# Copyright 2015 The TensorFlow Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""IoU metrics."""

from typing import List

from typing import Optional

from typing import Tuple

from typing import Union

import numpy as np

import tensorflow.compat.v2 as tf

from keras import backend

from keras.dtensor import utils as dtensor_utils

from keras.metrics import base_metric

# isort: off

from tensorflow.python.util.tf_export import keras_export

class _IoUBase(base_metric.Metric):

"""Computes the confusion matrix for Intersection-Over-Union metrics.

Intersection-Over-Union is a common evaluation metric for semantic image

segmentation.

For an individual class, the IoU metric is defined as follows:

```

iou = true_positives / (true_positives + false_positives + false_negatives)

```

From IoUs of individual classes, the MeanIoU can be computed as the mean of

the individual IoUs.

To compute IoUs, the predictions are accumulated in a confusion matrix,

weighted by `sample_weight` and the metric is then calculated from it.

If `sample_weight` is `None`, weights default to 1.

Use `sample_weight` of 0 to mask values.

Args:

num_classes: The possible number of labels the prediction task can have.

This value must be provided, since a confusion matrix of size

`(num_classes, num_classes)` will be allocated.

name: (Optional) string name of the metric instance.

dtype: (Optional) data type of the metric result.

ignore_class: Optional integer. The ID of a class to be ignored during

metric computation. This is useful, for example, in segmentation

problems featuring a "void" class (commonly -1 or 255) in segmentation

maps. By default (`ignore_class=None`), all classes are considered.

sparse_y_true: Whether labels are encoded using integers or

dense floating point vectors. If `False`, the `tf.argmax` function

will be used to determine each sample's most likely associated label.

sparse_y_pred: Whether predictions are encoded using integers or

dense floating point vectors. If `False`, the `tf.argmax` function

will be used to determine each sample's most likely associated label.

axis: (Optional) Defaults to -1. The dimension containing the logits.

"""

def __init__(

self,

num_classes: int,

name: Optional[str] = None,

dtype: Optional[Union[str, tf.dtypes.DType]] = None,

ignore_class: Optional[int] = None,

sparse_y_true: bool = True,

sparse_y_pred: bool = True,

axis: int = -1,

):

super().__init__(name=name, dtype=dtype)

self.num_classes = num_classes

self.ignore_class = ignore_class

self.sparse_y_true = sparse_y_true

self.sparse_y_pred = sparse_y_pred

self.axis = axis

# Variable to accumulate the predictions in the confusion matrix.

self.total_cm = self.add_weight(

"total_confusion_matrix",

shape=(num_classes, num_classes),

initializer="zeros",

)

def update_state(self, y_true, y_pred, sample_weight=None):

"""Accumulates the confusion matrix statistics.

Args:

y_true: The ground truth values.

y_pred: The predicted values.

sample_weight: Optional weighting of each example. Defaults to 1. Can

be a `Tensor` whose rank is either 0, or the same rank as `y_true`,

and must be broadcastable to `y_true`.

Returns:

Update op.

"""

if not self.sparse_y_true:

y_true = tf.argmax(y_true, axis=self.axis)

if not self.sparse_y_pred:

y_pred = tf.argmax(y_pred, axis=self.axis)

y_true = tf.cast(y_true, self._dtype)

y_pred = tf.cast(y_pred, self._dtype)

# Flatten the input if its rank > 1.

if y_pred.shape.ndims > 1:

y_pred = tf.reshape(y_pred, [-1])

if y_true.shape.ndims > 1:

y_true = tf.reshape(y_true, [-1])

if sample_weight is not None:

sample_weight = tf.cast(sample_weight, self._dtype)

if sample_weight.shape.ndims > 1:

sample_weight = tf.reshape(sample_weight, [-1])

if self.ignore_class is not None:

ignore_class = tf.cast(self.ignore_class, y_true.dtype)

valid_mask = tf.not_equal(y_true, ignore_class)

y_true = y_true[valid_mask]

y_pred = y_pred[valid_mask]

if sample_weight is not None:

sample_weight = sample_weight[valid_mask]

# Accumulate the prediction to current confusion matrix.

current_cm = tf.math.confusion_matrix(

y_true,

y_pred,

self.num_classes,

weights=sample_weight,

dtype=self._dtype,

)

return self.total_cm.assign_add(current_cm)

def reset_state(self):

backend.set_value(

self.total_cm, np.zeros((self.num_classes, self.num_classes))

)

@keras_export("keras.metrics.IoU")

class IoU(_IoUBase):

"""Computes the Intersection-Over-Union metric for specific target classes.

