xref: /aosp_15_r20/external/tensorflow/tensorflow/python/keras/metrics.py (revision b6fb3261f9314811a0f4371741dbb8839866f948)

# 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.
# ==============================================================================
# pylint: disable=g-classes-have-attributes
# pylint: disable=g-doc-return-or-yield
"""Built-in metrics."""

import abc
import types
import warnings

import numpy as np

from tensorflow.python.autograph.core import ag_ctx
from tensorflow.python.autograph.impl import api as autograph
from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras import activations
from tensorflow.python.keras import backend
from tensorflow.python.keras.engine import base_layer
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.keras.engine import keras_tensor
from tensorflow.python.keras.losses import binary_crossentropy
from tensorflow.python.keras.losses import categorical_crossentropy
from tensorflow.python.keras.losses import categorical_hinge
from tensorflow.python.keras.losses import hinge
from tensorflow.python.keras.losses import kullback_leibler_divergence
from tensorflow.python.keras.losses import logcosh
from tensorflow.python.keras.losses import mean_absolute_error
from tensorflow.python.keras.losses import mean_absolute_percentage_error
from tensorflow.python.keras.losses import mean_squared_error
from tensorflow.python.keras.losses import mean_squared_logarithmic_error
from tensorflow.python.keras.losses import poisson
from tensorflow.python.keras.losses import sparse_categorical_crossentropy
from tensorflow.python.keras.losses import squared_hinge
from tensorflow.python.keras.saving.saved_model import metric_serialization
from tensorflow.python.keras.utils import generic_utils
from tensorflow.python.keras.utils import losses_utils
from tensorflow.python.keras.utils import metrics_utils
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
from tensorflow.python.keras.utils.generic_utils import to_list
from tensorflow.python.keras.utils.tf_utils import is_tensor_or_variable
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import confusion_matrix
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variables as variables_module
from tensorflow.python.ops import weights_broadcast_ops
from tensorflow.python.util import dispatch
from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import keras_export
from tensorflow.tools.docs import doc_controls


@keras_export('keras.metrics.Metric')
class Metric(base_layer.Layer, metaclass=abc.ABCMeta):
  """Encapsulates metric logic and state.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
    **kwargs: Additional layer keywords arguments.

  Standalone usage:

  ```python
  m = SomeMetric(...)
  for input in ...:
    m.update_state(input)
  print('Final result: ', m.result().numpy())
  ```

  Usage with `compile()` API:

  ```python
  model = tf.keras.Sequential()
  model.add(tf.keras.layers.Dense(64, activation='relu'))
  model.add(tf.keras.layers.Dense(64, activation='relu'))
  model.add(tf.keras.layers.Dense(10, activation='softmax'))

  model.compile(optimizer=tf.keras.optimizers.RMSprop(0.01),
                loss=tf.keras.losses.CategoricalCrossentropy(),
                metrics=[tf.keras.metrics.CategoricalAccuracy()])

  data = np.random.random((1000, 32))
  labels = np.random.random((1000, 10))

  dataset = tf.data.Dataset.from_tensor_slices((data, labels))
  dataset = dataset.batch(32)

  model.fit(dataset, epochs=10)
  ```

  To be implemented by subclasses:
  * `__init__()`: All state variables should be created in this method by
    calling `self.add_weight()` like: `self.var = self.add_weight(...)`
  * `update_state()`: Has all updates to the state variables like:
    self.var.assign_add(...).
  * `result()`: Computes and returns a value for the metric
    from the state variables.

  Example subclass implementation:

  ```python
  class BinaryTruePositives(tf.keras.metrics.Metric):

    def __init__(self, name='binary_true_positives', **kwargs):
      super(BinaryTruePositives, self).__init__(name=name, **kwargs)
      self.true_positives = self.add_weight(name='tp', initializer='zeros')

    def update_state(self, y_true, y_pred, sample_weight=None):
      y_true = tf.cast(y_true, tf.bool)
      y_pred = tf.cast(y_pred, tf.bool)

      values = tf.logical_and(tf.equal(y_true, True), tf.equal(y_pred, True))
      values = tf.cast(values, self.dtype)
      if sample_weight is not None:
        sample_weight = tf.cast(sample_weight, self.dtype)
        sample_weight = tf.broadcast_to(sample_weight, values.shape)
        values = tf.multiply(values, sample_weight)
      self.true_positives.assign_add(tf.reduce_sum(values))

    def result(self):
      return self.true_positives
  ```
  """

  def __init__(self, name=None, dtype=None, **kwargs):
    super(Metric, self).__init__(name=name, dtype=dtype, **kwargs)
    self.stateful = True  # All metric layers are stateful.
    self.built = True
    if not base_layer_utils.v2_dtype_behavior_enabled():
      # We only do this when the V2 behavior is not enabled, as when it is
      # enabled, the dtype already defaults to floatx.
      self._dtype = (backend.floatx() if dtype is None
                     else dtypes.as_dtype(dtype).name)

  def __new__(cls, *args, **kwargs):
    obj = super(Metric, cls).__new__(cls)

    # If `update_state` is not in eager/tf.function and it is not from a
    # built-in metric, wrap it in `tf.function`. This is so that users writing
    # custom metrics in v1 need not worry about control dependencies and
    # return ops.
    if (base_layer_utils.is_in_eager_or_tf_function() or
        is_built_in(cls)):
      obj_update_state = obj.update_state

      def update_state_fn(*args, **kwargs):
        control_status = ag_ctx.control_status_ctx()
        ag_update_state = autograph.tf_convert(obj_update_state, control_status)
        return ag_update_state(*args, **kwargs)
    else:
      if isinstance(obj.update_state, def_function.Function):
        update_state_fn = obj.update_state
      else:
        update_state_fn = def_function.function(obj.update_state)

    obj.update_state = types.MethodType(
        metrics_utils.update_state_wrapper(update_state_fn), obj)

    obj_result = obj.result

    def result_fn(*args, **kwargs):
      control_status = ag_ctx.control_status_ctx()
      ag_result = autograph.tf_convert(obj_result, control_status)
      return ag_result(*args, **kwargs)

    obj.result = types.MethodType(metrics_utils.result_wrapper(result_fn), obj)

    return obj

  def __call__(self, *args, **kwargs):
    """Accumulates statistics and then computes metric result value.

    Args:
      *args:
      **kwargs: A mini-batch of inputs to the Metric,
        passed on to `update_state()`.

    Returns:
      The metric value tensor.
    """

    def replica_local_fn(*args, **kwargs):
      """Updates the state of the metric in a replica-local context."""
      if any(
          isinstance(arg, keras_tensor.KerasTensor)
          for arg in nest.flatten((args, kwargs))):
        update_op = None
      else:
        update_op = self.update_state(*args, **kwargs)  # pylint: disable=not-callable
      update_ops = []
      if update_op is not None:
        update_ops.append(update_op)
      with ops.control_dependencies(update_ops):
        result_t = self.result()  # pylint: disable=not-callable

        # We are adding the metric object as metadata on the result tensor.
        # This is required when we want to use a metric with `add_metric` API on
        # a Model/Layer in graph mode. This metric instance will later be used
        # to reset variable state after each epoch of training.
        # Example:
        #   model = Model()
        #   mean = Mean()
        #   model.add_metric(mean(values), name='mean')
        result_t._metric_obj = self  # pylint: disable=protected-access
        return result_t

    from tensorflow.python.keras.distribute import distributed_training_utils  # pylint:disable=g-import-not-at-top
    return distributed_training_utils.call_replica_local_fn(
        replica_local_fn, *args, **kwargs)

  @property
  def dtype(self):
    return self._dtype

  def get_config(self):
    """Returns the serializable config of the metric."""
    return {'name': self.name, 'dtype': self.dtype}

  def reset_state(self):
    """Resets all of the metric state variables.

    This function is called between epochs/steps,
    when a metric is evaluated during training.
    """
    if not generic_utils.is_default(self.reset_states):
      warnings.warn('Metric %s implements a `reset_states()` method; rename it '
                    'to `reset_state()` (without the final "s"). The name '
                    '`reset_states()` has been deprecated to improve API '
                    'consistency.' % (self.__class__.__name__,))
      return self.reset_states()
    else:
      backend.batch_set_value([(v, 0) for v in self.variables])

  @abc.abstractmethod
  def update_state(self, *args, **kwargs):
    """Accumulates statistics for the metric.

    Note: This function is executed as a graph function in graph mode.
    This means:
      a) Operations on the same resource are executed in textual order.
         This should make it easier to do things like add the updated
         value of a variable to another, for example.
      b) You don't need to worry about collecting the update ops to execute.
         All update ops added to the graph by this function will be executed.
      As a result, code should generally work the same way with graph or
      eager execution.

    Args:
      *args:
      **kwargs: A mini-batch of inputs to the Metric.
    """
    raise NotImplementedError('Must be implemented in subclasses.')

  @abc.abstractmethod
  def result(self):
    """Computes and returns the metric value tensor.

    Result computation is an idempotent operation that simply calculates the
    metric value using the state variables.
    """
    raise NotImplementedError('Must be implemented in subclasses.')

  ### For use by subclasses ###
  @doc_controls.for_subclass_implementers
  def add_weight(
      self,
      name,
      shape=(),
      aggregation=variables_module.VariableAggregation.SUM,
      synchronization=variables_module.VariableSynchronization.ON_READ,
      initializer=None,
      dtype=None):
    """Adds state variable. Only for use by subclasses."""
    if distribute_ctx.has_strategy():
      strategy = distribute_ctx.get_strategy()
    else:
      strategy = None

    # TODO(b/120571621): Make `ON_READ` work with Keras metrics on TPU.
    if backend.is_tpu_strategy(strategy):
      synchronization = variables_module.VariableSynchronization.ON_WRITE

    with ops.init_scope():
      return super(Metric, self).add_weight(
          name=name,
          shape=shape,
          dtype=self._dtype if dtype is None else dtype,
          trainable=False,
          initializer=initializer,
          collections=[],
          synchronization=synchronization,
          aggregation=aggregation)

  ### End: For use by subclasses ###

  @property
  def trainable_weights(self):
    # Overridden from Layer class to track submetric weights.
    if self.trainable:
      trainable_weights = self._trainable_weights
      for m in self._metrics:
        trainable_weights += m.trainable_weights
      return self._dedup_weights(trainable_weights)
    else:
      return []

  @property
  def non_trainable_weights(self):
    # Overridden from Layer class to track submetric weights.
    if self.trainable:
      non_trainable_weights = self._non_trainable_weights
      for m in self._metrics:
        non_trainable_weights += m.non_trainable_weights
    else:
      non_trainable_weights = (
          self._non_trainable_weights + self._trainable_weights)
      for m in self._metrics:
        non_trainable_weights += m.weights
    return self._dedup_weights(non_trainable_weights)

  @property
  def _trackable_saved_model_saver(self):
    return metric_serialization.MetricSavedModelSaver(self)

  @generic_utils.default
  @doc_controls.do_not_generate_docs
  def reset_states(self):
    # Backwards compatibility alias of `reset_state`. New classes should
    # only implement `reset_state`.
    return self.reset_state()


class Reduce(Metric):
  """Encapsulates metrics that perform a reduce operation on the values.

  Args:
    reduction: a `tf.keras.metrics.Reduction` enum value.
    name: string name of the metric instance.
    dtype: (Optional) data type of the metric result.
  """

  def __init__(self, reduction, name, dtype=None):
    super(Reduce, self).__init__(name=name, dtype=dtype)
    self.reduction = reduction
    self.total = self.add_weight(
        'total', initializer=init_ops.zeros_initializer)
    if reduction in [metrics_utils.Reduction.SUM_OVER_BATCH_SIZE,
                     metrics_utils.Reduction.WEIGHTED_MEAN]:
      self.count = self.add_weight(
          'count', initializer=init_ops.zeros_initializer)

  def update_state(self, values, sample_weight=None):
    """Accumulates statistics for computing the metric.

    Args:
      values: Per-example value.
      sample_weight: Optional weighting of each example. Defaults to 1.

