tf.estimator.BaselineClassifier

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A classifier that can establish a simple baseline. (deprecated)

Inherits From: Estimator, Estimator

tf.estimator.BaselineClassifier(
    model_dir=None,
    n_classes=2,
    weight_column=None,
    label_vocabulary=None,
    optimizer='Ftrl',
    config=None,
    loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE
)

This classifier ignores feature values and will learn to predict the average value of each label. For single-label problems, this will predict the probability distribution of the classes as seen in the labels. For multi-label problems, this will predict the fraction of examples that are positive for each class.

Example:

# Build BaselineClassifier
classifier = tf.estimator.BaselineClassifier(n_classes=3)

# Input builders
def input_fn_train:
  # Returns tf.data.Dataset of (x, y) tuple where y represents label's class
  # index.
  pass

def input_fn_eval:
  # Returns tf.data.Dataset of (x, y) tuple where y represents label's class
  # index.
  pass

# Fit model.
classifier.train(input_fn=input_fn_train)

# Evaluate cross entropy between the test and train labels.
loss = classifier.evaluate(input_fn=input_fn_eval)["loss"]

# predict outputs the probability distribution of the classes as seen in
# training.
predictions = classifier.predict(new_samples)

Input of train and evaluate should have following features, otherwise there will be a KeyError:

• if weight_column is not None, a feature with key=weight_column whose value is a Tensor.

model_dir Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. n_classes number of label classes. Default is binary classification. It must be greater than 1. Note: Class labels are integers representing the class index (i.e. values from 0 to n_classes-1). For arbitrary label values (e.g. string labels), convert to class indices first. weight_column A string or a NumericColumn created by tf.feature_column.numeric_column defining feature column representing weights. It will be multiplied by the loss of the example. label_vocabulary Optional list of strings with size [n_classes] defining the label vocabulary. Only supported for n_classes > 2. optimizer String, tf.keras.optimizers.* object, or callable that creates the optimizer to use for training. If not specified, will use Ftrl as the default optimizer. config RunConfig object to configure the runtime settings. loss_reduction One of tf.losses.Reduction except NONE. Describes how to reduce training loss over batch. Defaults to SUM_OVER_BATCH_SIZE.

ValueError If n_classes < 2.

config

export_savedmodel

model_dir

model_fn Returns the model_fn which is bound to self.params. params

Methods

eval_dir

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eval_dir(
    name=None
)

Shows the directory name where evaluation metrics are dumped.

name Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

evaluate

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evaluate(
    input_fn, steps=None, hooks=None, checkpoint_path=None, name=None
)

Evaluates the model given evaluation data input_fn.

For each step, calls input_fn, which returns one batch of data. Evaluates until:

• steps batches are processed, or
• input_fn raises an end-of-input exception (tf.errors.OutOfRangeError or StopIteration).

input_fn A function that constructs the input data for evaluation. See Premade Estimators for more information. The function should construct and return one of the following:

• A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
• A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs. steps Number of steps for which to evaluate model. If None, evaluates until input_fn raises an end-of-input exception. hooks List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the evaluation call. checkpoint_path Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, evaluation is run with newly initialized Variables instead of ones restored from checkpoint. name Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

ValueError If steps <= 0.

experimental_export_all_saved_models

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experimental_export_all_saved_models(
    export_dir_base,
    input_receiver_fn_map,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None
)

Exports a SavedModel with tf.MetaGraphDefs for each requested mode.

For each mode passed in via the input_receiver_fn_map, this method builds a new graph by calling the input_receiver_fn to obtain feature and label Tensors. Next, this method calls the Estimator's model_fn in the passed mode to generate the model graph based on those features and labels, and restores the given checkpoint (or, lacking that, the most recent checkpoint) into the graph. Only one of the modes is used for saving variables to the SavedModel (order of preference: tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, then tf.estimator.ModeKeys.PREDICT), such that up to three tf.MetaGraphDefs are saved with a single set of variables in a single SavedModel directory.

For the variables and tf.MetaGraphDefs, a timestamped export directory below export_dir_base, and writes a SavedModel into it containing the tf.MetaGraphDef for the given mode and its associated signatures.

For prediction, the exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

For training and evaluation, the train_op is stored in an extra collection, and loss, metrics, and predictions are included in a SignatureDef for the mode in question.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

export_dir_base A string containing a directory in which to create timestamped subdirectories containing exported SavedModels. input_receiver_fn_map dict of tf.estimator.ModeKeys to input_receiver_fn mappings, where the input_receiver_fn is a function that takes no arguments and returns the appropriate subclass of InputReceiver. assets_extra A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed. as_text whether to write the SavedModel proto in text format. checkpoint_path The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.