General definition and computation:

Intersection-Over-Union is a common evaluation metric for semantic image

segmentation.

For an individual class, the IoU metric is defined as follows:

```

iou = true_positives / (true_positives + false_positives + false_negatives)

```

To compute IoUs, the predictions are accumulated in a confusion matrix,

weighted by `sample_weight` and the metric is then calculated from it.

If `sample_weight` is `None`, weights default to 1.

Use `sample_weight` of 0 to mask values.

Note, this class first computes IoUs for all individual classes, then

returns the mean of IoUs for the classes that are specified by

`target_class_ids`. If `target_class_ids` has only one id value, the IoU of

that specific class is returned.

Args:

num_classes: The possible number of labels the prediction task can have.

A confusion matrix of dimension = [num_classes, num_classes] will be

allocated to accumulate predictions from which the metric is calculated.

target_class_ids: A tuple or list of target class ids for which the metric

is returned. To compute IoU for a specific class, a list (or tuple) of a

single id value should be provided.

name: (Optional) string name of the metric instance.

dtype: (Optional) data type of the metric result.

ignore_class: Optional integer. The ID of a class to be ignored during

metric computation. This is useful, for example, in segmentation

problems featuring a "void" class (commonly -1 or 255) in segmentation

maps. By default (`ignore_class=None`), all classes are considered.

sparse_y_true: Whether labels are encoded using integers or

dense floating point vectors. If `False`, the `tf.argmax` function

will be used to determine each sample's most likely associated label.

sparse_y_pred: Whether predictions are encoded using integers or

dense floating point vectors. If `False`, the `tf.argmax` function

will be used to determine each sample's most likely associated label.

axis: (Optional) Defaults to -1. The dimension containing the logits.

Standalone usage:

>>> # cm = [[1, 1],

>>> # [1, 1]]

>>> # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1]

>>> # iou = true_positives / (sum_row + sum_col - true_positives))

>>> # iou = [0.33, 0.33]

>>> m = tf.keras.metrics.IoU(num_classes=2, target_class_ids=[0])

>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1])

>>> m.result().numpy()

0.33333334

>>> m.reset_state()

>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1],

... sample_weight=[0.3, 0.3, 0.3, 0.1])

>>> # cm = [[0.3, 0.3],

>>> # [0.3, 0.1]]

>>> # sum_row = [0.6, 0.4], sum_col = [0.6, 0.4],

>>> # true_positives = [0.3, 0.1]

>>> # iou = [0.33, 0.14]

>>> m.result().numpy()

0.33333334

Usage with `compile()` API:

```python

model.compile(

optimizer='sgd',

loss='mse',

metrics=[tf.keras.metrics.IoU(num_classes=2, target_class_ids=[0])])

```

"""

@dtensor_utils.inject_mesh

def __init__(

self,

num_classes: int,

target_class_ids: Union[List[int], Tuple[int, ...]],

name: Optional[str] = None,

dtype: Optional[Union[str, tf.dtypes.DType]] = None,

ignore_class: Optional[int] = None,

sparse_y_true: bool = True,

sparse_y_pred: bool = True,

axis: int = -1,

):

super().__init__(

name=name,

num_classes=num_classes,

ignore_class=ignore_class,

sparse_y_true=sparse_y_true,

sparse_y_pred=sparse_y_pred,

axis=axis,

dtype=dtype,

)

if max(target_class_ids) >= num_classes:

raise ValueError(

f"Target class id {max(target_class_ids)} "

"is out of range, which is "

f"[{0}, {num_classes})."