    Returns:
      Update op.
    """
    [values], sample_weight = \
        metrics_utils.ragged_assert_compatible_and_get_flat_values(
            [values], sample_weight)
    try:
      values = math_ops.cast(values, self._dtype)
    except (ValueError, TypeError):
      msg = ('The output of a metric function can only be a single Tensor. '
             'Got: %s' % (values,))
      if isinstance(values, dict):
        msg += ('. To return a dict of values, implement a custom Metric '
                'subclass.')
      raise RuntimeError(msg)
    if sample_weight is not None:
      sample_weight = math_ops.cast(sample_weight, self._dtype)
      # Update dimensions of weights to match with values if possible.
      values, _, sample_weight = losses_utils.squeeze_or_expand_dimensions(
          values, sample_weight=sample_weight)
      try:
        # Broadcast weights if possible.
        sample_weight = weights_broadcast_ops.broadcast_weights(
            sample_weight, values)
      except ValueError:
        # Reduce values to same ndim as weight array
        ndim = backend.ndim(values)
        weight_ndim = backend.ndim(sample_weight)
        if self.reduction == metrics_utils.Reduction.SUM:
          values = math_ops.reduce_sum(
              values, axis=list(range(weight_ndim, ndim)))
        else:
          values = math_ops.reduce_mean(
              values, axis=list(range(weight_ndim, ndim)))
      values = math_ops.multiply(values, sample_weight)

    value_sum = math_ops.reduce_sum(values)
    with ops.control_dependencies([value_sum]):
      update_total_op = self.total.assign_add(value_sum)

    # Exit early if the reduction doesn't have a denominator.
    if self.reduction == metrics_utils.Reduction.SUM:
      return update_total_op

    # Update `count` for reductions that require a denominator.
    if self.reduction == metrics_utils.Reduction.SUM_OVER_BATCH_SIZE:
      num_values = math_ops.cast(array_ops.size(values), self._dtype)
    elif self.reduction == metrics_utils.Reduction.WEIGHTED_MEAN:
      if sample_weight is None:
        num_values = math_ops.cast(array_ops.size(values), self._dtype)
      else:
        num_values = math_ops.reduce_sum(sample_weight)
    else:
      raise NotImplementedError(
          'reduction [%s] not implemented' % self.reduction)

    with ops.control_dependencies([update_total_op]):
      return self.count.assign_add(num_values)

  def result(self):
    if self.reduction == metrics_utils.Reduction.SUM:
      return array_ops.identity(self.total)
    elif self.reduction in [
        metrics_utils.Reduction.WEIGHTED_MEAN,
        metrics_utils.Reduction.SUM_OVER_BATCH_SIZE
    ]:
      return math_ops.div_no_nan(self.total, self.count)
    else:
      raise NotImplementedError(
          'reduction [%s] not implemented' % self.reduction)


@keras_export('keras.metrics.Sum')
class Sum(Reduce):
  """Computes the (weighted) sum of the given values.

  For example, if values is [1, 3, 5, 7] then the sum is 16.
  If the weights were specified as [1, 1, 0, 0] then the sum would be 4.

  This metric creates one variable, `total`, that is used to compute the sum of
  `values`. This is ultimately returned as `sum`.

  If `sample_weight` is `None`, weights default to 1.  Use `sample_weight` of 0
  to mask values.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.Sum()
  >>> m.update_state([1, 3, 5, 7])
  >>> m.result().numpy()
  16.0

  Usage with `compile()` API:

  ```python
  model.add_metric(tf.keras.metrics.Sum(name='sum_1')(outputs))
  model.compile(optimizer='sgd', loss='mse')
  ```
  """

  def __init__(self, name='sum', dtype=None):
    super(Sum, self).__init__(reduction=metrics_utils.Reduction.SUM,
                              name=name, dtype=dtype)


@keras_export('keras.metrics.Mean')
class Mean(Reduce):
  """Computes the (weighted) mean of the given values.

  For example, if values is [1, 3, 5, 7] then the mean is 4.
  If the weights were specified as [1, 1, 0, 0] then the mean would be 2.

  This metric creates two variables, `total` and `count` that are used to
  compute the average of `values`. This average is ultimately returned as `mean`
  which is an idempotent operation that simply divides `total` by `count`.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.Mean()
  >>> m.update_state([1, 3, 5, 7])
  >>> m.result().numpy()
  4.0
  >>> m.reset_state()
  >>> m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
  >>> m.result().numpy()
  2.0

  Usage with `compile()` API:

  ```python
  model.add_metric(tf.keras.metrics.Mean(name='mean_1')(outputs))
  model.compile(optimizer='sgd', loss='mse')
  ```
  """

  def __init__(self, name='mean', dtype=None):
    super(Mean, self).__init__(
        reduction=metrics_utils.Reduction.WEIGHTED_MEAN, name=name, dtype=dtype)


@keras_export('keras.metrics.MeanRelativeError')
class MeanRelativeError(Mean):
  """Computes the mean relative error by normalizing with the given values.

  This metric creates two local variables, `total` and `count` that are used to
  compute the mean relative error. This is weighted by `sample_weight`, and
  it is ultimately returned as `mean_relative_error`:
  an idempotent operation that simply divides `total` by `count`.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    normalizer: The normalizer values with same shape as predictions.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.MeanRelativeError(normalizer=[1, 3, 2, 3])
  >>> m.update_state([1, 3, 2, 3], [2, 4, 6, 8])

  >>> # metric = mean(|y_pred - y_true| / normalizer)
  >>> #        = mean([1, 1, 4, 5] / [1, 3, 2, 3]) = mean([1, 1/3, 2, 5/3])
  >>> #        = 5/4 = 1.25
  >>> m.result().numpy()
  1.25

  Usage with `compile()` API:

  ```python
  model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.MeanRelativeError(normalizer=[1, 3])])
  ```
  """

  def __init__(self, normalizer, name=None, dtype=None):
    super(MeanRelativeError, self).__init__(name=name, dtype=dtype)
    normalizer = math_ops.cast(normalizer, self._dtype)
    self.normalizer = normalizer

  def update_state(self, y_true, y_pred, sample_weight=None):
    """Accumulates metric 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.
    """
    y_true = math_ops.cast(y_true, self._dtype)
    y_pred = math_ops.cast(y_pred, self._dtype)
    [y_pred, y_true], sample_weight = \
        metrics_utils.ragged_assert_compatible_and_get_flat_values(
            [y_pred, y_true], sample_weight)
    y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
        y_pred, y_true)

    y_pred, self.normalizer = losses_utils.remove_squeezable_dimensions(
        y_pred, self.normalizer)
    y_pred.shape.assert_is_compatible_with(y_true.shape)
    relative_errors = math_ops.div_no_nan(
        math_ops.abs(y_true - y_pred), self.normalizer)

    return super(MeanRelativeError, self).update_state(
        relative_errors, sample_weight=sample_weight)

  def get_config(self):
    n = self.normalizer
    config = {'normalizer': backend.eval(n) if is_tensor_or_variable(n) else n}
    base_config = super(MeanRelativeError, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.metrics.MeanMetricWrapper')
class MeanMetricWrapper(Mean):
  """Wraps a stateless metric function with the Mean metric.

  You could use this class to quickly build a mean metric from a function. The
  function needs to have the signature `fn(y_true, y_pred)` and return a
  per-sample loss array. `MeanMetricWrapper.result()` will return
  the average metric value across all samples seen so far.

  For example:

  ```python
  def accuracy(y_true, y_pred):
    return tf.cast(tf.math.equal(y_true, y_pred), tf.float32)

  accuracy_metric = tf.keras.metrics.MeanMetricWrapper(fn=accuracy)

  keras_model.compile(..., metrics=accuracy_metric)
  ```

  Args:
    fn: The metric function to wrap, with signature `fn(y_true, y_pred,
      **kwargs)`.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
    **kwargs: Keyword arguments to pass on to `fn`.
  """

  def __init__(self, fn, name=None, dtype=None, **kwargs):
    super(MeanMetricWrapper, self).__init__(name=name, dtype=dtype)
    self._fn = fn
    self._fn_kwargs = kwargs

  def update_state(self, y_true, y_pred, sample_weight=None):
    """Accumulates metric statistics.

    `y_true` and `y_pred` should have the same shape.

    Args:
      y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
      y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.
      sample_weight: Optional `sample_weight` acts as a
        coefficient for the metric. If a scalar is provided, then the metric is
        simply scaled by the given value. If `sample_weight` is a tensor of size
        `[batch_size]`, then the metric for each sample of the batch is rescaled
        by the corresponding element in the `sample_weight` vector. If the shape
        of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted
        to this shape), then each metric element of `y_pred` is scaled by the
        corresponding value of `sample_weight`. (Note on `dN-1`: all metric
        functions reduce by 1 dimension, usually the last axis (-1)).

    Returns:
      Update op.
    """
    y_true = math_ops.cast(y_true, self._dtype)
    y_pred = math_ops.cast(y_pred, self._dtype)
    [y_true, y_pred], sample_weight = (
        metrics_utils.ragged_assert_compatible_and_get_flat_values(
            [y_true, y_pred], sample_weight))
    y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
        y_pred, y_true)

    ag_fn = autograph.tf_convert(self._fn, ag_ctx.control_status_ctx())
    matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
    return super(MeanMetricWrapper, self).update_state(
        matches, sample_weight=sample_weight)

  def get_config(self):
    config = {}

    if type(self) is MeanMetricWrapper:  # pylint: disable=unidiomatic-typecheck
      # Only include function argument when the object is a MeanMetricWrapper
      # and not a subclass.
      config['fn'] = self._fn

    for k, v in self._fn_kwargs.items():
      config[k] = backend.eval(v) if is_tensor_or_variable(v) else v
    base_config = super(MeanMetricWrapper, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  @classmethod
  def from_config(cls, config):
    # Note that while MeanMetricWrapper itself isn't public, objects of this
    # class may be created and added to the model by calling model.compile.
    fn = config.pop('fn', None)
    if cls is MeanMetricWrapper:
      return cls(get(fn), **config)
    return super(MeanMetricWrapper, cls).from_config(config)


@keras_export('keras.metrics.Accuracy')
class Accuracy(MeanMetricWrapper):
  """Calculates how often predictions equal labels.

  This metric creates two local variables, `total` and `count` that are used to
  compute the frequency with which `y_pred` matches `y_true`. This frequency is
  ultimately returned as `binary accuracy`: an idempotent operation that simply
  divides `total` by `count`.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.Accuracy()
  >>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]])
  >>> m.result().numpy()
  0.75

  >>> m.reset_state()
  >>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]],
  ...                sample_weight=[1, 1, 0, 0])
  >>> m.result().numpy()
  0.5

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.Accuracy()])
  ```
  """

  def __init__(self, name='accuracy', dtype=None):
    super(Accuracy, self).__init__(accuracy, name, dtype=dtype)


@keras_export('keras.metrics.BinaryAccuracy')
class BinaryAccuracy(MeanMetricWrapper):
  """Calculates how often predictions match binary labels.

  This metric creates two local variables, `total` and `count` that are used to
  compute the frequency with which `y_pred` matches `y_true`. This frequency is
  ultimately returned as `binary accuracy`: an idempotent operation that simply
  divides `total` by `count`.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
    threshold: (Optional) Float representing the threshold for deciding
    whether prediction values are 1 or 0.

  Standalone usage:

  >>> m = tf.keras.metrics.BinaryAccuracy()
  >>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]])
  >>> m.result().numpy()
  0.75

  >>> m.reset_state()
  >>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]],
  ...                sample_weight=[1, 0, 0, 1])
  >>> m.result().numpy()
  0.5

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.BinaryAccuracy()])
  ```
  """

  def __init__(self, name='binary_accuracy', dtype=None, threshold=0.5):
    super(BinaryAccuracy, self).__init__(
        binary_accuracy, name, dtype=dtype, threshold=threshold)


@keras_export('keras.metrics.CategoricalAccuracy')
class CategoricalAccuracy(MeanMetricWrapper):
  """Calculates how often predictions match one-hot labels.

  You can provide logits of classes as `y_pred`, since argmax of
  logits and probabilities are same.

  This metric creates two local variables, `total` and `count` that are used to
  compute the frequency with which `y_pred` matches `y_true`. This frequency is
  ultimately returned as `categorical accuracy`: an idempotent operation that
  simply divides `total` by `count`.

