 0.1011 - binary_accuracy: 0.9512" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7797/10000 [======================>.......] - ETA: 13s - loss: 0.1010 - binary_accuracy: 0.9512" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7806/10000 [======================>.......] - ETA: 13s - loss: 0.1009 - binary_accuracy: 0.9513" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7814/10000 [======================>.......] - ETA: 13s - loss: 0.1008 - binary_accuracy: 0.9513" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7823/10000 [======================>.......] - ETA: 13s - loss: 0.1007 - binary_accuracy: 0.9514" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7832/10000 [======================>.......] - ETA: 13s - loss: 0.1006 - binary_accuracy: 0.9514" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7841/10000 [======================>.......] - ETA: 13s - loss: 0.1006 - binary_accuracy: 0.9515" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7850/10000 [======================>.......] - ETA: 13s - loss: 0.1005 - binary_accuracy: 0.9515" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7859/10000 [======================>.......] - ETA: 13s - loss: 0.1004 - binary_accuracy: 0.9516" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7868/10000 [======================>.......] - ETA: 13s - loss: 0.1003 - binary_accuracy: 0.9516" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7877/10000 [======================>.......] - ETA: 13s - loss: 0.1002 - binary_accuracy: 0.9517" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7886/10000 [======================>.......] - ETA: 12s - loss: 0.1001 - binary_accuracy: 0.9517" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7895/10000 [======================>.......] - ETA: 12s - loss: 0.1000 - binary_accuracy: 0.9518" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7904/10000 [======================>.......] - ETA: 12s - loss: 0.0999 - binary_accuracy: 0.9518" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7913/10000 [======================>.......] - ETA: 12s - loss: 0.0998 - binary_accuracy: 0.9519" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7922/10000 [======================>.......] - ETA: 12s - loss: 0.0997 - binary_accuracy: 0.9519" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7930/10000 [======================>.......] - ETA: 12s - loss: 0.0996 - binary_accuracy: 0.9520" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7939/10000 [======================>.......] - ETA: 12s - loss: 0.0995 - binary_accuracy: 0.9520" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7948/10000 [======================>.......] - ETA: 12s - loss: 0.0994 - binary_accuracy: 0.9520" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7957/10000 [======================>.......] - ETA: 12s - loss: 0.0993 - binary_accuracy: 0.9521" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7966/10000 [======================>.......] - ETA: 12s - loss: 0.0992 - binary_accuracy: 0.9521" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7974/10000 [======================>.......] - ETA: 12s - loss: 0.0992 - binary_accuracy: 0.9522" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7982/10000 [======================>.......] - ETA: 12s - loss: 0.0991 - binary_accuracy: 0.9522" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7991/10000 [======================>.......] - ETA: 12s - loss: 0.0990 - binary_accuracy: 0.9523" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7999/10000 [======================>.......] - ETA: 12s - loss: 0.0989 - binary_accuracy: 0.9523" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8008/10000 [=======================>......] - ETA: 12s - loss: 0.0988 - binary_accuracy: 0.9524" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8017/10000 [=======================>......] - ETA: 12s - loss: 0.0988 - binary_accuracy: 0.9524" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8026/10000 [=======================>......] - ETA: 12s - loss: 0.0987 - binary_accuracy: 0.9524" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8035/10000 [=======================>......] - ETA: 12s - loss: 0.0986 - binary_accuracy: 0.9525" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8043/10000 [=======================>......] - ETA: 12s - loss: 0.0985 - binary_accuracy: 0.9525" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8052/10000 [=======================>......] - ETA: 11s - loss: 0.0985 - binary_accuracy: 0.9525" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8060/10000 [=======================>......] - ETA: 11s - loss: 0.0984 - binary_accuracy: 0.9526" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8068/10000 [=======================>......] - ETA: 11s - loss: 0.0983 - binary_accuracy: 0.9526" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8076/10000 [=======================>......] - ETA: 11s - loss: 0.0983 - binary_accuracy: 0.9526" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8084/10000 [=======================>......] - ETA: 11s - loss: 0.0982 - binary_accuracy: 0.9527" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8092/10000 [=======================>......] - ETA: 11s - loss: 0.0981 - binary_accuracy: 0.9527" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8100/10000 [=======================>......] - ETA: 11s - loss: 0.0980 - binary_accuracy: 0.9528" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8109/10000 [=======================>......] - ETA: 11s - loss: 0.0979 - binary_accuracy: 0.9528" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8118/10000 [=======================>......] - ETA: 11s - loss: 0.0979 - binary_accuracy: 0.9528" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8127/10000 [=======================>......] - ETA: 11s - loss: 0.0978 - binary_accuracy: 0.9529" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8135/10000 [=======================>......] - ETA: 11s - loss: 0.0977 - binary_accuracy: 0.9529" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8143/10000 [=======================>......] - ETA: 11s - loss: 0.0976 - binary_accuracy: 0.9530" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8151/10000 [=======================>......] - ETA: 11s - loss: 0.0976 - binary_accuracy: 0.9530" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8159/10000 [=======================>......] - ETA: 11s - loss: 0.0975 - binary_accuracy: 0.9530" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8167/10000 [=======================>......] - ETA: 11s - loss: 0.0974 - binary_accuracy: 0.9531" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8176/10000 [=======================>......] - ETA: 11s - loss: 0.0973 - binary_accuracy: 0.9531" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8185/10000 [=======================>......] - ETA: 11s - loss: 0.0972 - binary_accuracy: 0.9532" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8194/10000 [=======================>......] - ETA: 11s - loss: 0.0972 - binary_accuracy: 0.9532" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8203/10000 [=======================>......] - ETA: 11s - loss: 0.0971 - binary_accuracy: 0.9533" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8212/10000 [=======================>......] - ETA: 10s - loss: 0.0970 - binary_accuracy: 0.9533" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8221/10000 [=======================>......] - ETA: 10s - loss: 0.0969 - binary_accuracy: 0.9533" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8230/10000 [=======================>......] - ETA: 10s - loss: 0.0968 - binary_accuracy: 0.9534" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8239/10000 [=======================>......] - ETA: 10s - loss: 0.0968 - binary_accuracy: 0.9534" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8248/10000 [=======================>......] - ETA: 10s - loss: 0.0967 - binary_accuracy: 0.9534" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8256/10000 [=======================>......] - ETA: 10s - loss: 0.0966 - binary_accuracy: 0.9535" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8264/10000 [=======================>......] - ETA: 10s - loss: 0.0966 - binary_accuracy: 0.9535" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8273/10000 [=======================>......] - ETA: 10s - loss: 0.0965 - binary_accuracy: 0.9535" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8282/10000 [=======================>......] - ETA: 10s - loss: 0.0964 - binary_accuracy: 0.9536" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8291/10000 [=======================>......] - ETA: 10s - loss: 0.0963 - binary_accuracy: 0.9536" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8300/10000 [=======================>......] - ETA: 10s - loss: 0.0962 - binary_accuracy: 0.9537" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8309/10000 [=======================>......] - ETA: 10s - loss: 0.0961 - binary_accuracy: 0.9537" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8318/10000 [=======================>......] - ETA: 10s - loss: 0.0961 - binary_accuracy: 0.9538" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8327/10000 [=======================>......] - ETA: 10s - loss: 0.0960 - binary_accuracy: 0.9538" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8336/10000 [========================>.....] - ETA: 10s - loss: 0.0959 - binary_accuracy: 0.9538" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8345/10000 [========================>.....] - ETA: 10s - loss: 0.0958 - binary_accuracy: 0.9539" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8354/10000 [========================>.....] - ETA: 10s - loss: 0.0957 - binary_accuracy: 0.9539" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8363/10000 [========================>.....] - ETA: 10s - loss: 0.0956 - binary_accuracy: 0.9540" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8372/10000 [========================>.....] - ETA: 10s - loss: 0.0955 - binary_accuracy: 0.9540" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8381/10000 [========================>.....] - ETA: 9s - loss: 0.0954 - binary_accuracy: 0.9541 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8390/10000 [========================>.....] - ETA: 9s - loss: 0.0953 - binary_accuracy: 0.9541" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8398/10000 [========================>.....] - ETA: 9s - loss: 0.0952 - binary_accuracy: 0.9542" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8406/10000 [========================>.....] - ETA: 9s - loss: 0.0952 - binary_accuracy: 0.9542" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8415/10000 [========================>.....] - ETA: 9s - loss: 0.0951 - binary_accuracy: 0.9543" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8424/10000 [========================>.....] - ETA: 9s - loss: 0.0950 - binary_accuracy: 0.9543" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8433/10000 [========================>.....] - ETA: 9s - loss: 0.0949 - binary_accuracy: 0.9543" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8442/10000 [========================>.....] - ETA: 9s - loss: 0.0948 - binary_accuracy: 0.9544" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8451/10000 [========================>.....] - ETA: 9s - loss: 0.0947 - binary_accuracy: 0.9544" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8460/10000 [========================>.....] - ETA: 9s - loss: 0.0946 - binary_accuracy: 0.9545" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8469/10000 [========================>.....] - ETA: 9s - loss: 0.0946 - binary_accuracy: 0.9545" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8477/10000 [========================>.....] - ETA: 9s - loss: 0.0945 - binary_accuracy: 0.9546" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8486/10000 [========================>.....] - ETA: 9s - loss: 0.0944 - binary_accuracy: 0.9546" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8495/10000 [========================>.....] - ETA: 9s - loss: 0.0943 - binary_accuracy: 0.9546" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8504/10000 [========================>.....] - ETA: 9s - loss: 0.0943 - binary_accuracy: 0.9547" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8513/10000 [========================>.....] - ETA: 9s - loss: 0.0942 - binary_accuracy: 0.9547" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8522/10000 [========================>.....] - ETA: 9s - loss: 0.0941 - binary_accuracy: 0.9547" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8531/10000 [========================>.....] - ETA: 9s - loss: 0.0941 - binary_accuracy: 0.9548" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8539/10000 [========================>.....] - ETA: 8s - loss: 0.0940 - binary_accuracy: 0.9548" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8547/10000 [========================>.....] - ETA: 8s - loss: 0.0940 - binary_accuracy: 0.9548" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8555/10000 [========================>.....] - ETA: 8s - loss: 0.0939 - binary_accuracy: 0.9549" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8564/10000 [========================>.....] - ETA: 8s - loss: 0.0938 - binary_accuracy: 0.9549" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8573/10000 [========================>.....] - ETA: 8s - loss: 0.0938 - binary_accuracy: 0.9549" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8582/10000 [========================>.....] - ETA: 8s - loss: 0.0937 - binary_accuracy: 0.9550" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8590/10000 [========================>.....] - ETA: 8s - loss: 0.0937 - binary_accuracy: 0.9550" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8598/10000 [========================>.....] - ETA: 8s - loss: 0.0936 - binary_accuracy: 0.9550" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8607/10000 [========================>.....] - ETA: 8s - loss: 0.0936 - binary_accuracy: 0.9551" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8616/10000 [========================>.....] - ETA: 8s - loss: 0.0935 - binary_accuracy: 0.9551" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8625/10000 [========================>.....] - ETA: 8s - loss: 0.0934 - binary_accuracy: 0.9551" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8633/10000 [========================>.....] - ETA: 8s - loss: 0.0934 - binary_accuracy: 0.9552" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8642/10000 [========================>.....] - ETA: 8s - loss: 0.0933 - binary_accuracy: 0.9552" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8651/10000 [========================>.....] - ETA: 8s - loss: 0.0932 - binary_accuracy: 0.9552" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8660/10000 [========================>.....] - ETA: 8s - loss: 0.0932 - binary_accuracy: 0.9553" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8669/10000 [=========================>....] - ETA: 8s - loss: 0.0931 - binary_accuracy: 0.9553" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8678/10000 [=========================>....] - ETA: 8s - loss: 0.0930 - binary_accuracy: 0.9553" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8687/10000 [=========================>....] - ETA: 8s - loss: 0.0929 - binary_accuracy: 0.9554" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8696/10000 [=========================>....] - ETA: 8s - loss: 0.0929 - binary_accuracy: 0.9554" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8705/10000 [=========================>....] - ETA: 7s - loss: 0.0928 - binary_accuracy: 0.9555" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8714/10000 [=========================>....] - ETA: 7s - loss: 0.0927 - binary_accuracy: 0.9555" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8723/10000 [=========================>....] - ETA: 7s - loss: 0.0926 - binary_accuracy: 0.9555" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8731/10000 [=========================>....] - ETA: 7s - loss: 0.0925 - binary_accuracy: 0.9556" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8740/10000 [=========================>....] - ETA: 7s - loss: 0.0925 - binary_accuracy: 0.9556" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8749/10000 [=========================>....] - ETA: 7s - loss: 0.0924 - binary_accuracy: 0.9556" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8758/10000 [=========================>....] - ETA: 7s - loss: 0.0923 - binary_accuracy: 0.9557" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8767/10000 [=========================>....] - ETA: 7s - loss: 0.0922 - binary_accuracy: 0.9557" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8776/10000 [=========================>....] - ETA: 7s - loss: 0.0921 - binary_accuracy: 0.9558" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8784/10000 [=========================>....] - ETA: 7s - loss: 0.0921 - binary_accuracy: 0.9558" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8793/10000 [=========================>....] - ETA: 7s - loss: 0.0920 - binary_accuracy: 0.9559" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8802/10000 [=========================>....] - ETA: 7s - loss: 0.0919 - binary_accuracy: 0.9559" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8811/10000 [=========================>....] - ETA: 7s - loss: 0.0918 - binary_accuracy: 0.9559" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8820/10000 [=========================>....] - ETA: 7s - loss: 0.0917 - binary_accuracy: 0.9560" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8828/10000 [=========================>....] - ETA: 7s - loss: 0.0916 - binary_accuracy: 0.9560" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8836/10000 [=========================>....] - ETA: 7s - loss: 0.0916 - binary_accuracy: 0.9561" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8845/10000 [=========================>....] - ETA: 7s - loss: 0.0915 - binary_accuracy: 0.9561" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8853/10000 [=========================>....] - ETA: 7s - loss: 0.0914 - binary_accuracy: 0.9561" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8862/10000 [=========================>....] - ETA: 6s - loss: 0.0913 - binary_accuracy: 0.9562" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8871/10000 [=========================>....] - ETA: 6s - loss: 0.0912 - binary_accuracy: 0.9562" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8880/10000 [=========================>....] - ETA: 6s - loss: 0.0911 - binary_accuracy: 0.9563" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8889/10000 [=========================>....] - ETA: 6s - loss: 0.0911 - binary_accuracy: 0.9563" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8898/10000 [=========================>....] - ETA: 6s - loss: 0.0910 - binary_accuracy: 0.9563" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8907/10000 [=========================>....] - ETA: 6s - loss: 0.0909 - binary_accuracy: 0.9564" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8915/10000 [=========================>....] - ETA: 6s - loss: 0.0908 - binary_accuracy: 0.9564" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8923/10000 [=========================>....] - ETA: 6s - loss: 0.0908 - binary_accuracy: 0.9565" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8931/10000 [=========================>....] - ETA: 6s - loss: 0.0907 - binary_accuracy: 0.9565" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8939/10000 [=========================>....] - ETA: 6s - loss: 0.0906 - binary_accuracy: 0.9565" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8947/10000 [=========================>....] - ETA: 6s - loss: 0.0905 - binary_accuracy: 0.9566" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8955/10000 [=========================>....] - ETA: 6s - loss: 0.0905 - binary_accuracy: 0.9566" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8964/10000 [=========================>....] - ETA: 6s - loss: 0.0904 - binary_accuracy: 0.9567" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8973/10000 [=========================>....] - ETA: 6s - loss: 0.0903 - binary_accuracy: 0.9567" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8982/10000 [=========================>....] - ETA: 6s - loss: 0.0902 - binary_accuracy: 0.9567" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8991/10000 [=========================>....] - ETA: 6s - loss: 0.0901 - binary_accuracy: 0.9568" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9000/10000 [==========================>...] - ETA: 6s - loss: 0.0900 - binary_accuracy: 0.9568" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9009/10000 [==========================>...] - ETA: 6s - loss: 0.0900 - binary_accuracy: 0.9569" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9018/10000 [==========================>...] - ETA: 6s - loss: 0.0899 - binary_accuracy: 0.9569" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9027/10000 [==========================>...] - ETA: 5s - loss: 0.0898 - binary_accuracy: 0.9569" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9036/10000 [==========================>...] - ETA: 5s - loss: 0.0897 - binary_accuracy: 0.9570" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9044/10000 [==========================>...] - ETA: 5s - loss: 0.0897 - binary_accuracy: 0.9570" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9052/10000 [==========================>...] - ETA: 5s - loss: 0.0896 - binary_accuracy: 0.9570" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9061/10000 [==========================>...] - ETA: 5s - loss: 0.0895 - binary_accuracy: 0.9571" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9070/10000 [==========================>...] - ETA: 5s - loss: 0.0894 - binary_accuracy: 0.9571" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9079/10000 [==========================>...] - ETA: 5s - loss: 0.0894 - binary_accuracy: 0.9571" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9088/10000 [==========================>...] - ETA: 5s - loss: 0.0893 - binary_accuracy: 0.9572" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9097/10000 [==========================>...] - ETA: 5s - loss: 0.0892 - binary_accuracy: 0.9572" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9105/10000 [==========================>...] - ETA: 5s - loss: 0.0891 - binary_accuracy: 0.9573" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9113/10000 [==========================>...] - ETA: 5s - loss: 0.0891 - binary_accuracy: 0.9573" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9122/10000 [==========================>...] - ETA: 5s - loss: 0.0890 - binary_accuracy: 0.9573" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9131/10000 [==========================>...] - ETA: 5s - loss: 0.0889 - binary_accuracy: 0.9574" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9139/10000 [==========================>...] - ETA: 5s - loss: 0.0888 - binary_accuracy: 0.9574" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9148/10000 [==========================>...] - ETA: 5s - loss: 0.0887 - binary_accuracy: 0.9575" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9157/10000 [==========================>...] - ETA: 5s - loss: 0.0887 - binary_accuracy: 0.9575" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9165/10000 [==========================>...] - ETA: 5s - loss: 0.0886 - binary_accuracy: 0.9575" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9173/10000 [==========================>...] - ETA: 5s - loss: 0.0885 - binary_accuracy: 0.9576" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9181/10000 [==========================>...] - ETA: 5s - loss: 0.0885 - binary_accuracy: 0.9576" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9190/10000 [==========================>...] - ETA: 4s - loss: 0.0884 - binary_accuracy: 0.9576" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9198/10000 [==========================>...] - ETA: 4s - loss: 0.0883 - binary_accuracy: 0.9577" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9206/10000 [==========================>...] - ETA: 4s - loss: 0.0883 - binary_accuracy: 0.9577" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9215/10000 [==========================>...] - ETA: 4s - loss: 0.0882 - binary_accuracy: 0.9577" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9224/10000 [==========================>...] - ETA: 4s - loss: 0.0881 - binary_accuracy: 0.9578" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9233/10000 [==========================>...] - ETA: 4s - loss: 0.0880 - binary_accuracy: 0.9578" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9241/10000 [==========================>...] - ETA: 4s - loss: 0.0880 - binary_accuracy: 0.9579" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9249/10000 [==========================>...] - ETA: 4s - loss: 0.0879 - binary_accuracy: 0.9579" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9257/10000 [==========================>...] - ETA: 4s - loss: 0.0878 - binary_accuracy: 0.9579" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9266/10000 [==========================>...] - ETA: 4s - loss: 0.0878 - binary_accuracy: 0.9580" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9275/10000 [==========================>...] - ETA: 4s - loss: 0.0877 - binary_accuracy: 0.9580" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9283/10000 [==========================>...] - ETA: 4s - loss: 0.0876 - binary_accuracy: 0.9580" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9292/10000 [==========================>...] - ETA: 4s - loss: 0.0875 - binary_accuracy: 0.9581" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9300/10000 [==========================>...] - ETA: 4s - loss: 0.0875 - binary_accuracy: 0.9581" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9308/10000 [==========================>...] - ETA: 4s - loss: 0.0874 - binary_accuracy: 0.9581" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9316/10000 [==========================>...] - ETA: 4s - loss: 0.0873 - binary_accuracy: 0.9582" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9324/10000 [==========================>...] - ETA: 4s - loss: 0.0873 - binary_accuracy: 0.9582" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9333/10000 [==========================>...] - ETA: 4s - loss: 0.0872 - binary_accuracy: 0.9582" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9341/10000 [===========================>..] - ETA: 4s - loss: 0.0871 - binary_accuracy: 0.9583" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9350/10000 [===========================>..] - ETA: 4s - loss: 0.0871 - binary_accuracy: 0.9583" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9358/10000 [===========================>..] - ETA: 3s - loss: 0.0870 - binary_accuracy: 0.9583" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9366/10000 [===========================>..] - ETA: 3s - loss: 0.0869 - binary_accuracy: 0.9584" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9375/10000 [===========================>..] - ETA: 3s - loss: 0.0869 - binary_accuracy: 0.9584" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9384/10000 [===========================>..] - ETA: 3s - loss: 0.0868 - binary_accuracy: 0.9584" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9393/10000 [===========================>..] - ETA: 3s - loss: 0.0867 - binary_accuracy: 0.9585" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9402/10000 [===========================>..] - ETA: 3s - loss: 0.0867 - binary_accuracy: 0.9585" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9410/10000 [===========================>..] - ETA: 3s - loss: 0.0866 - binary_accuracy: 0.9585" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9418/10000 [===========================>..] - ETA: 3s - loss: 0.0866 - binary_accuracy: 0.9585" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9426/10000 [===========================>..] - ETA: 3s - loss: 0.0865 - binary_accuracy: 0.9586" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9434/10000 [===========================>..] - ETA: 3s - loss: 0.0865 - binary_accuracy: 0.9586" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9442/10000 [===========================>..] - ETA: 3s - loss: 0.0865 - binary_accuracy: 0.9586" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9450/10000 [===========================>..] - ETA: 3s - loss: 0.0864 - binary_accuracy: 0.9586" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9458/10000 [===========================>..] - ETA: 3s - loss: 0.0864 - binary_accuracy: 0.9587" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9466/10000 [===========================>..] - ETA: 3s - loss: 0.0863 - binary_accuracy: 0.9587" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9474/10000 [===========================>..] - ETA: 3s - loss: 0.0863 - binary_accuracy: 0.9587" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9483/10000 [===========================>..] - ETA: 3s - loss: 0.0862 - binary_accuracy: 0.9587" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9491/10000 [===========================>..] - ETA: 3s - loss: 0.0862 - binary_accuracy: 0.9588" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9499/10000 [===========================>..] - ETA: 3s - loss: 0.0861 - binary_accuracy: 0.9588" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9507/10000 [===========================>..] - ETA: 3s - loss: 0.0861 - binary_accuracy: 0.9588" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9515/10000 [===========================>..] - ETA: 2s - loss: 0.0861 - binary_accuracy: 0.9588" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9523/10000 [===========================>..] - ETA: 2s - loss: 0.0860 - binary_accuracy: 0.9589" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9531/10000 [===========================>..] - ETA: 2s - loss: 0.0860 - binary_accuracy: 0.9589" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9540/10000 [===========================>..] - ETA: 2s - loss: 0.0859 - binary_accuracy: 0.9589" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9548/10000 [===========================>..] - ETA: 2s - loss: 0.0859 - binary_accuracy: 0.9590" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9557/10000 [===========================>..] - ETA: 2s - loss: 0.0858 - binary_accuracy: 0.9590" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9566/10000 [===========================>..] - ETA: 2s - loss: 0.0857 - binary_accuracy: 0.9590" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9575/10000 [===========================>..] - ETA: 2s - loss: 0.0857 - binary_accuracy: 0.9591" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9584/10000 [===========================>..] - ETA: 2s - loss: 0.0856 - binary_accuracy: 