)

self.target_class_ids = list(target_class_ids)

def result(self):

"""Compute the intersection-over-union via the confusion matrix."""

sum_over_row = tf.cast(

tf.reduce_sum(self.total_cm, axis=0), dtype=self._dtype

)

sum_over_col = tf.cast(

tf.reduce_sum(self.total_cm, axis=1), dtype=self._dtype

)

true_positives = tf.cast(

tf.linalg.tensor_diag_part(self.total_cm), dtype=self._dtype

)

# sum_over_row + sum_over_col =

# 2 * true_positives + false_positives + false_negatives.

denominator = sum_over_row + sum_over_col - true_positives

# Only keep the target classes

true_positives = tf.gather(true_positives, self.target_class_ids)

denominator = tf.gather(denominator, self.target_class_ids)

# If the denominator is 0, we need to ignore the class.

num_valid_entries = tf.reduce_sum(

tf.cast(tf.not_equal(denominator, 0), dtype=self._dtype)

)

iou = tf.math.divide_no_nan(true_positives, denominator)

return tf.math.divide_no_nan(

tf.reduce_sum(iou, name="mean_iou"), num_valid_entries

)

def get_config(self):

config = {

"num_classes": self.num_classes,

"target_class_ids": self.target_class_ids,

"ignore_class": self.ignore_class,

"sparse_y_true": self.sparse_y_true,

"sparse_y_pred": self.sparse_y_pred,

"axis": self.axis,

}

base_config = super().get_config()

return dict(list(base_config.items()) + list(config.items()))

@keras_export("keras.metrics.BinaryIoU")

class BinaryIoU(IoU):

"""Computes the Intersection-Over-Union metric for class 0 and/or 1.

General definition and computation:

Intersection-Over-Union is a common evaluation metric for semantic image

segmentation.

For an individual class, the IoU metric is defined as follows:

```

iou = true_positives / (true_positives + false_positives + false_negatives)

```

To compute IoUs, the predictions are accumulated in a confusion matrix,

weighted by `sample_weight` and the metric is then calculated from it.

If `sample_weight` is `None`, weights default to 1.

Use `sample_weight` of 0 to mask values.

This class can be used to compute IoUs for a binary classification task

where the predictions are provided as logits. First a `threshold` is applied

to the predicted values such that those that are below the `threshold` are

converted to class 0 and those that are above the `threshold` are converted

to class 1.

IoUs for classes 0 and 1 are then computed, the mean of IoUs for the classes

that are specified by `target_class_ids` is returned.

Note: with `threshold=0`, this metric has the same behavior as `IoU`.

Args:

target_class_ids: A tuple or list of target class ids for which the metric

is returned. Options are `[0]`, `[1]`, or `[0, 1]`. With `[0]` (or

`[1]`), the IoU metric for class 0 (or class 1, respectively) is

returned. With `[0, 1]`, the mean of IoUs for the two classes is

returned.

threshold: A threshold that applies to the prediction logits to convert

them to either predicted class 0 if the logit is below `threshold` or

predicted class 1 if the logit is above `threshold`.

name: (Optional) string name of the metric instance.

dtype: (Optional) data type of the metric result.

Standalone usage:

>>> m = tf.keras.metrics.BinaryIoU(target_class_ids=[0, 1], threshold=0.3)

>>> m.update_state([0, 1, 0, 1], [0.1, 0.2, 0.4, 0.7])

>>> m.result().numpy()

0.33333334

>>> m.reset_state()

>>> m.update_state([0, 1, 0, 1], [0.1, 0.2, 0.4, 0.7],

... sample_weight=[0.2, 0.3, 0.4, 0.1])

>>> # cm = [[0.2, 0.4],

>>> # [0.3, 0.1]]

>>> # sum_row = [0.6, 0.4], sum_col = [0.5, 0.5],

>>> # true_positives = [0.2, 0.1]

>>> # iou = [0.222, 0.125]

>>> m.result().numpy()

0.17361112

Usage with `compile()` API:

```python

model.compile(

optimizer='sgd',

loss='mse',

metrics=[tf.keras.metrics.BinaryIoU(target_class_ids=[0], threshold=0.5)])

```

"""

@dtensor_utils.inject_mesh

def __init__(

self,

target_class_ids: Union[List[int], Tuple[int, ...]] = (0, 1),

threshold=0.5,

name=None,

dtype=None,

):

super().__init__(

num_classes=2,

target_class_ids=target_class_ids,

name=name,

dtype=dtype,

)

self.threshold = threshold

def update_state(self, y_true, y_pred, sample_weight=None):

"""Accumulates the confusion matrix statistics.