  `y_pred` and `y_true` should be passed in as vectors of probabilities, rather
  than as labels. If necessary, use `tf.one_hot` to expand `y_true` as a vector.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.CategoricalAccuracy()
  >>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
  ...                 [0.05, 0.95, 0]])
  >>> m.result().numpy()
  0.5

  >>> m.reset_state()
  >>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8],
  ...                 [0.05, 0.95, 0]],
  ...                sample_weight=[0.7, 0.3])
  >>> m.result().numpy()
  0.3

  Usage with `compile()` API:

  ```python
  model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.CategoricalAccuracy()])
  ```
  """

  def __init__(self, name='categorical_accuracy', dtype=None):
    super(CategoricalAccuracy, self).__init__(
        categorical_accuracy, name, dtype=dtype)


@keras_export('keras.metrics.SparseCategoricalAccuracy')
class SparseCategoricalAccuracy(MeanMetricWrapper):
  """Calculates how often predictions match integer labels.

  ```python
  acc = np.dot(sample_weight, np.equal(y_true, np.argmax(y_pred, axis=1))
  ```

  You can provide logits of classes as `y_pred`, since argmax of
  logits and probabilities are same.

  This metric creates two local variables, `total` and `count` that are used to
  compute the frequency with which `y_pred` matches `y_true`. This frequency is
  ultimately returned as `sparse categorical accuracy`: an idempotent operation
  that simply divides `total` by `count`.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.SparseCategoricalAccuracy()
  >>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]])
  >>> m.result().numpy()
  0.5

  >>> m.reset_state()
  >>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]],
  ...                sample_weight=[0.7, 0.3])
  >>> m.result().numpy()
  0.3

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
  ```
  """

  def __init__(self, name='sparse_categorical_accuracy', dtype=None):
    super(SparseCategoricalAccuracy, self).__init__(
        sparse_categorical_accuracy, name, dtype=dtype)


@keras_export('keras.metrics.TopKCategoricalAccuracy')
class TopKCategoricalAccuracy(MeanMetricWrapper):
  """Computes how often targets are in the top `K` predictions.

  Args:
    k: (Optional) Number of top elements to look at for computing accuracy.
      Defaults to 5.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.TopKCategoricalAccuracy(k=1)
  >>> m.update_state([[0, 0, 1], [0, 1, 0]],
  ...                [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
  >>> m.result().numpy()
  0.5

  >>> m.reset_state()
  >>> m.update_state([[0, 0, 1], [0, 1, 0]],
  ...                [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
  ...                sample_weight=[0.7, 0.3])
  >>> m.result().numpy()
  0.3

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.TopKCategoricalAccuracy()])
  ```
  """

  def __init__(self, k=5, name='top_k_categorical_accuracy', dtype=None):
    super(TopKCategoricalAccuracy, self).__init__(
        top_k_categorical_accuracy, name, dtype=dtype, k=k)


@keras_export('keras.metrics.SparseTopKCategoricalAccuracy')
class SparseTopKCategoricalAccuracy(MeanMetricWrapper):
  """Computes how often integer targets are in the top `K` predictions.

  Args:
    k: (Optional) Number of top elements to look at for computing accuracy.
      Defaults to 5.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.SparseTopKCategoricalAccuracy(k=1)
  >>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
  >>> m.result().numpy()
  0.5

  >>> m.reset_state()
  >>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
  ...                sample_weight=[0.7, 0.3])
  >>> m.result().numpy()
  0.3

  Usage with `compile()` API:

  ```python
  model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.SparseTopKCategoricalAccuracy()])
  ```
  """

  def __init__(self, k=5, name='sparse_top_k_categorical_accuracy', dtype=None):
    super(SparseTopKCategoricalAccuracy, self).__init__(
        sparse_top_k_categorical_accuracy, name, dtype=dtype, k=k)


class _ConfusionMatrixConditionCount(Metric):
  """Calculates the number of the given confusion matrix condition.

  Args:
    confusion_matrix_cond: One of `metrics_utils.ConfusionMatrix` conditions.
    thresholds: (Optional) Defaults to 0.5. A float value or a python list/tuple
      of float threshold values in [0, 1]. A threshold is compared with
      prediction values to determine the truth value of predictions (i.e., above
      the threshold is `true`, below is `false`). One metric value is generated
      for each threshold value.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
  """

  def __init__(self,
               confusion_matrix_cond,
               thresholds=None,
               name=None,
               dtype=None):
    super(_ConfusionMatrixConditionCount, self).__init__(name=name, dtype=dtype)
    self._confusion_matrix_cond = confusion_matrix_cond
    self.init_thresholds = thresholds
    self.thresholds = metrics_utils.parse_init_thresholds(
        thresholds, default_threshold=0.5)
    self._thresholds_distributed_evenly = (
        metrics_utils.is_evenly_distributed_thresholds(self.thresholds))
    self.accumulator = self.add_weight(
        'accumulator',
        shape=(len(self.thresholds),),
        initializer=init_ops.zeros_initializer)

  def update_state(self, y_true, y_pred, sample_weight=None):
    """Accumulates the metric 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.
    """
    return metrics_utils.update_confusion_matrix_variables(
        {self._confusion_matrix_cond: self.accumulator},
        y_true,
        y_pred,
        thresholds=self.thresholds,
        thresholds_distributed_evenly=self._thresholds_distributed_evenly,
        sample_weight=sample_weight)

  def result(self):
    if len(self.thresholds) == 1:
      result = self.accumulator[0]
    else:
      result = self.accumulator
    return ops.convert_to_tensor_v2_with_dispatch(result)

  def reset_state(self):
    num_thresholds = len(to_list(self.thresholds))
    backend.batch_set_value(
        [(v, np.zeros((num_thresholds,))) for v in self.variables])

  def get_config(self):
    config = {'thresholds': self.init_thresholds}
    base_config = super(_ConfusionMatrixConditionCount, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.metrics.FalsePositives')
class FalsePositives(_ConfusionMatrixConditionCount):
  """Calculates the number of false positives.

  If `sample_weight` is given, calculates the sum of the weights of
  false positives. This metric creates one local variable, `accumulator`
  that is used to keep track of the number of false positives.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    thresholds: (Optional) Defaults to 0.5. A float value or a python
      list/tuple of float threshold values in [0, 1]. A threshold is compared
      with prediction values to determine the truth value of predictions
      (i.e., above the threshold is `true`, below is `false`). One metric
      value is generated for each threshold value.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.FalsePositives()
  >>> m.update_state([0, 1, 0, 0], [0, 0, 1, 1])
  >>> m.result().numpy()
  2.0

  >>> m.reset_state()
  >>> m.update_state([0, 1, 0, 0], [0, 0, 1, 1], sample_weight=[0, 0, 1, 0])
  >>> m.result().numpy()
  1.0

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.FalsePositives()])
  ```
  """

  def __init__(self, thresholds=None, name=None, dtype=None):
    super(FalsePositives, self).__init__(
        confusion_matrix_cond=metrics_utils.ConfusionMatrix.FALSE_POSITIVES,
        thresholds=thresholds,
        name=name,
        dtype=dtype)


@keras_export('keras.metrics.FalseNegatives')
class FalseNegatives(_ConfusionMatrixConditionCount):
  """Calculates the number of false negatives.

  If `sample_weight` is given, calculates the sum of the weights of
  false negatives. This metric creates one local variable, `accumulator`
  that is used to keep track of the number of false negatives.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    thresholds: (Optional) Defaults to 0.5. A float value or a python
      list/tuple of float threshold values in [0, 1]. A threshold is compared
      with prediction values to determine the truth value of predictions
      (i.e., above the threshold is `true`, below is `false`). One metric
      value is generated for each threshold value.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.FalseNegatives()
  >>> m.update_state([0, 1, 1, 1], [0, 1, 0, 0])
  >>> m.result().numpy()
  2.0

  >>> m.reset_state()
  >>> m.update_state([0, 1, 1, 1], [0, 1, 0, 0], sample_weight=[0, 0, 1, 0])
  >>> m.result().numpy()
  1.0

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.FalseNegatives()])
  ```
  """

  def __init__(self, thresholds=None, name=None, dtype=None):
    super(FalseNegatives, self).__init__(
        confusion_matrix_cond=metrics_utils.ConfusionMatrix.FALSE_NEGATIVES,
        thresholds=thresholds,
        name=name,
        dtype=dtype)


@keras_export('keras.metrics.TrueNegatives')
class TrueNegatives(_ConfusionMatrixConditionCount):
  """Calculates the number of true negatives.

  If `sample_weight` is given, calculates the sum of the weights of
  true negatives. This metric creates one local variable, `accumulator`
  that is used to keep track of the number of true negatives.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    thresholds: (Optional) Defaults to 0.5. A float value or a python
      list/tuple of float threshold values in [0, 1]. A threshold is compared
      with prediction values to determine the truth value of predictions
      (i.e., above the threshold is `true`, below is `false`). One metric
      value is generated for each threshold value.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.TrueNegatives()
  >>> m.update_state([0, 1, 0, 0], [1, 1, 0, 0])
  >>> m.result().numpy()
  2.0

  >>> m.reset_state()
  >>> m.update_state([0, 1, 0, 0], [1, 1, 0, 0], sample_weight=[0, 0, 1, 0])
  >>> m.result().numpy()
  1.0

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.TrueNegatives()])
  ```
  """

  def __init__(self, thresholds=None, name=None, dtype=None):
    super(TrueNegatives, self).__init__(
        confusion_matrix_cond=metrics_utils.ConfusionMatrix.TRUE_NEGATIVES,
        thresholds=thresholds,
        name=name,
        dtype=dtype)


@keras_export('keras.metrics.TruePositives')
class TruePositives(_ConfusionMatrixConditionCount):
  """Calculates the number of true positives.

  If `sample_weight` is given, calculates the sum of the weights of
  true positives. This metric creates one local variable, `true_positives`
  that is used to keep track of the number of true positives.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    thresholds: (Optional) Defaults to 0.5. A float value or a python
      list/tuple of float threshold values in [0, 1]. A threshold is compared
      with prediction values to determine the truth value of predictions
      (i.e., above the threshold is `true`, below is `false`). One metric
      value is generated for each threshold value.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.TruePositives()
  >>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
  >>> m.result().numpy()
  2.0

  >>> m.reset_state()
  >>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
  >>> m.result().numpy()
  1.0

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.TruePositives()])
  ```
  """

  def __init__(self, thresholds=None, name=None, dtype=None):
    super(TruePositives, self).__init__(
        confusion_matrix_cond=metrics_utils.ConfusionMatrix.TRUE_POSITIVES,
        thresholds=thresholds,
        name=name,
        dtype=dtype)


@keras_export('keras.metrics.Precision')
class Precision(Metric):
  """Computes the precision of the predictions with respect to the labels.

  The metric creates two local variables, `true_positives` and `false_positives`
  that are used to compute the precision. This value is ultimately returned as
  `precision`, an idempotent operation that simply divides `true_positives`
  by the sum of `true_positives` and `false_positives`.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  If `top_k` is set, we'll calculate precision as how often on average a class
  among the top-k classes with the highest predicted values of a batch entry is
  correct and can be found in the label for that entry.

  If `class_id` is specified, we calculate precision by considering only the
  entries in the batch for which `class_id` is above the threshold and/or in the
  top-k highest predictions, and computing the fraction of them for which
  `class_id` is indeed a correct label.