0.9591" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9592/10000 [===========================>..] - ETA: 2s - loss: 0.0855 - binary_accuracy: 0.9591" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9601/10000 [===========================>..] - ETA: 2s - loss: 0.0855 - binary_accuracy: 0.9592" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9610/10000 [===========================>..] - ETA: 2s - loss: 0.0854 - binary_accuracy: 0.9592" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9619/10000 [===========================>..] - ETA: 2s - loss: 0.0853 - binary_accuracy: 0.9592" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9628/10000 [===========================>..] - ETA: 2s - loss: 0.0853 - binary_accuracy: 0.9593" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9637/10000 [===========================>..] - ETA: 2s - loss: 0.0852 - binary_accuracy: 0.9593" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9646/10000 [===========================>..] - ETA: 2s - loss: 0.0851 - binary_accuracy: 0.9593" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9655/10000 [===========================>..] - ETA: 2s - loss: 0.0851 - binary_accuracy: 0.9594" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9664/10000 [===========================>..] - ETA: 2s - loss: 0.0850 - binary_accuracy: 0.9594" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9673/10000 [============================>.] - ETA: 2s - loss: 0.0849 - binary_accuracy: 0.9594" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9682/10000 [============================>.] - ETA: 1s - loss: 0.0849 - binary_accuracy: 0.9595" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9691/10000 [============================>.] - ETA: 1s - loss: 0.0848 - binary_accuracy: 0.9595" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9699/10000 [============================>.] - ETA: 1s - loss: 0.0847 - binary_accuracy: 0.9595" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9708/10000 [============================>.] - ETA: 1s - loss: 0.0847 - binary_accuracy: 0.9596" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9716/10000 [============================>.] - ETA: 1s - loss: 0.0846 - binary_accuracy: 0.9596" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9725/10000 [============================>.] - ETA: 1s - loss: 0.0845 - binary_accuracy: 0.9596" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9734/10000 [============================>.] - ETA: 1s - loss: 0.0844 - binary_accuracy: 0.9597" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9743/10000 [============================>.] - ETA: 1s - loss: 0.0844 - binary_accuracy: 0.9597" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9751/10000 [============================>.] - ETA: 1s - loss: 0.0843 - binary_accuracy: 0.9597" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9759/10000 [============================>.] - ETA: 1s - loss: 0.0842 - binary_accuracy: 0.9598" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9768/10000 [============================>.] - ETA: 1s - loss: 0.0842 - binary_accuracy: 0.9598" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9777/10000 [============================>.] - ETA: 1s - loss: 0.0841 - binary_accuracy: 0.9598" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9785/10000 [============================>.] - ETA: 1s - loss: 0.0840 - binary_accuracy: 0.9599" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9794/10000 [============================>.] - ETA: 1s - loss: 0.0840 - binary_accuracy: 0.9599" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9803/10000 [============================>.] - ETA: 1s - loss: 0.0839 - binary_accuracy: 0.9600" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9812/10000 [============================>.] - ETA: 1s - loss: 0.0838 - binary_accuracy: 0.9600" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9821/10000 [============================>.] - ETA: 1s - loss: 0.0837 - binary_accuracy: 0.9600" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9830/10000 [============================>.] - ETA: 1s - loss: 0.0837 - binary_accuracy: 0.9601" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9839/10000 [============================>.] - ETA: 0s - loss: 0.0836 - binary_accuracy: 0.9601" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9847/10000 [============================>.] - ETA: 0s - loss: 0.0835 - binary_accuracy: 0.9601" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9856/10000 [============================>.] - ETA: 0s - loss: 0.0834 - binary_accuracy: 0.9602" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9865/10000 [============================>.] - ETA: 0s - loss: 0.0834 - binary_accuracy: 0.9602" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9874/10000 [============================>.] - ETA: 0s - loss: 0.0833 - binary_accuracy: 0.9602" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9883/10000 [============================>.] - ETA: 0s - loss: 0.0832 - binary_accuracy: 0.9603" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9892/10000 [============================>.] - ETA: 0s - loss: 0.0831 - binary_accuracy: 0.9603" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9901/10000 [============================>.] - ETA: 0s - loss: 0.0831 - binary_accuracy: 0.9603" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9910/10000 [============================>.] - ETA: 0s - loss: 0.0830 - binary_accuracy: 0.9604" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9919/10000 [============================>.] - ETA: 0s - loss: 0.0829 - binary_accuracy: 0.9604" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9928/10000 [============================>.] - ETA: 0s - loss: 0.0829 - binary_accuracy: 0.9604" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9937/10000 [============================>.] - ETA: 0s - loss: 0.0828 - binary_accuracy: 0.9605" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9946/10000 [============================>.] - ETA: 0s - loss: 0.0827 - binary_accuracy: 0.9605" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9955/10000 [============================>.] - ETA: 0s - loss: 0.0827 - binary_accuracy: 0.9605" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9963/10000 [============================>.] - ETA: 0s - loss: 0.0826 - binary_accuracy: 0.9606" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9972/10000 [============================>.] - ETA: 0s - loss: 0.0825 - binary_accuracy: 0.9606" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9981/10000 [============================>.] - ETA: 0s - loss: 0.0825 - binary_accuracy: 0.9606" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9990/10000 [============================>.] - ETA: 0s - loss: 0.0824 - binary_accuracy: 0.9607" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9999/10000 [============================>.] - ETA: 0s - loss: 0.0824 - binary_accuracy: 0.9607" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "10000/10000 [==============================] - 78s 7ms/step - loss: 0.0824 - binary_accuracy: 0.9607 - val_loss: 1.0096 - val_binary_accuracy: 0.8731\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:struct2tensor is not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:tensorflow_decision_forests is not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:tensorflow_text is not available.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:serve_transformed_features = {'trip_miles_xf': , 'pickup_community_area_xf': , 'dropoff_longitude_xf': , 'payment_type_xf': , 'fare_xf': , 'pickup_longitude_xf': , 'trip_seconds_xf': , 'dropoff_census_tract_xf': , 'pickup_census_tract_xf': , 'trip_start_day_xf': , 'dropoff_community_area_xf': , 'pickup_latitude_xf': , 'dropoff_latitude_xf': , 'trip_start_month_xf': , 'trip_start_hour_xf': , 'company_xf': }\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Function `serve_tf_examples_fn` contains input name(s) 327971, 327981, 327991, 328001, 328011, 328021, 328031, 328041, 328051, resource with unsupported characters which will be renamed to transform_features_layer_327971, transform_features_layer_327981, transform_features_layer_327991, transform_features_layer_328001, transform_features_layer_328011, transform_features_layer_328021, transform_features_layer_328031, transform_features_layer_328041, transform_features_layer_328051, model_dense_4_biasadd_readvariableop_resource in the SavedModel.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature company has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature dropoff_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature fare has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature payment_type has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_census_tract has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_community_area has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_latitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature pickup_longitude has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature tips has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_miles has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_seconds has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_day has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_hour has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_month has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Feature trip_start_timestamp has no shape. Setting to varlen_sparse_tensor.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:eval_transformed_features = {'trip_miles_xf': , 'pickup_community_area_xf': , 'dropoff_longitude_xf': , 'payment_type_xf': , 'fare_xf': , 'pickup_longitude_xf': , 'trip_seconds_xf': , 'dropoff_census_tract_xf': , 'pickup_census_tract_xf': , 'trip_start_day_xf': , 'dropoff_community_area_xf': , 'pickup_latitude_xf': , 'dropoff_latitude_xf': , 'trip_start_month_xf': , 'trip_start_hour_xf': , 'company_xf': , 'tips': }\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Function `transform_features_fn` contains input name(s) 328346, 328356, 328366, 328376, 328386, 328396, 328406, 328416, 328426 with unsupported characters which will be renamed to transform_features_layer_328346, transform_features_layer_328356, transform_features_layer_328366, transform_features_layer_328376, transform_features_layer_328386, transform_features_layer_328396, transform_features_layer_328406, transform_features_layer_328416, transform_features_layer_328426 in the SavedModel.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6/Format-Serving/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Writing fingerprint to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6/Format-Serving/fingerprint.pb\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Training complete. Model written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6/Format-Serving. ModelRun written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model_run/6\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running publisher for Trainer\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "data": { "text/html": [ "\n", "\n", "