Before the confusion matrix is updated, the predicted values are

thresholded to be:

0 for values that are smaller than the `threshold`

1 for values that are larger or equal to the `threshold`

Args:

y_true: The ground truth values.

y_pred: The predicted values.

sample_weight: Optional weighting of each example. Defaults to 1. Can

be a `Tensor` whose rank is either 0, or the same rank as `y_true`,

and must be broadcastable to `y_true`.

Returns:

Update op.

"""

y_pred = tf.cast(y_pred, self._dtype)

y_pred = tf.cast(y_pred >= self.threshold, self._dtype)

return super().update_state(y_true, y_pred, sample_weight)

def get_config(self):

return {

"target_class_ids": self.target_class_ids,

"threshold": self.threshold,

"name": self.name,

"dtype": self._dtype,

}

@keras_export("keras.metrics.MeanIoU")

class MeanIoU(IoU):

"""Computes the mean Intersection-Over-Union metric.

General definition and computation:

Intersection-Over-Union is a common evaluation metric for semantic image

segmentation.

For an individual class, the IoU metric is defined as follows:

```

iou = true_positives / (true_positives + false_positives + false_negatives)

```

To compute IoUs, the predictions are accumulated in a confusion matrix,

weighted by `sample_weight` and the metric is then calculated from it.

If `sample_weight` is `None`, weights default to 1.

Use `sample_weight` of 0 to mask values.

Note that this class first computes IoUs for all individual classes, then

returns the mean of these values.

Args:

num_classes: The possible number of labels the prediction task can have.

This value must be provided, since a confusion matrix of dimension =

[num_classes, num_classes] will be allocated.

name: (Optional) string name of the metric instance.

dtype: (Optional) data type of the metric result.

ignore_class: Optional integer. The ID of a class to be ignored during

metric computation. This is useful, for example, in segmentation

problems featuring a "void" class (commonly -1 or 255) in segmentation

maps. By default (`ignore_class=None`), all classes are considered.

sparse_y_true: Whether labels are encoded using integers or

dense floating point vectors. If `False`, the `tf.argmax` function

will be used to determine each sample's most likely associated label.

sparse_y_pred: Whether predictions are encoded using integers or

dense floating point vectors. If `False`, the `tf.argmax` function

will be used to determine each sample's most likely associated label.

axis: (Optional) Defaults to -1. The dimension containing the logits.

Standalone usage:

>>> # cm = [[1, 1],

>>> # [1, 1]]

>>> # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1]

>>> # iou = true_positives / (sum_row + sum_col - true_positives))

>>> # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33

>>> m = tf.keras.metrics.MeanIoU(num_classes=2)

>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1])

>>> m.result().numpy()

0.33333334

>>> m.reset_state()

>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1],

... sample_weight=[0.3, 0.3, 0.3, 0.1])

>>> m.result().numpy()

0.23809525

Usage with `compile()` API:

```python

model.compile(

optimizer='sgd',

loss='mse',

metrics=[tf.keras.metrics.MeanIoU(num_classes=2)])

```

"""

@dtensor_utils.inject_mesh

def __init__(

self,

num_classes: int,

name: Optional[str] = None,

dtype: Optional[Union[str, tf.dtypes.DType]] = None,

ignore_class: Optional[int] = None,

sparse_y_true: bool = True,

sparse_y_pred: bool = True,

axis: int = -1,

):

target_class_ids = list(range(num_classes))

super().__init__(

name=name,

num_classes=num_classes,

target_class_ids=target_class_ids,

axis=axis,

dtype=dtype,

ignore_class=ignore_class,

sparse_y_true=sparse_y_true,

sparse_y_pred=sparse_y_pred,

)

def get_config(self):

return {

"num_classes": self.num_classes,

"name": self.name,

"dtype": self._dtype,

"ignore_class": self.ignore_class,

"sparse_y_true": self.sparse_y_true,

"sparse_y_pred": self.sparse_y_pred,

"axis": self.axis,

}

@keras_export("keras.metrics.OneHotIoU")

class OneHotIoU(IoU):

"""Computes the Intersection-Over-Union metric for one-hot encoded labels.