  Args:
    thresholds: (Optional) A float value or a python list/tuple of float
      threshold values in [0, 1]. A threshold is compared with prediction
      values to determine the truth value of predictions (i.e., above the
      threshold is `true`, below is `false`). One metric value is generated
      for each threshold value. If neither thresholds nor top_k are set, the
      default is to calculate precision with `thresholds=0.5`.
    top_k: (Optional) Unset by default. An int value specifying the top-k
      predictions to consider when calculating precision.
    class_id: (Optional) Integer class ID for which we want binary metrics.
      This must be in the half-open interval `[0, num_classes)`, where
      `num_classes` is the last dimension of predictions.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.Precision()
  >>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
  >>> m.result().numpy()
  0.6666667

  >>> m.reset_state()
  >>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
  >>> m.result().numpy()
  1.0

  >>> # With top_k=2, it will calculate precision over y_true[:2] and y_pred[:2]
  >>> m = tf.keras.metrics.Precision(top_k=2)
  >>> m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
  >>> m.result().numpy()
  0.0

  >>> # With top_k=4, it will calculate precision over y_true[:4] and y_pred[:4]
  >>> m = tf.keras.metrics.Precision(top_k=4)
  >>> m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
  >>> m.result().numpy()
  0.5

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.Precision()])
  ```
  """

  def __init__(self,
               thresholds=None,
               top_k=None,
               class_id=None,
               name=None,
               dtype=None):
    super(Precision, self).__init__(name=name, dtype=dtype)
    self.init_thresholds = thresholds
    self.top_k = top_k
    self.class_id = class_id

    default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
    self.thresholds = metrics_utils.parse_init_thresholds(
        thresholds, default_threshold=default_threshold)
    self._thresholds_distributed_evenly = (
        metrics_utils.is_evenly_distributed_thresholds(self.thresholds))
    self.true_positives = self.add_weight(
        'true_positives',
        shape=(len(self.thresholds),),
        initializer=init_ops.zeros_initializer)
    self.false_positives = self.add_weight(
        'false_positives',
        shape=(len(self.thresholds),),
        initializer=init_ops.zeros_initializer)

  def update_state(self, y_true, y_pred, sample_weight=None):
    """Accumulates true positive and false positive statistics.

    Args:
      y_true: The ground truth values, with the same dimensions as `y_pred`.
        Will be cast to `bool`.
      y_pred: The predicted values. Each element must be in the range `[0, 1]`.
      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.
    """
    return metrics_utils.update_confusion_matrix_variables(
        {
            metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,
            metrics_utils.ConfusionMatrix.FALSE_POSITIVES: self.false_positives
        },
        y_true,
        y_pred,
        thresholds=self.thresholds,
        thresholds_distributed_evenly=self._thresholds_distributed_evenly,
        top_k=self.top_k,
        class_id=self.class_id,
        sample_weight=sample_weight)

  def result(self):
    result = math_ops.div_no_nan(self.true_positives,
                                 self.true_positives + self.false_positives)
    return result[0] if len(self.thresholds) == 1 else result

  def reset_state(self):
    num_thresholds = len(to_list(self.thresholds))
    backend.batch_set_value([(v, np.zeros((num_thresholds,)))
                             for v in (self.true_positives,
                                       self.false_positives)])

  def get_config(self):
    config = {
        'thresholds': self.init_thresholds,
        'top_k': self.top_k,
        'class_id': self.class_id
    }
    base_config = super(Precision, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.metrics.Recall')
class Recall(Metric):
  """Computes the recall of the predictions with respect to the labels.

  This metric creates two local variables, `true_positives` and
  `false_negatives`, that are used to compute the recall. This value is
  ultimately returned as `recall`, an idempotent operation that simply divides
  `true_positives` by the sum of `true_positives` and `false_negatives`.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  If `top_k` is set, recall will be computed as how often on average a class
  among the labels of a batch entry is in the top-k predictions.

  If `class_id` is specified, we calculate recall by considering only the
  entries in the batch for which `class_id` is in the label, and computing the
  fraction of them for which `class_id` is above the threshold and/or in the
  top-k predictions.

  Args:
    thresholds: (Optional) A float value or a python list/tuple of float
      threshold values in [0, 1]. A threshold is compared with prediction
      values to determine the truth value of predictions (i.e., above the
      threshold is `true`, below is `false`). One metric value is generated
      for each threshold value. If neither thresholds nor top_k are set, the
      default is to calculate recall with `thresholds=0.5`.
    top_k: (Optional) Unset by default. An int value specifying the top-k
      predictions to consider when calculating recall.
    class_id: (Optional) Integer class ID for which we want binary metrics.
      This must be in the half-open interval `[0, num_classes)`, where
      `num_classes` is the last dimension of predictions.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.Recall()
  >>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
  >>> m.result().numpy()
  0.6666667

  >>> m.reset_state()
  >>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
  >>> m.result().numpy()
  1.0

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.Recall()])
  ```
  """

  def __init__(self,
               thresholds=None,
               top_k=None,
               class_id=None,
               name=None,
               dtype=None):
    super(Recall, self).__init__(name=name, dtype=dtype)
    self.init_thresholds = thresholds
    self.top_k = top_k
    self.class_id = class_id

    default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
    self.thresholds = metrics_utils.parse_init_thresholds(
        thresholds, default_threshold=default_threshold)
    self._thresholds_distributed_evenly = (
        metrics_utils.is_evenly_distributed_thresholds(self.thresholds))
    self.true_positives = self.add_weight(
        'true_positives',
        shape=(len(self.thresholds),),
        initializer=init_ops.zeros_initializer)
    self.false_negatives = self.add_weight(
        'false_negatives',
        shape=(len(self.thresholds),),
        initializer=init_ops.zeros_initializer)

  def update_state(self, y_true, y_pred, sample_weight=None):
    """Accumulates true positive and false negative statistics.

    Args:
      y_true: The ground truth values, with the same dimensions as `y_pred`.
        Will be cast to `bool`.
      y_pred: The predicted values. Each element must be in the range `[0, 1]`.
      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.
    """
    return metrics_utils.update_confusion_matrix_variables(
        {
            metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,
            metrics_utils.ConfusionMatrix.FALSE_NEGATIVES: self.false_negatives
        },
        y_true,
        y_pred,
        thresholds=self.thresholds,
        thresholds_distributed_evenly=self._thresholds_distributed_evenly,
        top_k=self.top_k,
        class_id=self.class_id,
        sample_weight=sample_weight)

  def result(self):
    result = math_ops.div_no_nan(self.true_positives,
                                 self.true_positives + self.false_negatives)
    return result[0] if len(self.thresholds) == 1 else result

  def reset_state(self):
    num_thresholds = len(to_list(self.thresholds))
    backend.batch_set_value([(v, np.zeros((num_thresholds,)))
                             for v in (self.true_positives,
                                       self.false_negatives)])

  def get_config(self):
    config = {
        'thresholds': self.init_thresholds,
        'top_k': self.top_k,
        'class_id': self.class_id
    }
    base_config = super(Recall, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


class SensitivitySpecificityBase(Metric, metaclass=abc.ABCMeta):
  """Abstract base class for computing sensitivity and specificity.

  For additional information about specificity and sensitivity, see
  [the following](https://en.wikipedia.org/wiki/Sensitivity_and_specificity).
  """

  def __init__(self,
               value,
               num_thresholds=200,
               class_id=None,
               name=None,
               dtype=None):
    super(SensitivitySpecificityBase, self).__init__(name=name, dtype=dtype)
    if num_thresholds <= 0:
      raise ValueError('`num_thresholds` must be > 0.')
    self.value = value
    self.class_id = class_id
    self.true_positives = self.add_weight(
        'true_positives',
        shape=(num_thresholds,),
        initializer=init_ops.zeros_initializer)
    self.true_negatives = self.add_weight(
        'true_negatives',
        shape=(num_thresholds,),
        initializer=init_ops.zeros_initializer)
    self.false_positives = self.add_weight(
        'false_positives',
        shape=(num_thresholds,),
        initializer=init_ops.zeros_initializer)
    self.false_negatives = self.add_weight(
        'false_negatives',
        shape=(num_thresholds,),
        initializer=init_ops.zeros_initializer)

    # Compute `num_thresholds` thresholds in [0, 1]
    if num_thresholds == 1:
      self.thresholds = [0.5]
      self._thresholds_distributed_evenly = False
    else:
      thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
                    for i in range(num_thresholds - 2)]
      self.thresholds = [0.0] + thresholds + [1.0]
      self._thresholds_distributed_evenly = True

  def update_state(self, y_true, y_pred, sample_weight=None):
    """Accumulates 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.
    """
    return metrics_utils.update_confusion_matrix_variables(
        {
            metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,
            metrics_utils.ConfusionMatrix.TRUE_NEGATIVES: self.true_negatives,
            metrics_utils.ConfusionMatrix.FALSE_POSITIVES: self.false_positives,
            metrics_utils.ConfusionMatrix.FALSE_NEGATIVES: self.false_negatives,
        },
        y_true,
        y_pred,
        thresholds=self.thresholds,
        thresholds_distributed_evenly=self._thresholds_distributed_evenly,
        class_id=self.class_id,
        sample_weight=sample_weight)

  def reset_state(self):
    num_thresholds = len(self.thresholds)
    confusion_matrix_variables = (self.true_positives, self.true_negatives,
                                  self.false_positives, self.false_negatives)
    backend.batch_set_value([
        (v, np.zeros((num_thresholds,))) for v in confusion_matrix_variables
    ])

  def get_config(self):
    config = {'class_id': self.class_id}
    base_config = super(SensitivitySpecificityBase, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def _find_max_under_constraint(self, constrained, dependent, predicate):
    """Returns the maximum of dependent_statistic that satisfies the constraint.

    Args:
      constrained: Over these values the constraint
        is specified. A rank-1 tensor.
      dependent: From these values the maximum that satiesfies the
        constraint is selected. Values in this tensor and in
        `constrained` are linked by having the same threshold at each
        position, hence this tensor must have the same shape.
      predicate: A binary boolean functor to be applied to arguments
      `constrained` and `self.value`, e.g. `tf.greater`.

    Returns maximal dependent value, if no value satiesfies the constraint 0.0.
    """
    feasible = array_ops.where_v2(predicate(constrained, self.value))
    feasible_exists = math_ops.greater(array_ops.size(feasible), 0)
    max_dependent = math_ops.reduce_max(array_ops.gather(dependent, feasible))

    return array_ops.where_v2(feasible_exists, max_dependent, 0.0)


@keras_export('keras.metrics.SensitivityAtSpecificity')
class SensitivityAtSpecificity(SensitivitySpecificityBase):
  """Computes best sensitivity where specificity is >= specified value.

  the sensitivity at a given specificity.

  `Sensitivity` measures the proportion of actual positives that are correctly
  identified as such (tp / (tp + fn)).
  `Specificity` measures the proportion of actual negatives that are correctly
  identified as such (tn / (tn + fp)).

  This metric creates four local variables, `true_positives`, `true_negatives`,
  `false_positives` and `false_negatives` that are used to compute the
  sensitivity at the given specificity. The threshold for the given specificity
  value is computed and used to evaluate the corresponding sensitivity.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  If `class_id` is specified, we calculate precision by considering only the
  entries in the batch for which `class_id` is above the threshold predictions,
  and computing the fraction of them for which `class_id` is indeed a correct
  label.

  For additional information about specificity and sensitivity, see
  [the following](https://en.wikipedia.org/wiki/Sensitivity_and_specificity).