ExecutionResult at 0x7faae5ce2a30
.execution_id6
.component\n", "\n", "
Trainer at 0x7fa9c4200ee0
.inputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7fabe90f7d90
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transformed_examples/5) at 0x7fac00504ac0
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transformed_examples/5
.span0
.split_names["eval", "train"]
.version0
['transform_graph']\n", "\n", "
Channel of type 'TransformGraph' (1 artifact) at 0x7fabe90f7940
.type_nameTransformGraph
._artifacts
[0]\n", "\n", "
Artifact of type 'TransformGraph' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5) at 0x7fabe90f79d0
.type<class 'tfx.types.standard_artifacts.TransformGraph'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5
['schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fac1cbe04c0
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3) at 0x7fac0081e0a0
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3
.outputs
['model']\n", "\n", "
Channel of type 'Model' (1 artifact) at 0x7fa9c41525b0
.type_nameModel
._artifacts
[0]\n", "\n", "
Artifact of type 'Model' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6) at 0x7fa9c41524f0
.type<class 'tfx.types.standard_artifacts.Model'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6
['model_run']\n", "\n", "
Channel of type 'ModelRun' (1 artifact) at 0x7fa9c4152310
.type_nameModelRun
._artifacts
[0]\n", "\n", "
Artifact of type 'ModelRun' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model_run/6) at 0x7fa9c4152550
.type<class 'tfx.types.standard_artifacts.ModelRun'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model_run/6
.exec_properties
['train_args']{\n", " "num_steps": 10000\n", "}
['eval_args']{\n", " "num_steps": 5000\n", "}
['module_file']None
['run_fn']None
['trainer_fn']None
['custom_config']null
['module_path']taxi_trainer@/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/_wheels/tfx_user_code_Trainer-0.0+c83184ecc7def0038911b1171ce3fafec7757168a9f4d647f3c3b86b5467668d-py3-none-any.whl
.component.inputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7fabe90f7d90
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transformed_examples/5) at 0x7fac00504ac0
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transformed_examples/5
.span0
.split_names["eval", "train"]
.version0
['transform_graph']\n", "\n", "
Channel of type 'TransformGraph' (1 artifact) at 0x7fabe90f7940
.type_nameTransformGraph
._artifacts
[0]\n", "\n", "
Artifact of type 'TransformGraph' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5) at 0x7fabe90f79d0
.type<class 'tfx.types.standard_artifacts.TransformGraph'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Transform/transform_graph/5
['schema']\n", "\n", "
Channel of type 'Schema' (1 artifact) at 0x7fac1cbe04c0
.type_nameSchema
._artifacts
[0]\n", "\n", "
Artifact of type 'Schema' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3) at 0x7fac0081e0a0
.type<class 'tfx.types.standard_artifacts.Schema'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/SchemaGen/schema/3
.component.outputs
['model']\n", "\n", "
Channel of type 'Model' (1 artifact) at 0x7fa9c41525b0
.type_nameModel
._artifacts
[0]\n", "\n", "
Artifact of type 'Model' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6) at 0x7fa9c41524f0
.type<class 'tfx.types.standard_artifacts.Model'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6
['model_run']\n", "\n", "
Channel of type 'ModelRun' (1 artifact) at 0x7fa9c4152310
.type_nameModelRun
._artifacts
[0]\n", "\n", "
Artifact of type 'ModelRun' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model_run/6) at 0x7fa9c4152550
.type<class 'tfx.types.standard_artifacts.ModelRun'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model_run/6
" ], "text/plain": [ "ExecutionResult(\n", " component_id: Trainer\n", " execution_id: 6\n", " outputs:\n", " model: OutputChannel(artifact_type=Model, producer_component_id=Trainer, output_key=model, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)\n", " model_run: OutputChannel(artifact_type=ModelRun, producer_component_id=Trainer, output_key=model_run, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False))" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer = tfx.components.Trainer(\n", " module_file=os.path.abspath(_taxi_trainer_module_file),\n", " examples=transform.outputs['transformed_examples'],\n", " transform_graph=transform.outputs['transform_graph'],\n", " schema=schema_gen.outputs['schema'],\n", " train_args=tfx.proto.TrainArgs(num_steps=10000),\n", " eval_args=tfx.proto.EvalArgs(num_steps=5000))\n", "context.run(trainer, enable_cache=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "6Cql1G35StJp" }, "source": [ "#### Analyze Training with TensorBoard\n", "Take a peek at the trainer artifact. It points to a directory containing the model subdirectories." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:27:47.436219Z", "iopub.status.busy": "2024-08-02T09:27:47.435960Z", "iopub.status.idle": "2024-08-02T09:27:47.440918Z", "shell.execute_reply": "2024-08-02T09:27:47.440277Z" }, "id": "bXe62WE0S0Ek" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Format-Serving']\n", "['saved_model.pb', 'variables', 'assets', 'fingerprint.pb', 'keras_metadata.pb']\n" ] } ], "source": [ "model_artifact_dir = trainer.outputs['model'].get()[0].uri\n", "pp.pprint(os.listdir(model_artifact_dir))\n", "model_dir = os.path.join(model_artifact_dir, 'Format-Serving')\n", "pp.pprint(os.listdir(model_dir))" ] }, { "cell_type": "markdown", "metadata": { "id": "DfjOmSro6Q3Y" }, "source": [ "Optionally, we can connect TensorBoard to the Trainer to analyze our model's training curves." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-APzqz2NeAyj" }, "outputs": [], "source": [ "model_run_artifact_dir = trainer.outputs['model_run'].get()[0].uri\n", "\n", "%load_ext tensorboard\n", "%tensorboard --logdir {model_run_artifact_dir}" ] }, { "cell_type": "markdown", "metadata": { "id": "FmPftrv0lEQy" }, "source": [ "### Evaluator\n", "The `Evaluator` component computes model performance metrics over the evaluation set. It uses the [TensorFlow Model Analysis](https://www.tensorflow.org/tfx/model_analysis/get_started) library. The `Evaluator` can also optionally validate that a newly trained model is better than the previous model. This is useful in a production pipeline setting where you may automatically train and validate a model every day. In this notebook, we only train one model, so the `Evaluator` automatically will label the\n", "model as \"good\".\n", "\n", "`Evaluator` will take as input the data from `ExampleGen`, the trained model from `Trainer`, and slicing configuration. The slicing configuration allows you to slice your metrics on feature values (e.g. how does your model perform on taxi trips that start at 8am versus 8pm?). See an example of this configuration below:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:27:47.444453Z", "iopub.status.busy": "2024-08-02T09:27:47.444230Z", "iopub.status.idle": "2024-08-02T09:27:47.450360Z", "shell.execute_reply": "2024-08-02T09:27:47.449681Z" }, "id": "fVhfzzh9PDEx" }, "outputs": [], "source": [ "# Imported files such as taxi_constants are normally cached, so changes are\n", "# not honored after the first import. Normally this is good for efficiency, but\n", "# during development when we may be iterating code it can be a problem. To\n", "# avoid this problem during development, reload the file.\n", "import taxi_constants\n", "import sys\n", "if 'google.colab' in sys.modules: # Testing to see if we're doing development\n", " import importlib\n", " importlib.reload(taxi_constants)\n", "\n", "eval_config = tfma.EvalConfig(\n", " model_specs=[\n", " # This assumes a serving model with signature 'serving_default'. If\n", " # using estimator based EvalSavedModel, add signature_name: 'eval' and\n", " # remove the label_key.\n", " tfma.ModelSpec(\n", " signature_name='serving_default',\n", " label_key=taxi_constants.LABEL_KEY,\n", " preprocessing_function_names=['transform_features'],\n", " )\n", " ],\n", " metrics_specs=[\n", " tfma.MetricsSpec(\n", " # The metrics added here are in addition to those saved with the\n", " # model (assuming either a keras model or EvalSavedModel is used).\n", " # Any metrics added into the saved model (for example using\n", " # model.compile(..., metrics=[...]), etc) will be computed\n", " # automatically.\n", " # To add validation thresholds for metrics saved with the model,\n", " # add them keyed by metric name to the thresholds map.\n", " metrics=[\n", " tfma.MetricConfig(class_name='ExampleCount'),\n", " tfma.MetricConfig(class_name='BinaryAccuracy',\n", " threshold=tfma.MetricThreshold(\n", " value_threshold=tfma.GenericValueThreshold(\n", " lower_bound={'value': 0.5}),\n", " # Change threshold will be ignored if there is no\n", " # baseline model resolved from MLMD (first run).\n", " change_threshold=tfma.GenericChangeThreshold(\n", " direction=tfma.MetricDirection.HIGHER_IS_BETTER,\n", " absolute={'value': -1e-10})))\n", " ]\n", " )\n", " ],\n", " slicing_specs=[\n", " # An empty slice spec means the overall slice, i.e. the whole dataset.\n", " tfma.SlicingSpec(),\n", " # Data can be sliced along a feature column. In this case, data is\n", " # sliced along feature column trip_start_hour.