General definition and computation:

Intersection-Over-Union is a common evaluation metric for semantic image

segmentation.

For an individual class, the IoU metric is defined as follows:

```

iou = true_positives / (true_positives + false_positives + false_negatives)

```

To compute IoUs, the predictions are accumulated in a confusion matrix,

weighted by `sample_weight` and the metric is then calculated from it.

If `sample_weight` is `None`, weights default to 1.

Use `sample_weight` of 0 to mask values.

This class can be used to compute IoU for multi-class classification tasks

where the labels are one-hot encoded (the last axis should have one

dimension per class). Note that the predictions should also have the same

shape. To compute the IoU, first the labels and predictions are converted

back into integer format by taking the argmax over the class axis. Then the

same computation steps as for the base `IoU` class apply.

Note, if there is only one channel in the labels and predictions, this class

is the same as class `IoU`. In this case, use `IoU` instead.

Also, make sure that `num_classes` is equal to the number of classes in the

data, to avoid a "labels out of bound" error when the confusion matrix is

computed.

Args:

num_classes: The possible number of labels the prediction task can have.

A confusion matrix of shape `(num_classes, num_classes)` will be

allocated to accumulate predictions from which the metric is calculated.

target_class_ids: A tuple or list of target class ids for which the metric

is returned. To compute IoU for a specific class, a list (or tuple) of a

single id value should be provided.

name: (Optional) string name of the metric instance.

dtype: (Optional) data type of the metric result.

ignore_class: Optional integer. The ID of a class to be ignored during

metric computation. This is useful, for example, in segmentation

problems featuring a "void" class (commonly -1 or 255) in segmentation

maps. By default (`ignore_class=None`), all classes are considered.

sparse_y_pred: Whether predictions are encoded using natural numbers or

probability distribution vectors. If `False`, the `tf.argmax` function

will be used to determine each sample's most likely associated label.

axis: (Optional) Defaults to -1. The dimension containing the logits.

Standalone usage:

>>> y_true = tf.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]])

>>> y_pred = tf.constant([[0.2, 0.3, 0.5], [0.1, 0.2, 0.7], [0.5, 0.3, 0.1],

... [0.1, 0.4, 0.5]])

>>> sample_weight = [0.1, 0.2, 0.3, 0.4]

>>> m = tf.keras.metrics.OneHotIoU(num_classes=3, target_class_ids=[0, 2])

>>> m.update_state(

... y_true=y_true, y_pred=y_pred, sample_weight=sample_weight)

>>> # cm = [[0, 0, 0.2+0.4],

>>> # [0.3, 0, 0],

>>> # [0, 0, 0.1]]

>>> # sum_row = [0.3, 0, 0.7], sum_col = [0.6, 0.3, 0.1]

>>> # true_positives = [0, 0, 0.1]

>>> # single_iou = true_positives / (sum_row + sum_col - true_positives))

>>> # mean_iou = (0 / (0.3 + 0.6 - 0) + 0.1 / (0.7 + 0.1 - 0.1)) / 2

>>> m.result().numpy()

0.071

Usage with `compile()` API:

```python

model.compile(

optimizer='sgd',

loss='mse',

metrics=[tf.keras.metrics.OneHotIoU(num_classes=3, target_class_id=[1])])

```

"""

@dtensor_utils.inject_mesh

def __init__(

self,

num_classes: int,

target_class_ids: Union[List[int], Tuple[int, ...]],

name=None,

dtype=None,

ignore_class: Optional[int] = None,

sparse_y_pred: bool = False,

axis: int = -1,

):

super().__init__(

num_classes=num_classes,

target_class_ids=target_class_ids,

name=name,

dtype=dtype,

ignore_class=ignore_class,

sparse_y_true=False,

sparse_y_pred=sparse_y_pred,

axis=axis,

)

def get_config(self):

return {

"num_classes": self.num_classes,

"target_class_ids": self.target_class_ids,

"name": self.name,

"dtype": self._dtype,

"ignore_class": self.ignore_class,

"sparse_y_pred": self.sparse_y_pred,

"axis": self.axis,

}

@keras_export("keras.metrics.OneHotMeanIoU")

class OneHotMeanIoU(MeanIoU):

"""Computes mean Intersection-Over-Union metric for one-hot encoded labels.