  Args:
    specificity: A scalar value in range `[0, 1]`.
    num_thresholds: (Optional) Defaults to 200. The number of thresholds to
      use for matching the given specificity.
    class_id: (Optional) Integer class ID for which we want binary metrics.
      This must be in the half-open interval `[0, num_classes)`, where
      `num_classes` is the last dimension of predictions.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.SensitivityAtSpecificity(0.5)
  >>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
  >>> m.result().numpy()
  0.5

  >>> m.reset_state()
  >>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
  ...                sample_weight=[1, 1, 2, 2, 1])
  >>> m.result().numpy()
  0.333333

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.SensitivityAtSpecificity()])
  ```
  """

  def __init__(self,
               specificity,
               num_thresholds=200,
               class_id=None,
               name=None,
               dtype=None):
    if specificity < 0 or specificity > 1:
      raise ValueError('`specificity` must be in the range [0, 1].')
    self.specificity = specificity
    self.num_thresholds = num_thresholds
    super(SensitivityAtSpecificity, self).__init__(
        specificity,
        num_thresholds=num_thresholds,
        class_id=class_id,
        name=name,
        dtype=dtype)

  def result(self):
    specificities = math_ops.div_no_nan(
        self.true_negatives, self.true_negatives + self.false_positives)
    sensitivities = math_ops.div_no_nan(
        self.true_positives, self.true_positives + self.false_negatives)
    return self._find_max_under_constraint(
        specificities, sensitivities, math_ops.greater_equal)

  def get_config(self):
    config = {
        'num_thresholds': self.num_thresholds,
        'specificity': self.specificity
    }
    base_config = super(SensitivityAtSpecificity, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.metrics.SpecificityAtSensitivity')
class SpecificityAtSensitivity(SensitivitySpecificityBase):
  """Computes best specificity where sensitivity is >= specified value.

  `Sensitivity` measures the proportion of actual positives that are correctly
  identified as such (tp / (tp + fn)).
  `Specificity` measures the proportion of actual negatives that are correctly
  identified as such (tn / (tn + fp)).

  This metric creates four local variables, `true_positives`, `true_negatives`,
  `false_positives` and `false_negatives` that are used to compute the
  specificity at the given sensitivity. The threshold for the given sensitivity
  value is computed and used to evaluate the corresponding specificity.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  If `class_id` is specified, we calculate precision by considering only the
  entries in the batch for which `class_id` is above the threshold predictions,
  and computing the fraction of them for which `class_id` is indeed a correct
  label.

  For additional information about specificity and sensitivity, see
  [the following](https://en.wikipedia.org/wiki/Sensitivity_and_specificity).

  Args:
    sensitivity: A scalar value in range `[0, 1]`.
    num_thresholds: (Optional) Defaults to 200. The number of thresholds to
      use for matching the given sensitivity.
    class_id: (Optional) Integer class ID for which we want binary metrics.
      This must be in the half-open interval `[0, num_classes)`, where
      `num_classes` is the last dimension of predictions.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.SpecificityAtSensitivity(0.5)
  >>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
  >>> m.result().numpy()
  0.66666667

  >>> m.reset_state()
  >>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
  ...                sample_weight=[1, 1, 2, 2, 2])
  >>> m.result().numpy()
  0.5

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.SpecificityAtSensitivity()])
  ```
  """

  def __init__(self,
               sensitivity,
               num_thresholds=200,
               class_id=None,
               name=None,
               dtype=None):
    if sensitivity < 0 or sensitivity > 1:
      raise ValueError('`sensitivity` must be in the range [0, 1].')
    self.sensitivity = sensitivity
    self.num_thresholds = num_thresholds
    super(SpecificityAtSensitivity, self).__init__(
        sensitivity,
        num_thresholds=num_thresholds,
        class_id=class_id,
        name=name,
        dtype=dtype)

  def result(self):
    sensitivities = math_ops.div_no_nan(
        self.true_positives, self.true_positives + self.false_negatives)
    specificities = math_ops.div_no_nan(
        self.true_negatives, self.true_negatives + self.false_positives)
    return self._find_max_under_constraint(
        sensitivities, specificities, math_ops.greater_equal)

  def get_config(self):
    config = {
        'num_thresholds': self.num_thresholds,
        'sensitivity': self.sensitivity
    }
    base_config = super(SpecificityAtSensitivity, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.metrics.PrecisionAtRecall')
class PrecisionAtRecall(SensitivitySpecificityBase):
  """Computes best precision where recall is >= specified value.

  This metric creates four local variables, `true_positives`, `true_negatives`,
  `false_positives` and `false_negatives` that are used to compute the
  precision at the given recall. The threshold for the given recall
  value is computed and used to evaluate the corresponding precision.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  If `class_id` is specified, we calculate precision by considering only the
  entries in the batch for which `class_id` is above the threshold predictions,
  and computing the fraction of them for which `class_id` is indeed a correct
  label.

  Args:
    recall: A scalar value in range `[0, 1]`.
    num_thresholds: (Optional) Defaults to 200. The number of thresholds to
      use for matching the given recall.
    class_id: (Optional) Integer class ID for which we want binary metrics.
      This must be in the half-open interval `[0, num_classes)`, where
      `num_classes` is the last dimension of predictions.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.PrecisionAtRecall(0.5)
  >>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
  >>> m.result().numpy()
  0.5

  >>> m.reset_state()
  >>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
  ...                sample_weight=[2, 2, 2, 1, 1])
  >>> m.result().numpy()
  0.33333333

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.PrecisionAtRecall(recall=0.8)])
  ```
  """

  def __init__(self,
               recall,
               num_thresholds=200,
               class_id=None,
               name=None,
               dtype=None):
    if recall < 0 or recall > 1:
      raise ValueError('`recall` must be in the range [0, 1].')
    self.recall = recall
    self.num_thresholds = num_thresholds
    super(PrecisionAtRecall, self).__init__(
        value=recall,
        num_thresholds=num_thresholds,
        class_id=class_id,
        name=name,
        dtype=dtype)

  def result(self):
    recalls = math_ops.div_no_nan(
        self.true_positives, self.true_positives + self.false_negatives)
    precisions = math_ops.div_no_nan(
        self.true_positives, self.true_positives + self.false_positives)
    return self._find_max_under_constraint(
        recalls, precisions, math_ops.greater_equal)

  def get_config(self):
    config = {'num_thresholds': self.num_thresholds, 'recall': self.recall}
    base_config = super(PrecisionAtRecall, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.metrics.RecallAtPrecision')
class RecallAtPrecision(SensitivitySpecificityBase):
  """Computes best recall where precision is >= specified value.

  For a given score-label-distribution the required precision might not
  be achievable, in this case 0.0 is returned as recall.

  This metric creates four local variables, `true_positives`, `true_negatives`,
  `false_positives` and `false_negatives` that are used to compute the
  recall at the given precision. The threshold for the given precision
  value is computed and used to evaluate the corresponding recall.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  If `class_id` is specified, we calculate precision by considering only the
  entries in the batch for which `class_id` is above the threshold predictions,
  and computing the fraction of them for which `class_id` is indeed a correct
  label.

  Args:
    precision: A scalar value in range `[0, 1]`.
    num_thresholds: (Optional) Defaults to 200. The number of thresholds to
      use for matching the given precision.
    class_id: (Optional) Integer class ID for which we want binary metrics.
      This must be in the half-open interval `[0, num_classes)`, where
      `num_classes` is the last dimension of predictions.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.RecallAtPrecision(0.8)
  >>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
  >>> m.result().numpy()
  0.5

  >>> m.reset_state()
  >>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
  ...                sample_weight=[1, 0, 0, 1])
  >>> m.result().numpy()
  1.0

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.RecallAtPrecision(precision=0.8)])
  ```
  """

  def __init__(self,
               precision,
               num_thresholds=200,
               class_id=None,
               name=None,
               dtype=None):
    if precision < 0 or precision > 1:
      raise ValueError('`precision` must be in the range [0, 1].')
    self.precision = precision
    self.num_thresholds = num_thresholds
    super(RecallAtPrecision, self).__init__(
        value=precision,
        num_thresholds=num_thresholds,
        class_id=class_id,
        name=name,
        dtype=dtype)

  def result(self):
    precisions = math_ops.div_no_nan(
        self.true_positives, self.true_positives + self.false_positives)
    recalls = math_ops.div_no_nan(
        self.true_positives, self.true_positives + self.false_negatives)
    return self._find_max_under_constraint(
        precisions, recalls, math_ops.greater_equal)

  def get_config(self):
    config = {'num_thresholds': self.num_thresholds,
              'precision': self.precision}
    base_config = super(RecallAtPrecision, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.metrics.AUC')
class AUC(Metric):
  """Approximates the AUC (Area under the curve) of the ROC or PR curves.

  The AUC (Area under the curve) of the ROC (Receiver operating
  characteristic; default) or PR (Precision Recall) curves are quality measures
  of binary classifiers. Unlike the accuracy, and like cross-entropy
  losses, ROC-AUC and PR-AUC evaluate all the operational points of a model.

  This class approximates AUCs using a Riemann sum. During the metric
  accumulation phrase, predictions are accumulated within predefined buckets
  by value. The AUC is then computed by interpolating per-bucket averages. These
  buckets define the evaluated operational points.

  This metric creates four local variables, `true_positives`, `true_negatives`,
  `false_positives` and `false_negatives` that are used to compute the AUC.
  To discretize the AUC curve, a linearly spaced set of thresholds is used to
  compute pairs of recall and precision values. The area under the ROC-curve is
  therefore computed using the height of the recall values by the false positive
  rate, while the area under the PR-curve is the computed using the height of
  the precision values by the recall.

  This value is ultimately returned as `auc`, an idempotent operation that
  computes the area under a discretized curve of precision versus recall values
  (computed using the aforementioned variables). The `num_thresholds` variable
  controls the degree of discretization with larger numbers of thresholds more
  closely approximating the true AUC. The quality of the approximation may vary
  dramatically depending on `num_thresholds`. The `thresholds` parameter can be
  used to manually specify thresholds which split the predictions more evenly.

  For a best approximation of the real AUC, `predictions` should be distributed
  approximately uniformly in the range [0, 1] (if `from_logits=False`). The
  quality of the AUC approximation may be poor if this is not the case. Setting
  `summation_method` to 'minoring' or 'majoring' can help quantify the error in
  the approximation by providing lower or upper bound estimate of the AUC.

  If `sample_weight` is `None`, weights default to 1.
  Use `sample_weight` of 0 to mask values.

  Args:
    num_thresholds: (Optional) Defaults to 200. The number of thresholds to
      use when discretizing the roc curve. Values must be > 1.
    curve: (Optional) Specifies the name of the curve to be computed, 'ROC'
      [default] or 'PR' for the Precision-Recall-curve.
    summation_method: (Optional) Specifies the [Riemann summation method](
        https://en.wikipedia.org/wiki/Riemann_sum) used.
        'interpolation' (default) applies mid-point summation scheme for `ROC`.
        For PR-AUC, interpolates (true/false) positives but not the ratio that
        is precision (see Davis & Goadrich 2006 for details);
        'minoring' applies left summation
        for increasing intervals and right summation for decreasing intervals;
        'majoring' does the opposite.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
    thresholds: (Optional) A list of floating point values to use as the
      thresholds for discretizing the curve. If set, the `num_thresholds`
      parameter is ignored. Values should be in [0, 1]. Endpoint thresholds
      equal to {-epsilon, 1+epsilon} for a small positive epsilon value will
      be automatically included with these to correctly handle predictions
      equal to exactly 0 or 1.
    multi_label: boolean indicating whether multilabel data should be
      treated as such, wherein AUC is computed separately for each label and
      then averaged across labels, or (when False) if the data should be
      flattened into a single label before AUC computation. In the latter
      case, when multilabel data is passed to AUC, each label-prediction pair
      is treated as an individual data point. Should be set to False for
      multi-class data.
    num_labels: (Optional) The number of labels, used when `multi_label` is
      True. If `num_labels` is not specified, then state variables get created
      on the first call to `update_state`.
    label_weights: (Optional) list, array, or tensor of non-negative weights
      used to compute AUCs for multilabel data. When `multi_label` is True,
      the weights are applied to the individual label AUCs when they are
      averaged to produce the multi-label AUC. When it's False, they are used
      to weight the individual label predictions in computing the confusion
      matrix on the flattened data. Note that this is unlike class_weights in
      that class_weights weights the example depending on the value of its
      label, whereas label_weights depends only on the index of that label
      before flattening; therefore `label_weights` should not be used for
      multi-class data.
    from_logits: boolean indicating whether the predictions (`y_pred` in
      `update_state`) are probabilities or sigmoid logits. As a rule of thumb,
      when using a keras loss, the `from_logits` constructor argument of the
      loss should match the AUC `from_logits` constructor argument.