\n", " tfma.SlicingSpec(\n", " feature_keys=['trip_start_hour'])\n", " ])" ] }, { "cell_type": "markdown", "metadata": { "id": "9mBdKH1F8JuT" }, "source": [ "Next, we give this configuration to `Evaluator` and run it." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:27:47.453432Z", "iopub.status.busy": "2024-08-02T09:27:47.453196Z", "iopub.status.idle": "2024-08-02T09:28:08.875427Z", "shell.execute_reply": "2024-08-02T09:28:08.874713Z" }, "id": "Zjcx8g6mihSt" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running driver for latest_blessed_model_resolver\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running publisher for latest_blessed_model_resolver\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running driver for Evaluator\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running executor for Evaluator\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:udf_utils.get_fn {'eval_config': '{\\n \"metrics_specs\": [\\n {\\n \"metrics\": [\\n {\\n \"class_name\": \"ExampleCount\"\\n },\\n {\\n \"class_name\": \"BinaryAccuracy\",\\n \"threshold\": {\\n \"change_threshold\": {\\n \"absolute\": -1e-10,\\n \"direction\": \"HIGHER_IS_BETTER\"\\n },\\n \"value_threshold\": {\\n \"lower_bound\": 0.5\\n }\\n }\\n }\\n ]\\n }\\n ],\\n \"model_specs\": [\\n {\\n \"label_key\": \"tips\",\\n \"preprocessing_function_names\": [\\n \"transform_features\"\\n ],\\n \"signature_name\": \"serving_default\"\\n }\\n ],\\n \"slicing_specs\": [\\n {},\\n {\\n \"feature_keys\": [\\n \"trip_start_hour\"\\n ]\\n }\\n ]\\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_eval_shared_model'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=\n", "model_specs {\n", " signature_name: \"serving_default\"\n", " label_key: \"tips\"\n", " preprocessing_function_names: \"transform_features\"\n", "}\n", "slicing_specs {\n", "}\n", "slicing_specs {\n", " feature_keys: \"trip_start_hour\"\n", "}\n", "metrics_specs {\n", " metrics {\n", " class_name: \"ExampleCount\"\n", " }\n", " metrics {\n", " class_name: \"BinaryAccuracy\"\n", " threshold {\n", " value_threshold {\n", " lower_bound {\n", " value: 0.5\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Using /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6/Format-Serving as model.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:The 'example_splits' parameter is not set, using 'eval' split.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Evaluating model.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:udf_utils.get_fn {'eval_config': '{\\n \"metrics_specs\": [\\n {\\n \"metrics\": [\\n {\\n \"class_name\": \"ExampleCount\"\\n },\\n {\\n \"class_name\": \"BinaryAccuracy\",\\n \"threshold\": {\\n \"change_threshold\": {\\n \"absolute\": -1e-10,\\n \"direction\": \"HIGHER_IS_BETTER\"\\n },\\n \"value_threshold\": {\\n \"lower_bound\": 0.5\\n }\\n }\\n }\\n ]\\n }\\n ],\\n \"model_specs\": [\\n {\\n \"label_key\": \"tips\",\\n \"preprocessing_function_names\": [\\n \"transform_features\"\\n ],\\n \"signature_name\": \"serving_default\"\\n }\\n ],\\n \"slicing_specs\": [\\n {},\\n {\\n \"feature_keys\": [\\n \"trip_start_hour\"\\n ]\\n }\\n ]\\n}', 'feature_slicing_spec': None, 'fairness_indicator_thresholds': 'null', 'example_splits': 'null', 'module_file': None, 'module_path': None} 'custom_extractors'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=\n", "model_specs {\n", " signature_name: \"serving_default\"\n", " label_key: \"tips\"\n", " preprocessing_function_names: \"transform_features\"\n", "}\n", "slicing_specs {\n", "}\n", "slicing_specs {\n", " feature_keys: \"trip_start_hour\"\n", "}\n", "metrics_specs {\n", " metrics {\n", " class_name: \"ExampleCount\"\n", " }\n", " metrics {\n", " class_name: \"BinaryAccuracy\"\n", " threshold {\n", " value_threshold {\n", " lower_bound {\n", " value: 0.5\n", " }\n", " }\n", " }\n", " }\n", " model_names: \"\"\n", "}\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=\n", "model_specs {\n", " signature_name: \"serving_default\"\n", " label_key: \"tips\"\n", " preprocessing_function_names: \"transform_features\"\n", "}\n", "slicing_specs {\n", "}\n", "slicing_specs {\n", " feature_keys: \"trip_start_hour\"\n", "}\n", "metrics_specs {\n", " metrics {\n", " class_name: \"ExampleCount\"\n", " }\n", " metrics {\n", " class_name: \"BinaryAccuracy\"\n", " threshold {\n", " value_threshold {\n", " lower_bound {\n", " value: 0.5\n", " }\n", " }\n", " }\n", " }\n", " model_names: \"\"\n", "}\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:eval_shared_models have model_types: {'tf_keras'}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Request was made to ignore the baseline ModelSpec and any change thresholds. This is likely because a baseline model was not provided: updated_config=\n", "model_specs {\n", " signature_name: \"serving_default\"\n", " label_key: \"tips\"\n", " preprocessing_function_names: \"transform_features\"\n", "}\n", "slicing_specs {\n", "}\n", "slicing_specs {\n", " feature_keys: \"trip_start_hour\"\n", "}\n", "metrics_specs {\n", " metrics {\n", " class_name: \"ExampleCount\"\n", " }\n", " metrics {\n", " class_name: \"BinaryAccuracy\"\n", " threshold {\n", " value_threshold {\n", " lower_bound {\n", " value: 0.5\n", " }\n", " }\n", " }\n", " }\n", " model_names: \"\"\n", "}\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Evaluation complete. Results written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/evaluation/8.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Checking validation results.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:112: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use eager execution and: \n", "`tf.data.TFRecordDataset(path)`\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Blessing result True written to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/blessing/8.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running publisher for Evaluator\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "data": { "text/html": [ "\n", "\n", "
ExecutionResult at 0x7fac1cc5c850
.execution_id8
.component\n", "\n", "
Evaluator at 0x7faabc230790
.inputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7faad4550820
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1) at 0x7fac1c3a6070
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1
.span0
.split_names["train", "eval"]
.version0
['model']\n", "\n", "
Channel of type 'Model' (1 artifact) at 0x7fa9c41525b0
.type_nameModel
._artifacts
[0]\n", "\n", "
Artifact of type 'Model' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6) at 0x7fa9c41524f0
.type<class 'tfx.types.standard_artifacts.Model'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6
['baseline_model']\n", "\n", "
Channel of type 'Model' (0 artifacts) at 0x7faad452ac70
.type_nameModel
._artifacts[]
.outputs
['evaluation']\n", "\n", "
Channel of type 'ModelEvaluation' (1 artifact) at 0x7fa9d00edd60
.type_nameModelEvaluation
._artifacts
[0]\n", "\n", "
Artifact of type 'ModelEvaluation' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/evaluation/8) at 0x7faad452aa30
.type<class 'tfx.types.standard_artifacts.ModelEvaluation'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/evaluation/8
['blessing']\n", "\n", "
Channel of type 'ModelBlessing' (1 artifact) at 0x7fa9d00ed520
.type_nameModelBlessing
._artifacts
[0]\n", "\n", "
Artifact of type 'ModelBlessing' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/blessing/8) at 0x7fa9c417f820
.type<class 'tfx.types.standard_artifacts.ModelBlessing'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/blessing/8
.exec_properties
['eval_config']{\n", " "metrics_specs": [\n", " {\n", " "metrics": [\n", " {\n", " "class_name": "ExampleCount"\n", " },\n", " {\n", " "class_name": "BinaryAccuracy",\n", " "threshold": {\n", " "change_threshold": {\n", " "absolute": -1e-10,\n", " "direction": "HIGHER_IS_BETTER"\n", " },\n", " "value_threshold": {\n", " "lower_bound": 0.5\n", " }\n", " }\n", " }\n", " ]\n", " }\n", " ],\n", " "model_specs": [\n", " {\n", " "label_key": "tips",\n", " "preprocessing_function_names": [\n", " "transform_features"\n", " ],\n", " "signature_name": "serving_default"\n", " }\n", " ],\n", " "slicing_specs": [\n", " {},\n", " {\n", " "feature_keys": [\n", " "trip_start_hour"\n", " ]\n", " }\n", " ]\n", "}
['feature_slicing_spec']None
['fairness_indicator_thresholds']null
['example_splits']null
['module_file']None
['module_path']None
.component.inputs
['examples']\n", "\n", "
Channel of type 'Examples' (1 artifact) at 0x7faad4550820
.type_nameExamples
._artifacts
[0]\n", "\n", "
Artifact of type 'Examples' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1) at 0x7fac1c3a6070
.type<class 'tfx.types.standard_artifacts.Examples'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/CsvExampleGen/examples/1
.span0
.split_names["train", "eval"]
.version0
['model']\n", "\n", "
Channel of type 'Model' (1 artifact) at 0x7fa9c41525b0
.type_nameModel
._artifacts
[0]\n", "\n", "
Artifact of type 'Model' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6) at 0x7fa9c41524f0
.type<class 'tfx.types.standard_artifacts.Model'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6
['baseline_model']\n", "\n", "
Channel of type 'Model' (0 artifacts) at 0x7faad452ac70
.type_nameModel
._artifacts[]
.component.outputs
['evaluation']\n", "\n", "
Channel of type 'ModelEvaluation' (1 artifact) at 0x7fa9d00edd60
.type_nameModelEvaluation
._artifacts
[0]\n", "\n", "
Artifact of type 'ModelEvaluation' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/evaluation/8) at 0x7faad452aa30
.type<class 'tfx.types.standard_artifacts.ModelEvaluation'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/evaluation/8
['blessing']\n", "\n", "
Channel of type 'ModelBlessing' (1 artifact) at 0x7fa9d00ed520
.type_nameModelBlessing
._artifacts
[0]\n", "\n", "
Artifact of type 'ModelBlessing' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/blessing/8) at 0x7fa9c417f820
.type<class 'tfx.types.standard_artifacts.ModelBlessing'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/blessing/8