General definition and computation:

Intersection-Over-Union is a common evaluation metric for semantic image

segmentation.

For an individual class, the IoU metric is defined as follows:

```

iou = true_positives / (true_positives + false_positives + false_negatives)

```

To compute IoUs, the predictions are accumulated in a confusion matrix,

weighted by `sample_weight` and the metric is then calculated from it.

If `sample_weight` is `None`, weights default to 1.

Use `sample_weight` of 0 to mask values.

This class can be used to compute the mean IoU for multi-class

classification tasks where the labels are one-hot encoded (the last axis

should have one dimension per class). Note that the predictions should also

have the same shape. To compute the mean IoU, first the labels and

predictions are converted back into integer format by taking the argmax over

the class axis. Then the same computation steps as for the base `MeanIoU`

class apply.

Note, if there is only one channel in the labels and predictions, this class

is the same as class `MeanIoU`. In this case, use `MeanIoU` instead.

Also, make sure that `num_classes` is equal to the number of classes in the

data, to avoid a "labels out of bound" error when the confusion matrix is

computed.

Args:

num_classes: The possible number of labels the prediction task can have.

A confusion matrix of shape `(num_classes, num_classes)` will be

allocated to accumulate predictions from which the metric is calculated.

name: (Optional) string name of the metric instance.

dtype: (Optional) data type of the metric result.

ignore_class: Optional integer. The ID of a class to be ignored during

metric computation. This is useful, for example, in segmentation

problems featuring a "void" class (commonly -1 or 255) in segmentation

maps. By default (`ignore_class=None`), all classes are considered.

sparse_y_pred: Whether predictions are encoded using natural numbers or

probability distribution vectors. If `False`, the `tf.argmax` function

will be used to determine each sample's most likely associated label.

axis: (Optional) Defaults to -1. The dimension containing the logits.

Standalone usage:

>>> y_true = tf.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]])

>>> y_pred = tf.constant([[0.2, 0.3, 0.5], [0.1, 0.2, 0.7], [0.5, 0.3, 0.1],

... [0.1, 0.4, 0.5]])

>>> sample_weight = [0.1, 0.2, 0.3, 0.4]

>>> m = tf.keras.metrics.OneHotMeanIoU(num_classes=3)

>>> m.update_state(

... y_true=y_true, y_pred=y_pred, sample_weight=sample_weight)

>>> # cm = [[0, 0, 0.2+0.4],

>>> # [0.3, 0, 0],

>>> # [0, 0, 0.1]]

>>> # sum_row = [0.3, 0, 0.7], sum_col = [0.6, 0.3, 0.1]

>>> # true_positives = [0, 0, 0.1]

>>> # single_iou = true_positives / (sum_row + sum_col - true_positives))

>>> # mean_iou = (0 + 0 + 0.1 / (0.7 + 0.1 - 0.1)) / 3

>>> m.result().numpy()

0.048

Usage with `compile()` API:

```python

model.compile(

optimizer='sgd',

loss='mse',

metrics=[tf.keras.metrics.OneHotMeanIoU(num_classes=3)])

```

"""

@dtensor_utils.inject_mesh

def __init__(

self,

num_classes: int,

name: str = None,

dtype: Optional[Union[str, tf.dtypes.DType]] = None,

ignore_class: Optional[int] = None,

sparse_y_pred: bool = False,

axis: int = -1,

):

super().__init__(

num_classes=num_classes,

axis=axis,

name=name,

dtype=dtype,

ignore_class=ignore_class,

sparse_y_true=False,

sparse_y_pred=sparse_y_pred,

)

def get_config(self):

return {

"num_classes": self.num_classes,

"name": self.name,

"dtype": self._dtype,

"ignore_class": self.ignore_class,

"sparse_y_pred": self.sparse_y_pred,

"axis": self.axis,

}