  Standalone usage:

  >>> m = tf.keras.metrics.AUC(num_thresholds=3)
  >>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
  >>> # threshold values are [0 - 1e-7, 0.5, 1 + 1e-7]
  >>> # tp = [2, 1, 0], fp = [2, 0, 0], fn = [0, 1, 2], tn = [0, 2, 2]
  >>> # tp_rate = recall = [1, 0.5, 0], fp_rate = [1, 0, 0]
  >>> # auc = ((((1+0.5)/2)*(1-0)) + (((0.5+0)/2)*(0-0))) = 0.75
  >>> m.result().numpy()
  0.75

  >>> m.reset_state()
  >>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
  ...                sample_weight=[1, 0, 0, 1])
  >>> m.result().numpy()
  1.0

  Usage with `compile()` API:

  ```python
  # Reports the AUC of a model outputing a probability.
  model.compile(optimizer='sgd',
                loss=tf.keras.losses.BinaryCrossentropy(),
                metrics=[tf.keras.metrics.AUC()])

  # Reports the AUC of a model outputing a logit.
  model.compile(optimizer='sgd',
                loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
                metrics=[tf.keras.metrics.AUC(from_logits=True)])
  ```
  """

  def __init__(self,
               num_thresholds=200,
               curve='ROC',
               summation_method='interpolation',
               name=None,
               dtype=None,
               thresholds=None,
               multi_label=False,
               num_labels=None,
               label_weights=None,
               from_logits=False):
    # Validate configurations.
    if isinstance(curve, metrics_utils.AUCCurve) and curve not in list(
        metrics_utils.AUCCurve):
      raise ValueError('Invalid curve: "{}". Valid options are: "{}"'.format(
          curve, list(metrics_utils.AUCCurve)))
    if isinstance(
        summation_method,
        metrics_utils.AUCSummationMethod) and summation_method not in list(
            metrics_utils.AUCSummationMethod):
      raise ValueError(
          'Invalid summation method: "{}". Valid options are: "{}"'.format(
              summation_method, list(metrics_utils.AUCSummationMethod)))

    # Update properties.
    if thresholds is not None:
      # If specified, use the supplied thresholds.
      self.num_thresholds = len(thresholds) + 2
      thresholds = sorted(thresholds)
      self._thresholds_distributed_evenly = (
          metrics_utils.is_evenly_distributed_thresholds(
              np.array([0.0] + thresholds + [1.0])))
    else:
      if num_thresholds <= 1:
        raise ValueError('`num_thresholds` must be > 1.')

      # Otherwise, linearly interpolate (num_thresholds - 2) thresholds in
      # (0, 1).
      self.num_thresholds = num_thresholds
      thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
                    for i in range(num_thresholds - 2)]
      self._thresholds_distributed_evenly = True

    # Add an endpoint "threshold" below zero and above one for either
    # threshold method to account for floating point imprecisions.
    self._thresholds = np.array([0.0 - backend.epsilon()] + thresholds +
                                [1.0 + backend.epsilon()])

    if isinstance(curve, metrics_utils.AUCCurve):
      self.curve = curve
    else:
      self.curve = metrics_utils.AUCCurve.from_str(curve)
    if isinstance(summation_method, metrics_utils.AUCSummationMethod):
      self.summation_method = summation_method
    else:
      self.summation_method = metrics_utils.AUCSummationMethod.from_str(
          summation_method)
    super(AUC, self).__init__(name=name, dtype=dtype)

    # Handle multilabel arguments.
    self.multi_label = multi_label
    if label_weights is not None:
      label_weights = constant_op.constant(label_weights, dtype=self.dtype)
      checks = [
          check_ops.assert_non_negative(
              label_weights,
              message='All values of `label_weights` must be non-negative.')
      ]
      with ops.control_dependencies(checks):
        self.label_weights = label_weights

    else:
      self.label_weights = None

    self._from_logits = from_logits

    self._built = False
    if self.multi_label:
      if num_labels:
        shape = tensor_shape.TensorShape([None, num_labels])
        self._build(shape)
    else:
      if num_labels:
        raise ValueError(
            '`num_labels` is needed only when `multi_label` is True.')
      self._build(None)

  @property
  def thresholds(self):
    """The thresholds used for evaluating AUC."""
    return list(self._thresholds)

  def _build(self, shape):
    """Initialize TP, FP, TN, and FN tensors, given the shape of the data."""
    if self.multi_label:
      if shape.ndims != 2:
        raise ValueError('`y_true` must have rank=2 when `multi_label` is '
                         'True. Found rank %s.' % shape.ndims)
      self._num_labels = shape[1]
      variable_shape = tensor_shape.TensorShape(
          [tensor_shape.Dimension(self.num_thresholds), self._num_labels])

    else:
      variable_shape = tensor_shape.TensorShape(
          [tensor_shape.Dimension(self.num_thresholds)])
    self._build_input_shape = shape
    # Create metric variables
    self.true_positives = self.add_weight(
        'true_positives',
        shape=variable_shape,
        initializer=init_ops.zeros_initializer)
    self.true_negatives = self.add_weight(
        'true_negatives',
        shape=variable_shape,
        initializer=init_ops.zeros_initializer)
    self.false_positives = self.add_weight(
        'false_positives',
        shape=variable_shape,
        initializer=init_ops.zeros_initializer)
    self.false_negatives = self.add_weight(
        'false_negatives',
        shape=variable_shape,
        initializer=init_ops.zeros_initializer)

    if self.multi_label:
      with ops.init_scope():
        # This should only be necessary for handling v1 behavior. In v2, AUC
        # should be initialized outside of any tf.functions, and therefore in
        # eager mode.
        if not context.executing_eagerly():
          backend._initialize_variables(backend._get_session())  # pylint: disable=protected-access

    self._built = True

  def update_state(self, y_true, y_pred, sample_weight=None):
    """Accumulates 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.
    """
    deps = []
    if not self._built:
      self._build(tensor_shape.TensorShape(y_pred.shape))

    if self.multi_label or (self.label_weights is not None):
      # y_true should have shape (number of examples, number of labels).
      shapes = [
          (y_true, ('N', 'L'))
      ]
      if self.multi_label:
        # TP, TN, FP, and FN should all have shape
        # (number of thresholds, number of labels).
        shapes.extend([(self.true_positives, ('T', 'L')),
                       (self.true_negatives, ('T', 'L')),
                       (self.false_positives, ('T', 'L')),
                       (self.false_negatives, ('T', 'L'))])
      if self.label_weights is not None:
        # label_weights should be of length equal to the number of labels.
        shapes.append((self.label_weights, ('L',)))
      deps = [
          check_ops.assert_shapes(
              shapes, message='Number of labels is not consistent.')
      ]

    # Only forward label_weights to update_confusion_matrix_variables when
    # multi_label is False. Otherwise the averaging of individual label AUCs is
    # handled in AUC.result
    label_weights = None if self.multi_label else self.label_weights

    if self._from_logits:
      y_pred = activations.sigmoid(y_pred)

    with ops.control_dependencies(deps):
      return metrics_utils.update_confusion_matrix_variables(
          {
              metrics_utils.ConfusionMatrix.TRUE_POSITIVES:
                  self.true_positives,
              metrics_utils.ConfusionMatrix.TRUE_NEGATIVES:
                  self.true_negatives,
              metrics_utils.ConfusionMatrix.FALSE_POSITIVES:
                  self.false_positives,
              metrics_utils.ConfusionMatrix.FALSE_NEGATIVES:
                  self.false_negatives,
          },
          y_true,
          y_pred,
          self._thresholds,
          thresholds_distributed_evenly=self._thresholds_distributed_evenly,
          sample_weight=sample_weight,
          multi_label=self.multi_label,
          label_weights=label_weights)

  def interpolate_pr_auc(self):
    """Interpolation formula inspired by section 4 of Davis & Goadrich 2006.

    https://www.biostat.wisc.edu/~page/rocpr.pdf

    Note here we derive & use a closed formula not present in the paper
    as follows:

      Precision = TP / (TP + FP) = TP / P

    Modeling all of TP (true positive), FP (false positive) and their sum
    P = TP + FP (predicted positive) as varying linearly within each interval
    [A, B] between successive thresholds, we get

      Precision slope = dTP / dP
                      = (TP_B - TP_A) / (P_B - P_A)
                      = (TP - TP_A) / (P - P_A)
      Precision = (TP_A + slope * (P - P_A)) / P

    The area within the interval is (slope / total_pos_weight) times

      int_A^B{Precision.dP} = int_A^B{(TP_A + slope * (P - P_A)) * dP / P}
      int_A^B{Precision.dP} = int_A^B{slope * dP + intercept * dP / P}

    where intercept = TP_A - slope * P_A = TP_B - slope * P_B, resulting in

      int_A^B{Precision.dP} = TP_B - TP_A + intercept * log(P_B / P_A)

    Bringing back the factor (slope / total_pos_weight) we'd put aside, we get

      slope * [dTP + intercept *  log(P_B / P_A)] / total_pos_weight

    where dTP == TP_B - TP_A.

    Note that when P_A == 0 the above calculation simplifies into

      int_A^B{Precision.dTP} = int_A^B{slope * dTP} = slope * (TP_B - TP_A)

    which is really equivalent to imputing constant precision throughout the
    first bucket having >0 true positives.

    Returns:
      pr_auc: an approximation of the area under the P-R curve.
    """
    dtp = self.true_positives[:self.num_thresholds -
                              1] - self.true_positives[1:]
    p = self.true_positives + self.false_positives
    dp = p[:self.num_thresholds - 1] - p[1:]
    prec_slope = math_ops.div_no_nan(
        dtp, math_ops.maximum(dp, 0), name='prec_slope')
    intercept = self.true_positives[1:] - math_ops.multiply(prec_slope, p[1:])

    safe_p_ratio = array_ops.where(
        math_ops.logical_and(p[:self.num_thresholds - 1] > 0, p[1:] > 0),
        math_ops.div_no_nan(
            p[:self.num_thresholds - 1],
            math_ops.maximum(p[1:], 0),
            name='recall_relative_ratio'),
        array_ops.ones_like(p[1:]))

    pr_auc_increment = math_ops.div_no_nan(
        prec_slope * (dtp + intercept * math_ops.log(safe_p_ratio)),
        math_ops.maximum(self.true_positives[1:] + self.false_negatives[1:], 0),
        name='pr_auc_increment')

    if self.multi_label:
      by_label_auc = math_ops.reduce_sum(
          pr_auc_increment, name=self.name + '_by_label', axis=0)
      if self.label_weights is None:
        # Evenly weighted average of the label AUCs.
        return math_ops.reduce_mean(by_label_auc, name=self.name)
      else:
        # Weighted average of the label AUCs.
        return math_ops.div_no_nan(
            math_ops.reduce_sum(
                math_ops.multiply(by_label_auc, self.label_weights)),
            math_ops.reduce_sum(self.label_weights),
            name=self.name)
    else:
      return math_ops.reduce_sum(pr_auc_increment, name='interpolate_pr_auc')

  def result(self):
    if (self.curve == metrics_utils.AUCCurve.PR and
        self.summation_method == metrics_utils.AUCSummationMethod.INTERPOLATION
       ):
      # This use case is different and is handled separately.
      return self.interpolate_pr_auc()

    # Set `x` and `y` values for the curves based on `curve` config.
    recall = math_ops.div_no_nan(self.true_positives,
                                 self.true_positives + self.false_negatives)
    if self.curve == metrics_utils.AUCCurve.ROC:
      fp_rate = math_ops.div_no_nan(self.false_positives,
                                    self.false_positives + self.true_negatives)
      x = fp_rate
      y = recall
    else:  # curve == 'PR'.
      precision = math_ops.div_no_nan(
          self.true_positives, self.true_positives + self.false_positives)
      x = recall
      y = precision

    # Find the rectangle heights based on `summation_method`.
    if self.summation_method == metrics_utils.AUCSummationMethod.INTERPOLATION:
      # Note: the case ('PR', 'interpolation') has been handled above.
      heights = (y[:self.num_thresholds - 1] + y[1:]) / 2.
    elif self.summation_method == metrics_utils.AUCSummationMethod.MINORING:
      heights = math_ops.minimum(y[:self.num_thresholds - 1], y[1:])
    else:  # self.summation_method = metrics_utils.AUCSummationMethod.MAJORING:
      heights = math_ops.maximum(y[:self.num_thresholds - 1], y[1:])

    # Sum up the areas of all the rectangles.
    if self.multi_label:
      riemann_terms = math_ops.multiply(x[:self.num_thresholds - 1] - x[1:],
                                        heights)
      by_label_auc = math_ops.reduce_sum(
          riemann_terms, name=self.name + '_by_label', axis=0)

      if self.label_weights is None:
        # Unweighted average of the label AUCs.
        return math_ops.reduce_mean(by_label_auc, name=self.name)
      else:
        # Weighted average of the label AUCs.
        return math_ops.div_no_nan(
            math_ops.reduce_sum(
                math_ops.multiply(by_label_auc, self.label_weights)),
            math_ops.reduce_sum(self.label_weights),
            name=self.name)
    else:
      return math_ops.reduce_sum(
          math_ops.multiply(x[:self.num_thresholds - 1] - x[1:], heights),
          name=self.name)

  def reset_state(self):
    if self._built:
      confusion_matrix_variables = (self.true_positives, self.true_negatives,
                                    self.false_positives, self.false_negatives)
      if self.multi_label:
        backend.batch_set_value(
            [(v, np.zeros((self.num_thresholds, self._num_labels)))
             for v in confusion_matrix_variables])
      else:
        backend.batch_set_value([(v, np.zeros((self.num_thresholds,)))
                                 for v in confusion_matrix_variables])

  def get_config(self):
    if is_tensor_or_variable(self.label_weights):
      label_weights = backend.eval(self.label_weights)
    else:
      label_weights = self.label_weights
    config = {
        'num_thresholds': self.num_thresholds,
        'curve': self.curve.value,
        'summation_method': self.summation_method.value,
        # We remove the endpoint thresholds as an inverse of how the thresholds
        # were initialized. This ensures that a metric initialized from this
        # config has the same thresholds.
        'thresholds': self.thresholds[1:-1],
        'multi_label': self.multi_label,
        'label_weights': label_weights
    }
    base_config = super(AUC, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.metrics.CosineSimilarity')
class CosineSimilarity(MeanMetricWrapper):
  """Computes the cosine similarity between the labels and predictions.