" ], "text/plain": [ "ExecutionResult(\n", " component_id: Evaluator\n", " execution_id: 8\n", " outputs:\n", " evaluation: OutputChannel(artifact_type=ModelEvaluation, producer_component_id=Evaluator, output_key=evaluation, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)\n", " blessing: OutputChannel(artifact_type=ModelBlessing, producer_component_id=Evaluator, output_key=blessing, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False))" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use TFMA to compute a evaluation statistics over features of a model and\n", "# validate them against a baseline.\n", "\n", "# The model resolver is only required if performing model validation in addition\n", "# to evaluation. In this case we validate against the latest blessed model. If\n", "# no model has been blessed before (as in this case) the evaluator will make our\n", "# candidate the first blessed model.\n", "model_resolver = tfx.dsl.Resolver(\n", " strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,\n", " model=tfx.dsl.Channel(type=tfx.types.standard_artifacts.Model),\n", " model_blessing=tfx.dsl.Channel(\n", " type=tfx.types.standard_artifacts.ModelBlessing)).with_id(\n", " 'latest_blessed_model_resolver')\n", "context.run(model_resolver, enable_cache=True)\n", "\n", "evaluator = tfx.components.Evaluator(\n", " examples=example_gen.outputs['examples'],\n", " model=trainer.outputs['model'],\n", " baseline_model=model_resolver.outputs['model'],\n", " eval_config=eval_config)\n", "context.run(evaluator, enable_cache=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "AeCVkBusS_8g" }, "source": [ "Now let's examine the output artifacts of `Evaluator`." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:28:08.879405Z", "iopub.status.busy": "2024-08-02T09:28:08.878879Z", "iopub.status.idle": "2024-08-02T09:28:08.883333Z", "shell.execute_reply": "2024-08-02T09:28:08.882771Z" }, "id": "k4GghePOTJxL" }, "outputs": [ { "data": { "text/plain": [ "{'evaluation': OutputChannel(artifact_type=ModelEvaluation, producer_component_id=Evaluator, output_key=evaluation, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False),\n", " 'blessing': OutputChannel(artifact_type=ModelBlessing, producer_component_id=Evaluator, output_key=blessing, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)}" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluator.outputs" ] }, { "cell_type": "markdown", "metadata": { "id": "Y5TMskWe9LL0" }, "source": [ "Using the `evaluation` output we can show the default visualization of global metrics on the entire evaluation set." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:28:08.886954Z", "iopub.status.busy": "2024-08-02T09:28:08.886352Z", "iopub.status.idle": "2024-08-02T09:28:08.900379Z", "shell.execute_reply": "2024-08-02T09:28:08.899719Z" }, "id": "U729j5X5QQUQ" }, "outputs": [ { "data": { "text/html": [ "Artifact at /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/evaluation/8

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e2b1cd9f121342649f9a59401460ff97", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'Overall', 'metrics':…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "context.show(evaluator.outputs['evaluation'])" ] }, { "cell_type": "markdown", "metadata": { "id": "t-tI4p6m-OAn" }, "source": [ "To see the visualization for sliced evaluation metrics, we can directly call the TensorFlow Model Analysis library." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:28:08.904798Z", "iopub.status.busy": "2024-08-02T09:28:08.904231Z", "iopub.status.idle": "2024-08-02T09:28:08.917775Z", "shell.execute_reply": "2024-08-02T09:28:08.917097Z" }, "id": "pyis6iy0HLdi" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "07061bf8a5a54f16b11843ba964f5be4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "SlicingMetricsViewer(config={'weightedExamplesColumn': 'example_count'}, data=[{'slice': 'trip_start_hour:19',…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tensorflow_model_analysis as tfma\n", "\n", "# Get the TFMA output result path and load the result.\n", "PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri\n", "tfma_result = tfma.load_eval_result(PATH_TO_RESULT)\n", "\n", "# Show data sliced along feature column trip_start_hour.\n", "tfma.view.render_slicing_metrics(\n", " tfma_result, slicing_column='trip_start_hour')" ] }, { "cell_type": "markdown", "metadata": { "id": "7uvYrUf2-r_6" }, "source": [ "This visualization shows the same metrics, but computed at every feature value of `trip_start_hour` instead of on the entire evaluation set.\n", "\n", "TensorFlow Model Analysis supports many other visualizations, such as Fairness Indicators and plotting a time series of model performance. To learn more, see [the tutorial](https://www.tensorflow.org/tfx/tutorials/model_analysis/tfma_basic)." ] }, { "cell_type": "markdown", "metadata": { "id": "TEotnkxEswUb" }, "source": [ "Since we added thresholds to our config, validation output is also available. The precence of a `blessing` artifact indicates that our model passed validation. Since this is the first validation being performed the candidate is automatically blessed." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:28:08.921930Z", "iopub.status.busy": "2024-08-02T09:28:08.921375Z", "iopub.status.idle": "2024-08-02T09:28:09.118377Z", "shell.execute_reply": "2024-08-02T09:28:09.117295Z" }, "id": "FZmiRtg6TKtR" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 0\r\n", "-rw-rw-r-- 1 kbuilder kbuilder 0 Aug 2 09:28 BLESSED\r\n" ] } ], "source": [ "blessing_uri = evaluator.outputs['blessing'].get()[0].uri\n", "!ls -l {blessing_uri}" ] }, { "cell_type": "markdown", "metadata": { "id": "hM1tFkOVSBa0" }, "source": [ "Now can also verify the success by loading the validation result record:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:28:09.123173Z", "iopub.status.busy": "2024-08-02T09:28:09.122492Z", "iopub.status.idle": "2024-08-02T09:28:09.129226Z", "shell.execute_reply": "2024-08-02T09:28:09.128517Z" }, "id": "lxa5G08bSJ8a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "validation_ok: true\n", "validation_details {\n", " slicing_details {\n", " slicing_spec {\n", " }\n", " num_matching_slices: 25\n", " }\n", "}\n", "\n" ] } ], "source": [ "PATH_TO_RESULT = evaluator.outputs['evaluation'].get()[0].uri\n", "print(tfma.load_validation_result(PATH_TO_RESULT))" ] }, { "cell_type": "markdown", "metadata": { "id": "T8DYekCZlHfj" }, "source": [ "### Pusher\n", "The `Pusher` component is usually at the end of a TFX pipeline. It checks whether a model has passed validation, and if so, exports the model to `_serving_model_dir`." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:28:09.132592Z", "iopub.status.busy": "2024-08-02T09:28:09.132105Z", "iopub.status.idle": "2024-08-02T09:28:09.201270Z", "shell.execute_reply": "2024-08-02T09:28:09.200544Z" }, "id": "r45nQ69eikc9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running driver for Pusher\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running executor for Pusher\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Model version: 1722590889\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Model written to serving path /tmpfs/tmp/tmpyd4qezdk/serving_model/taxi_simple/1722590889.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Model pushed to /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Pusher/pushed_model/9.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:Running publisher for Pusher\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:absl:MetadataStore with DB connection initialized\n" ] }, { "data": { "text/html": [ "\n", "\n", "
ExecutionResult at 0x7fa9e03794c0
.execution_id9
.component\n", "\n", "
Pusher at 0x7fa9d07c0f10
.inputs
['model']\n", "\n", "
Channel of type 'Model' (1 artifact) at 0x7fa9c41525b0
.type_nameModel
._artifacts
[0]\n", "\n", "
Artifact of type 'Model' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6) at 0x7fa9c41524f0
.type<class 'tfx.types.standard_artifacts.Model'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6
['model_blessing']\n", "\n", "
Channel of type 'ModelBlessing' (1 artifact) at 0x7fa9d00ed520
.type_nameModelBlessing
._artifacts
[0]\n", "\n", "
Artifact of type 'ModelBlessing' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/blessing/8) at 0x7fa9c417f820
.type<class 'tfx.types.standard_artifacts.ModelBlessing'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/blessing/8
.outputs
['pushed_model']\n", "\n", "
Channel of type 'PushedModel' (1 artifact) at 0x7fa9e0050490
.type_namePushedModel
._artifacts
[0]\n", "\n", "
Artifact of type 'PushedModel' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Pusher/pushed_model/9) at 0x7fa9e037a880
.type<class 'tfx.types.standard_artifacts.PushedModel'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Pusher/pushed_model/9
.exec_properties
['push_destination']{\n", " "filesystem": {\n", " "base_directory": "/tmpfs/tmp/tmpyd4qezdk/serving_model/taxi_simple"\n", " }\n", "}
['custom_config']null
.component.inputs
['model']\n", "\n", "
Channel of type 'Model' (1 artifact) at 0x7fa9c41525b0
.type_nameModel
._artifacts
[0]\n", "\n", "
Artifact of type 'Model' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6) at 0x7fa9c41524f0
.type<class 'tfx.types.standard_artifacts.Model'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Trainer/model/6
['model_blessing']\n", "\n", "
Channel of type 'ModelBlessing' (1 artifact) at 0x7fa9d00ed520
.type_nameModelBlessing
._artifacts
[0]\n", "\n", "
Artifact of type 'ModelBlessing' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/blessing/8) at 0x7fa9c417f820
.type<class 'tfx.types.standard_artifacts.ModelBlessing'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Evaluator/blessing/8
.component.outputs
['pushed_model']\n", "\n", "
Channel of type 'PushedModel' (1 artifact) at 0x7fa9e0050490
.type_namePushedModel
._artifacts
[0]\n", "\n", "
Artifact of type 'PushedModel' (uri: /tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Pusher/pushed_model/9) at 0x7fa9e037a880
.type<class 'tfx.types.standard_artifacts.PushedModel'>
.uri/tmpfs/tmp/tfx-interactive-2024-08-02T09_25_22.884962-abik3cle/Pusher/pushed_model/9