ValueError if any input_receiver_fn is None, no export_outputs are provided, or no checkpoint can be found.

export_saved_model

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export_saved_model(
    export_dir_base,
    serving_input_receiver_fn,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None,
    experimental_mode=ModeKeys.PREDICT
)

Exports inference graph as a SavedModel into the given dir.

For a detailed guide on SavedModel, see Using the SavedModel format.

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single tf.MetaGraphDef saved from this session.

The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

The experimental_mode parameter can be used to export a single train/eval/predict graph as a SavedModel. See experimental_export_all_saved_models for full docs.

export_dir_base A string containing a directory in which to create timestamped subdirectories containing exported SavedModels. serving_input_receiver_fn A function that takes no argument and returns a tf.estimator.export.ServingInputReceiver or tf.estimator.export.TensorServingInputReceiver. assets_extra A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed. as_text whether to write the SavedModel proto in text format. checkpoint_path The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen. experimental_mode tf.estimator.ModeKeys value indicating with mode will be exported. Note that this feature is experimental.

ValueError if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found.

get_variable_names

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get_variable_names()

Returns list of all variable names in this model.

ValueError If the Estimator has not produced a checkpoint yet.

get_variable_value

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get_variable_value(
    name
)

Returns value of the variable given by name.

name string or a list of string, name of the tensor.

ValueError If the Estimator has not produced a checkpoint yet.

latest_checkpoint

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latest_checkpoint()

Finds the filename of the latest saved checkpoint file in model_dir.

predict

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predict(
    input_fn,
    predict_keys=None,
    hooks=None,
    checkpoint_path=None,
    yield_single_examples=True
)

Yields predictions for given features.

Please note that interleaving two predict outputs does not work. See: issue/20506

input_fn A function that constructs the features. Prediction continues until input_fn raises an end-of-input exception (tf.errors.OutOfRangeError or StopIteration). See Premade Estimators for more information. The function should construct and return one of the following:

• tf.data.Dataset object -- Outputs of Dataset object must have same constraints as below.
• features -- A tf.Tensor or a dictionary of string feature name to Tensor. features are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
• A tuple, in which case the first item is extracted as features. predict_keys list of str, name of the keys to predict. It is used if the tf.estimator.EstimatorSpec.predictions is a dict. If predict_keys is used then rest of the predictions will be filtered from the dictionary. If None, returns all. hooks List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the prediction call. checkpoint_path Path of a specific checkpoint to predict. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, prediction is run with newly initialized Variables instead of ones restored from checkpoint. yield_single_examples If False, yields the whole batch as returned by the model_fn instead of decomposing the batch into individual elements. This is useful if model_fn returns some tensors whose first dimension is not equal to the batch size.

ValueError If batch length of predictions is not the same and yield_single_examples is True. ValueError If there is a conflict between predict_keys and predictions. For example if predict_keys is not None but tf.estimator.EstimatorSpec.predictions is not a dict.

train

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train(
    input_fn, hooks=None, steps=None, max_steps=None, saving_listeners=None
)

Trains a model given training data input_fn.

input_fn A function that provides input data for training as minibatches. See Premade Estimators for more information. The function should construct and return one of the following:

• A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
• A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs. hooks List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the training loop. steps Number of steps for which to train the model. If None, train forever or train until input_fn generates the tf.errors.OutOfRange error or StopIteration exception. steps works incrementally. If you call two times train(steps=10) then training occurs in total 20 steps. If OutOfRange or StopIteration occurs in the middle, training stops before 20 steps. If you don't want to have incremental behavior please set max_steps instead. If set, max_steps must be None. max_steps Number of total steps for which to train model. If None, train forever or train until input_fn generates the tf.errors.OutOfRange error or StopIteration exception. If set, steps must be None. If OutOfRange or StopIteration occurs in the middle, training stops before max_steps steps. Two calls to train(steps=100) means 200 training iterations. On the other hand, two calls to train(max_steps=100) means that the second call will not do any iteration since first call did all 100 steps. saving_listeners list of CheckpointSaverListener objects. Used for callbacks that run immediately before or after checkpoint savings.

ValueError If both steps and max_steps are not None. ValueError If either steps or max_steps <= 0.

Estimators can be used while eager execution is enabled. Note that input_fn and all hooks are executed inside a graph context, so they have to be written to be compatible with graph mode. Note that input_fn code using tf.data generally works in both graph and eager modes.