  `cosine similarity = (a . b) / ||a|| ||b||`

  See: [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity).

  This metric keeps the average cosine similarity between `predictions` and
  `labels` over a stream of data.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
    axis: (Optional) Defaults to -1. The dimension along which the cosine
      similarity is computed.

  Standalone usage:

  >>> # l2_norm(y_true) = [[0., 1.], [1./1.414, 1./1.414]]
  >>> # l2_norm(y_pred) = [[1., 0.], [1./1.414, 1./1.414]]
  >>> # l2_norm(y_true) . l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]]
  >>> # result = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
  >>> #        = ((0. + 0.) +  (0.5 + 0.5)) / 2
  >>> m = tf.keras.metrics.CosineSimilarity(axis=1)
  >>> m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]])
  >>> m.result().numpy()
  0.49999997

  >>> m.reset_state()
  >>> m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]],
  ...                sample_weight=[0.3, 0.7])
  >>> m.result().numpy()
  0.6999999

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.CosineSimilarity(axis=1)])
  ```
  """

  def __init__(self, name='cosine_similarity', dtype=None, axis=-1):
    super(CosineSimilarity, self).__init__(
        cosine_similarity, name, dtype=dtype, axis=axis)


@keras_export('keras.metrics.MeanAbsoluteError')
class MeanAbsoluteError(MeanMetricWrapper):
  """Computes the mean absolute error between the labels and predictions.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.MeanAbsoluteError()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
  >>> m.result().numpy()
  0.25

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  0.5

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.MeanAbsoluteError()])
  ```
  """

  def __init__(self, name='mean_absolute_error', dtype=None):
    super(MeanAbsoluteError, self).__init__(
        mean_absolute_error, name, dtype=dtype)


@keras_export('keras.metrics.MeanAbsolutePercentageError')
class MeanAbsolutePercentageError(MeanMetricWrapper):
  """Computes the mean absolute percentage error between `y_true` and `y_pred`.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.MeanAbsolutePercentageError()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
  >>> m.result().numpy()
  250000000.0

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  500000000.0

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.MeanAbsolutePercentageError()])
  ```
  """

  def __init__(self, name='mean_absolute_percentage_error', dtype=None):
    super(MeanAbsolutePercentageError, self).__init__(
        mean_absolute_percentage_error, name, dtype=dtype)


@keras_export('keras.metrics.MeanSquaredError')
class MeanSquaredError(MeanMetricWrapper):
  """Computes the mean squared error between `y_true` and `y_pred`.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.MeanSquaredError()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
  >>> m.result().numpy()
  0.25

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  0.5

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.MeanSquaredError()])
  ```
  """

  def __init__(self, name='mean_squared_error', dtype=None):
    super(MeanSquaredError, self).__init__(
        mean_squared_error, name, dtype=dtype)


@keras_export('keras.metrics.MeanSquaredLogarithmicError')
class MeanSquaredLogarithmicError(MeanMetricWrapper):
  """Computes the mean squared logarithmic error between `y_true` and `y_pred`.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.MeanSquaredLogarithmicError()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
  >>> m.result().numpy()
  0.12011322

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  0.24022643

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.MeanSquaredLogarithmicError()])
  ```
  """

  def __init__(self, name='mean_squared_logarithmic_error', dtype=None):
    super(MeanSquaredLogarithmicError, self).__init__(
        mean_squared_logarithmic_error, name, dtype=dtype)


@keras_export('keras.metrics.Hinge')
class Hinge(MeanMetricWrapper):
  """Computes the hinge metric between `y_true` and `y_pred`.

  `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are
  provided we will convert them to -1 or 1.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.Hinge()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
  >>> m.result().numpy()
  1.3

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  1.1

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.Hinge()])
  ```
  """

  def __init__(self, name='hinge', dtype=None):
    super(Hinge, self).__init__(hinge, name, dtype=dtype)


@keras_export('keras.metrics.SquaredHinge')
class SquaredHinge(MeanMetricWrapper):
  """Computes the squared hinge metric between `y_true` and `y_pred`.

  `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are
  provided we will convert them to -1 or 1.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.SquaredHinge()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
  >>> m.result().numpy()
  1.86

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  1.46

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.SquaredHinge()])
  ```
  """

  def __init__(self, name='squared_hinge', dtype=None):
    super(SquaredHinge, self).__init__(squared_hinge, name, dtype=dtype)


@keras_export('keras.metrics.CategoricalHinge')
class CategoricalHinge(MeanMetricWrapper):
  """Computes the categorical hinge metric between `y_true` and `y_pred`.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.CategoricalHinge()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
  >>> m.result().numpy()
  1.4000001

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  1.2

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.CategoricalHinge()])
  ```
  """

  def __init__(self, name='categorical_hinge', dtype=None):
    super(CategoricalHinge, self).__init__(categorical_hinge, name, dtype=dtype)


@keras_export('keras.metrics.RootMeanSquaredError')
class RootMeanSquaredError(Mean):
  """Computes root mean squared error metric between `y_true` and `y_pred`.

  Standalone usage:

  >>> m = tf.keras.metrics.RootMeanSquaredError()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
  >>> m.result().numpy()
  0.5

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  0.70710677

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.RootMeanSquaredError()])
  ```
  """

  def __init__(self, name='root_mean_squared_error', dtype=None):
    super(RootMeanSquaredError, self).__init__(name, dtype=dtype)

  def update_state(self, y_true, y_pred, sample_weight=None):
    """Accumulates root mean squared error 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.
    """
    y_true = math_ops.cast(y_true, self._dtype)
    y_pred = math_ops.cast(y_pred, self._dtype)
    y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
        y_pred, y_true)
    error_sq = math_ops.squared_difference(y_pred, y_true)
    return super(RootMeanSquaredError, self).update_state(
        error_sq, sample_weight=sample_weight)

  def result(self):
    return math_ops.sqrt(math_ops.div_no_nan(self.total, self.count))


@keras_export('keras.metrics.LogCoshError')
class LogCoshError(MeanMetricWrapper):
  """Computes the logarithm of the hyperbolic cosine of the prediction error.

  `logcosh = log((exp(x) + exp(-x))/2)`, where x is the error (y_pred - y_true)

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.LogCoshError()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
  >>> m.result().numpy()
  0.10844523

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  0.21689045

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.LogCoshError()])
  ```
  """

  def __init__(self, name='logcosh', dtype=None):
    super(LogCoshError, self).__init__(logcosh, name, dtype=dtype)


@keras_export('keras.metrics.Poisson')
class Poisson(MeanMetricWrapper):
  """Computes the Poisson metric between `y_true` and `y_pred`.

  `metric = y_pred - y_true * log(y_pred)`

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.Poisson()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
  >>> m.result().numpy()
  0.49999997

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  0.99999994

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.Poisson()])
  ```
  """

  def __init__(self, name='poisson', dtype=None):
    super(Poisson, self).__init__(poisson, name, dtype=dtype)


@keras_export('keras.metrics.KLDivergence')
class KLDivergence(MeanMetricWrapper):
  """Computes Kullback-Leibler divergence metric between `y_true` and `y_pred`.

  `metric = y_true * log(y_true / y_pred)`

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  Standalone usage:

  >>> m = tf.keras.metrics.KLDivergence()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
  >>> m.result().numpy()
  0.45814306

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  0.9162892

  Usage with `compile()` API:

  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[tf.keras.metrics.KLDivergence()])
  ```
  """

  def __init__(self, name='kullback_leibler_divergence', dtype=None):
    super(KLDivergence, self).__init__(
        kullback_leibler_divergence, name, dtype=dtype)


@keras_export('keras.metrics.MeanIoU')
class MeanIoU(Metric):
  """Computes the mean Intersection-Over-Union metric.

  Mean Intersection-Over-Union is a common evaluation metric for semantic image
  segmentation, which first computes the IOU for each semantic class and then
  computes the average over classes. IOU is defined as follows:
    IOU = true_positive / (true_positive + false_positive + false_negative).
  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 dimension =
      [num_classes, num_classes] will be allocated.
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.