" ], "text/plain": [ "ExecutionResult(\n", " component_id: Pusher\n", " execution_id: 9\n", " outputs:\n", " pushed_model: OutputChannel(artifact_type=PushedModel, producer_component_id=Pusher, output_key=pushed_model, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False))" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pusher = tfx.components.Pusher(\n", " model=trainer.outputs['model'],\n", " model_blessing=evaluator.outputs['blessing'],\n", " push_destination=tfx.proto.PushDestination(\n", " filesystem=tfx.proto.PushDestination.Filesystem(\n", " base_directory=_serving_model_dir)))\n", "context.run(pusher, enable_cache=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "ctUErBYoTO9I" }, "source": [ "Let's examine the output artifacts of `Pusher`." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:28:09.204904Z", "iopub.status.busy": "2024-08-02T09:28:09.204300Z", "iopub.status.idle": "2024-08-02T09:28:09.208714Z", "shell.execute_reply": "2024-08-02T09:28:09.208028Z" }, "id": "pRkWo-MzTSss" }, "outputs": [ { "data": { "text/plain": [ "{'pushed_model': OutputChannel(artifact_type=PushedModel, producer_component_id=Pusher, output_key=pushed_model, additional_properties={}, additional_custom_properties={}, _input_trigger=None, _is_async=False)}" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pusher.outputs" ] }, { "cell_type": "markdown", "metadata": { "id": "peH2PPS3VgkL" }, "source": [ "In particular, the Pusher will export your model in the SavedModel format, which looks like this:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "execution": { "iopub.execute_input": "2024-08-02T09:28:09.212182Z", "iopub.status.busy": "2024-08-02T09:28:09.211627Z", "iopub.status.idle": "2024-08-02T09:28:10.639736Z", "shell.execute_reply": "2024-08-02T09:28:10.638837Z" }, "id": "4zyIqWl9TSdG" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('serving_default',\n", " Dict[['outputs', TensorSpec(shape=(None, 1), dtype=tf.float32, name='outputs')]] at 0x7FA96012E850>)\n", "('transform_features',\n", " Dict[['pickup_longitude_xf', TensorSpec(shape=(None,), dtype=tf.float32, name='pickup_longitude_xf')], ['trip_miles_xf', TensorSpec(shape=(None,), dtype=tf.float32, name='trip_miles_xf')], ['dropoff_community_area_xf', TensorSpec(shape=(None, 79), dtype=tf.float32, name='dropoff_community_area_xf')], ['trip_start_day_xf', TensorSpec(shape=(None, 17), dtype=tf.float32, name='trip_start_day_xf')], ['tips', TensorSpec(shape=(None,), dtype=tf.int64, name='tips')], ['trip_start_hour_xf', TensorSpec(shape=(None, 34), dtype=tf.float32, name='trip_start_hour_xf')], ['dropoff_longitude_xf', TensorSpec(shape=(None,), dtype=tf.float32, name='dropoff_longitude_xf')], ['company_xf', TensorSpec(shape=(None, 55), dtype=tf.float32, name='company_xf')], ['dropoff_latitude_xf', TensorSpec(shape=(None,), dtype=tf.float32, name='dropoff_latitude_xf')], ['payment_type_xf', TensorSpec(shape=(None, 16), dtype=tf.float32, name='payment_type_xf')], ['pickup_community_area_xf', TensorSpec(shape=(None, 66), dtype=tf.float32, name='pickup_community_area_xf')], ['fare_xf', TensorSpec(shape=(None,), dtype=tf.float32, name='fare_xf')], ['pickup_census_tract_xf', TensorSpec(shape=(None, 11), dtype=tf.float32, name='pickup_census_tract_xf')], ['dropoff_census_tract_xf', TensorSpec(shape=(None, 216), dtype=tf.float32, name='dropoff_census_tract_xf')], ['pickup_latitude_xf', TensorSpec(shape=(None,), dtype=tf.float32, name='pickup_latitude_xf')], ['trip_seconds_xf', TensorSpec(shape=(None,), dtype=tf.float32, name='trip_seconds_xf')], ['trip_start_month_xf', TensorSpec(shape=(None, 22), dtype=tf.float32, name='trip_start_month_xf')]] at 0x7FA9600DDDC0>)\n" ] } ], "source": [ "push_uri = pusher.outputs['pushed_model'].get()[0].uri\n", "model = tf.saved_model.load(push_uri)\n", "\n", "for item in model.signatures.items():\n", " pp.pprint(item)" ] }, { "cell_type": "markdown", "metadata": { "id": "3-YPNUuHANtj" }, "source": [ "We're finished our tour of built-in TFX components!" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "wdeKOEkv1Fe8" ], "name": "components_keras.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.19" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "07061bf8a5a54f16b11843ba964f5be4": { "model_module": "tensorflow_model_analysis", "model_module_version": "0.46.0", "model_name": "SlicingMetricsModel", "state": { "_dom_classes": [], "_model_module": "tensorflow_model_analysis", "_model_module_version": "0.46.0", "_model_name": "SlicingMetricsModel", "_view_count": null, "_view_module": "tensorflow_model_analysis", "_view_module_version": "0.46.0", "_view_name": "SlicingMetricsView", "config": { "weightedExamplesColumn": "example_count" }, "data": [ { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8823529411764706 }, "example_count": { "doubleValue": 323.0 }, "loss": { "doubleValue": 0.8281715512275696 } } } }, "slice": "trip_start_hour:19" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8539325842696629 }, "example_count": { "doubleValue": 178.0 }, "loss": { "doubleValue": 1.5785566568374634 } } } }, "slice": "trip_start_hour:1" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8636363636363636 }, "example_count": { "doubleValue": 110.0 }, "loss": { "doubleValue": 0.5877605676651001 } } } }, "slice": "trip_start_hour:7" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.892018779342723 }, "example_count": { "doubleValue": 213.0 }, "loss": { "doubleValue": 0.7854340076446533 } } } }, "slice": "trip_start_hour:10" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8436018957345972 }, "example_count": { "doubleValue": 211.0 }, "loss": { "doubleValue": 0.8283379077911377 } } } }, "slice": "trip_start_hour:9" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.9174757281553398 }, "example_count": { "doubleValue": 206.0 }, "loss": { "doubleValue": 0.6498335599899292 } } } }, "slice": "trip_start_hour:0" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.903448275862069 }, "example_count": { "doubleValue": 145.0 }, "loss": { "doubleValue": 1.1471656560897827 } } } }, "slice": "trip_start_hour:2" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.884 }, "example_count": { "doubleValue": 250.0 }, "loss": { "doubleValue": 0.9394456744194031 } } } }, "slice": "trip_start_hour:15" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.860655737704918 }, "example_count": { "doubleValue": 244.0 }, "loss": { "doubleValue": 1.0552647113800049 } } } }, "slice": "trip_start_hour:12" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8878504672897196 }, "example_count": { "doubleValue": 214.0 }, "loss": { "doubleValue": 0.8450745940208435 } } } }, "slice": "trip_start_hour:11" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8807947019867549 }, "example_count": { "doubleValue": 302.0 }, "loss": { "doubleValue": 1.0075381994247437 } } } }, "slice": "trip_start_hour:20" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.894927536231884 }, "example_count": { "doubleValue": 276.0 }, "loss": { "doubleValue": 0.7288517355918884 } } } }, "slice": "trip_start_hour:22" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8482490272373541 }, "example_count": { "doubleValue": 257.0 }, "loss": { "doubleValue": 1.4192315340042114 } } } }, "slice": "trip_start_hour:17" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.9058441558441559 }, "example_count": { "doubleValue": 308.0 }, "loss": { "doubleValue": 0.9150420427322388 } } } }, "slice": "trip_start_hour:21" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.859375 }, "example_count": { "doubleValue": 256.0 }, "loss": { "doubleValue": 1.0757791996002197 } } } }, "slice": "trip_start_hour:13" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.9045643153526971 }, "example_count": { "doubleValue": 241.0 }, "loss": { "doubleValue": 0.906991183757782 } } } }, "slice": "trip_start_hour:23" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8642384105960265 }, "example_count": { "doubleValue": 302.0 }, "loss": { "doubleValue": 1.0720185041427612 } } } }, "slice": "trip_start_hour:18" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8305084745762712 }, "example_count": { "doubleValue": 236.0 }, "loss": { "doubleValue": 1.1061257123947144 } } } }, "slice": "trip_start_hour:14" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.83 }, "example_count": { "doubleValue": 100.0 }, "loss": { "doubleValue": 1.4133607149124146 } } } }, "slice": "trip_start_hour:3" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8823529411764706 }, "example_count": { "doubleValue": 170.0 }, "loss": { "doubleValue": 1.2222793102264404 } } } }, "slice": "trip_start_hour:8" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.9032258064516129 }, "example_count": { "doubleValue": 62.0 }, "loss": { "doubleValue": 1.0278323888778687 } } } }, "slice": "trip_start_hour:4" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8225806451612904 }, "example_count": { "doubleValue": 248.0 }, "loss": { "doubleValue": 1.1323517560958862 } } } }, "slice": "trip_start_hour:16" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.7735849056603774 }, "example_count": { "doubleValue": 53.0 }, "loss": { "doubleValue": 1.5400522947311401 } } } }, "slice": "trip_start_hour:5" }, { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.9180327868852459 }, "example_count": { "doubleValue": 61.0 }, "loss": { "doubleValue": 1.2621021270751953 } } } }, "slice": "trip_start_hour:6" } ], "js_events": [], "layout": "IPY_MODEL_0abd1f70029f44ef87800295a765a112" } }, "0abd1f70029f44ef87800295a765a112": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "27a60e99f305419795bad0dbb65bf834": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e2b1cd9f121342649f9a59401460ff97": { "model_module": "tensorflow_model_analysis", "model_module_version": "0.46.0", "model_name": "SlicingMetricsModel", "state": { "_dom_classes": [], "_model_module": "tensorflow_model_analysis", "_model_module_version": "0.46.0", "_model_name": "SlicingMetricsModel", "_view_count": null, "_view_module": "tensorflow_model_analysis", "_view_module_version": "0.46.0", "_view_name": "SlicingMetricsView", "config": { "weightedExamplesColumn": "example_count" }, "data": [ { "metrics": { "": { "": { "binary_accuracy": { "doubleValue": 0.8731373338703181 }, "example_count": { "doubleValue": 4966.0 }, "loss": { "doubleValue": 1.009200096130371 } } } }, "slice": "Overall" } ], "js_events": [], "layout": "IPY_MODEL_27a60e99f305419795bad0dbb65bf834" } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 0 }