  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)])
  ```
  """

  def __init__(self, num_classes, name=None, dtype=None):
    super(MeanIoU, self).__init__(name=name, dtype=dtype)
    self.num_classes = num_classes

    # Variable to accumulate the predictions in the confusion matrix.
    self.total_cm = self.add_weight(
        'total_confusion_matrix',
        shape=(num_classes, num_classes),
        initializer=init_ops.zeros_initializer)

  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.
    """

    y_true = math_ops.cast(y_true, self._dtype)
    y_pred = math_ops.cast(y_pred, self._dtype)

    # Flatten the input if its rank > 1.
    if y_pred.shape.ndims > 1:
      y_pred = array_ops.reshape(y_pred, [-1])

    if y_true.shape.ndims > 1:
      y_true = array_ops.reshape(y_true, [-1])

    if sample_weight is not None:
      sample_weight = math_ops.cast(sample_weight, self._dtype)
      if sample_weight.shape.ndims > 1:
        sample_weight = array_ops.reshape(sample_weight, [-1])

    # Accumulate the prediction to current confusion matrix.
    current_cm = confusion_matrix.confusion_matrix(
        y_true,
        y_pred,
        self.num_classes,
        weights=sample_weight,
        dtype=self._dtype)
    return self.total_cm.assign_add(current_cm)

  def result(self):
    """Compute the mean intersection-over-union via the confusion matrix."""
    sum_over_row = math_ops.cast(
        math_ops.reduce_sum(self.total_cm, axis=0), dtype=self._dtype)
    sum_over_col = math_ops.cast(
        math_ops.reduce_sum(self.total_cm, axis=1), dtype=self._dtype)
    true_positives = math_ops.cast(
        array_ops.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

    # The mean is only computed over classes that appear in the
    # label or prediction tensor. If the denominator is 0, we need to
    # ignore the class.
    num_valid_entries = math_ops.reduce_sum(
        math_ops.cast(math_ops.not_equal(denominator, 0), dtype=self._dtype))

    iou = math_ops.div_no_nan(true_positives, denominator)

    return math_ops.div_no_nan(
        math_ops.reduce_sum(iou, name='mean_iou'), num_valid_entries)

  def reset_state(self):
    backend.set_value(
        self.total_cm, np.zeros((self.num_classes, self.num_classes)))

  def get_config(self):
    config = {'num_classes': self.num_classes}
    base_config = super(MeanIoU, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.metrics.MeanTensor')
class MeanTensor(Metric):
  """Computes the element-wise (weighted) mean of the given tensors.

  `MeanTensor` returns a tensor with the same shape of the input tensors. The
  mean value is updated by keeping local variables `total` and `count`. The
  `total` tracks the sum of the weighted values, and `count` stores the sum of
  the weighted counts.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
    shape: (Optional) A list of integers, a tuple of integers, or a 1-D Tensor
      of type int32. If not specified, the shape is inferred from the values at
      the first call of update_state.

  Standalone usage:

  >>> m = tf.keras.metrics.MeanTensor()
  >>> m.update_state([0, 1, 2, 3])
  >>> m.update_state([4, 5, 6, 7])
  >>> m.result().numpy()
  array([2., 3., 4., 5.], dtype=float32)

  >>> m.update_state([12, 10, 8, 6], sample_weight= [0, 0.2, 0.5, 1])
  >>> m.result().numpy()
  array([2.       , 3.6363635, 4.8      , 5.3333335], dtype=float32)

  >>> m = tf.keras.metrics.MeanTensor(dtype=tf.float64, shape=(1, 4))
  >>> m.result().numpy()
  array([[0., 0., 0., 0.]])
  >>> m.update_state([[0, 1, 2, 3]])
  >>> m.update_state([[4, 5, 6, 7]])
  >>> m.result().numpy()
  array([[2., 3., 4., 5.]])
  """

  def __init__(self, name='mean_tensor', dtype=None, shape=None):
    super(MeanTensor, self).__init__(name=name, dtype=dtype)
    self._shape = None
    self._total = None
    self._count = None
    self._built = False
    if shape is not None:
      self._build(shape)

  def _build(self, shape):
    self._shape = tensor_shape.TensorShape(shape)
    self._build_input_shape = self._shape
    # Create new state variables
    self._total = self.add_weight(
        'total', shape=shape, initializer=init_ops.zeros_initializer)
    self._count = self.add_weight(
        'count', shape=shape, initializer=init_ops.zeros_initializer)
    with ops.init_scope():
      if not context.executing_eagerly():
        backend._initialize_variables(backend._get_session())  # pylint: disable=protected-access
    self._built = True

  @property
  def total(self):
    return self._total if self._built else None

  @property
  def count(self):
    return self._count if self._built else None

  def update_state(self, values, sample_weight=None):
    """Accumulates statistics for computing the element-wise mean.

    Args:
      values: Per-example value.
      sample_weight: Optional weighting of each example. Defaults to 1.

    Returns:
      Update op.
    """
    values = math_ops.cast(values, self._dtype)
    if not self._built:
      self._build(values.shape)
    elif values.shape != self._shape:
      raise ValueError('MeanTensor input values must always have the same '
                       'shape. Expected shape (set during the first call): {}. '
                       'Got: {}'.format(self._shape, values.shape))

    num_values = array_ops.ones_like(values)
    if sample_weight is not None:
      sample_weight = math_ops.cast(sample_weight, self._dtype)

      # Update dimensions of weights to match with values if possible.
      values, _, sample_weight = losses_utils.squeeze_or_expand_dimensions(
          values, sample_weight=sample_weight)
      try:
        # Broadcast weights if possible.
        sample_weight = weights_broadcast_ops.broadcast_weights(
            sample_weight, values)
      except ValueError:
        # Reduce values to same ndim as weight array
        ndim = backend.ndim(values)
        weight_ndim = backend.ndim(sample_weight)
        values = math_ops.reduce_mean(
            values, axis=list(range(weight_ndim, ndim)))

      num_values = math_ops.multiply(num_values, sample_weight)
      values = math_ops.multiply(values, sample_weight)

    update_total_op = self._total.assign_add(values)
    with ops.control_dependencies([update_total_op]):
      return self._count.assign_add(num_values)

  def result(self):
    if not self._built:
      raise ValueError(
          'MeanTensor does not have any result yet. Please call the MeanTensor '
          'instance or use `.update_state(value)` before retrieving the result.'
          )
    return math_ops.div_no_nan(self.total, self.count)

  def reset_state(self):
    if self._built:
      backend.batch_set_value(
          [(v, np.zeros(self._shape.as_list())) for v in self.variables])


@keras_export('keras.metrics.BinaryCrossentropy')
class BinaryCrossentropy(MeanMetricWrapper):
  """Computes the crossentropy metric between the labels and predictions.

  This is the crossentropy metric class to be used when there are only two
  label classes (0 and 1).

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
    from_logits: (Optional )Whether output is expected to be a logits tensor.
      By default, we consider that output encodes a probability distribution.
    label_smoothing: (Optional) Float in [0, 1]. When > 0, label values are
      smoothed, meaning the confidence on label values are relaxed.
      e.g. `label_smoothing=0.2` means that we will use a value of `0.1` for
      label `0` and `0.9` for label `1`".

  Standalone usage:

  >>> m = tf.keras.metrics.BinaryCrossentropy()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
  >>> m.result().numpy()
  0.81492424

  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
  ...                sample_weight=[1, 0])
  >>> m.result().numpy()
  0.9162905

  Usage with `compile()` API:

  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.BinaryCrossentropy()])
  ```
  """

  def __init__(self,
               name='binary_crossentropy',
               dtype=None,
               from_logits=False,
               label_smoothing=0):
    super(BinaryCrossentropy, self).__init__(
        binary_crossentropy,
        name,
        dtype=dtype,
        from_logits=from_logits,
        label_smoothing=label_smoothing)


@keras_export('keras.metrics.CategoricalCrossentropy')
class CategoricalCrossentropy(MeanMetricWrapper):
  """Computes the crossentropy metric between the labels and predictions.

  This is the crossentropy metric class to be used when there are multiple
  label classes (2 or more). Here we assume that labels are given as a `one_hot`
  representation. eg., When labels values are [2, 0, 1],
   `y_true` = [[0, 0, 1], [1, 0, 0], [0, 1, 0]].

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
    from_logits: (Optional) Whether output is expected to be a logits tensor.
      By default, we consider that output encodes a probability distribution.
    label_smoothing: (Optional) Float in [0, 1]. When > 0, label values are
      smoothed, meaning the confidence on label values are relaxed. e.g.
      `label_smoothing=0.2` means that we will use a value of `0.1` for label
      `0` and `0.9` for label `1`"

  Standalone usage:

  >>> # EPSILON = 1e-7, y = y_true, y` = y_pred
  >>> # y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
  >>> # y` = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
  >>> # xent = -sum(y * log(y'), axis = -1)
  >>> #      = -((log 0.95), (log 0.1))
  >>> #      = [0.051, 2.302]
  >>> # Reduced xent = (0.051 + 2.302) / 2
  >>> m = tf.keras.metrics.CategoricalCrossentropy()
  >>> m.update_state([[0, 1, 0], [0, 0, 1]],
  ...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
  >>> m.result().numpy()
  1.1769392

  >>> m.reset_state()
  >>> m.update_state([[0, 1, 0], [0, 0, 1]],
  ...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
  ...                sample_weight=tf.constant([0.3, 0.7]))
  >>> m.result().numpy()
  1.6271976

  Usage with `compile()` API:

  ```python
  model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.CategoricalCrossentropy()])
  ```
  """

  def __init__(self,
               name='categorical_crossentropy',
               dtype=None,
               from_logits=False,
               label_smoothing=0):
    super(CategoricalCrossentropy, self).__init__(
        categorical_crossentropy,
        name,
        dtype=dtype,
        from_logits=from_logits,
        label_smoothing=label_smoothing)


@keras_export('keras.metrics.SparseCategoricalCrossentropy')
class SparseCategoricalCrossentropy(MeanMetricWrapper):
  """Computes the crossentropy metric between the labels and predictions.

  Use this crossentropy metric when there are two or more label classes.
  We expect labels to be provided as integers. If you want to provide labels
  using `one-hot` representation, please use `CategoricalCrossentropy` metric.
  There should be `# classes` floating point values per feature for `y_pred`
  and a single floating point value per feature for `y_true`.

  In the snippet below, there is a single floating point value per example for
  `y_true` and `# classes` floating pointing values per example for `y_pred`.
  The shape of `y_true` is `[batch_size]` and the shape of `y_pred` is
  `[batch_size, num_classes]`.

  Args:
    name: (Optional) string name of the metric instance.
    dtype: (Optional) data type of the metric result.
    from_logits: (Optional) Whether output is expected to be a logits tensor.
      By default, we consider that output encodes a probability distribution.
    axis: (Optional) Defaults to -1. The dimension along which the metric is
      computed.

  Standalone usage:

  >>> # y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
  >>> # logits = log(y_pred)
  >>> # softmax = exp(logits) / sum(exp(logits), axis=-1)
  >>> # softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
  >>> # xent = -sum(y * log(softmax), 1)
  >>> # log(softmax) = [[-2.9957, -0.0513, -16.1181],
  >>> #                [-2.3026, -0.2231, -2.3026]]
  >>> # y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]
  >>> # xent = [0.0513, 2.3026]
  >>> # Reduced xent = (0.0513 + 2.3026) / 2
  >>> m = tf.keras.metrics.SparseCategoricalCrossentropy()
  >>> m.update_state([1, 2],
  ...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
  >>> m.result().numpy()
  1.1769392

  >>> m.reset_state()
  >>> m.update_state([1, 2],
  ...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
  ...                sample_weight=tf.constant([0.3, 0.7]))
  >>> m.result().numpy()
  1.6271976

  Usage with `compile()` API:

  ```python
  model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.SparseCategoricalCrossentropy()])
  ```
  """

  def __init__(self,
               name='sparse_categorical_crossentropy',
               dtype=None,
               from_logits=False,
               axis=-1):
    super(SparseCategoricalCrossentropy, self).__init__(
        sparse_categorical_crossentropy,
        name,
        dtype=dtype,
        from_logits=from_logits,
        axis=axis)


class SumOverBatchSize(Reduce):
  """Computes the weighted sum over batch size of the given values.

  For example, if values is [1, 3, 5, 7] then the metric value is 4.
  If the weights were specified as [1, 1, 0, 0] then the value would be 1.

  This metric creates two variables, `total` and `count` that are used to
  compute the average of `values`. This average is ultimately returned as sum
  over batch size which is an idempotent operation that simply divides `total`
  by `count`.

  If `sample_weight` is `None`, weights default to 1.  Use `sample_weight` of 0
  to mask values.
  """

  def __init__(self, name='sum_over_batch_size', dtype=None):
    super(SumOverBatchSize, self).__init__(
        reduction=metrics_utils.Reduction.SUM_OVER_BATCH_SIZE,
        name=name,
        dtype=dtype)


class SumOverBatchSizeMetricWrapper(SumOverBatchSize):
  """Wraps a function with the `SumOverBatchSizeMetricWrapper` metric."""

  def __init__(self, fn, name=None, dtype=None, **kwargs):
    """Creates a `SumOverBatchSizeMetricWrapper` instance.

    Args:
      fn: The metric function to wrap, with signature `fn(y_true, y_pred,
        **kwargs)`.
      name: (Optional) string name of the metric instance.
      dtype: (Optional) data type of the metric result.
      **kwargs: The keyword arguments that are passed on to `fn`.
    """
    super(SumOverBatchSizeMetricWrapper, self).__init__(name=name, dtype=dtype)
    self._fn = fn
    self._fn_kwargs = kwargs

  def update_state(self, y_true, y_pred, sample_weight=None):
    y_true = math_ops.cast(y_true, self._dtype)
    y_pred = math_ops.cast(y_pred, self._dtype)
    y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
        y_pred, y_true)

    ag_fn = autograph.tf_convert(self._fn, ag_ctx.control_status_ctx())
    matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
    return super(SumOverBatchSizeMetricWrapper, self).update_state(
        matches, sample_weight=sample_weight)

  def get_config(self):
    config = {}
    for k, v in self._fn_kwargs.items():
      config[k] = backend.eval(v) if is_tensor_or_variable(v) else v
    base_config = super(SumOverBatchSizeMetricWrapper, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


def accuracy(y_true, y_pred):
  [y_pred, y_true], _ = \
      metrics_utils.ragged_assert_compatible_and_get_flat_values(
          [y_pred, y_true])
  y_true.shape.assert_is_compatible_with(y_pred.shape)
  if y_true.dtype != y_pred.dtype:
    y_pred = math_ops.cast(y_pred, y_true.dtype)
  return math_ops.cast(math_ops.equal(y_true, y_pred), backend.floatx())


@keras_export('keras.metrics.binary_accuracy')
@dispatch.add_dispatch_support
def binary_accuracy(y_true, y_pred, threshold=0.5):
  """Calculates how often predictions match binary labels.

  Standalone usage:
  >>> y_true = [[1], [1], [0], [0]]
  >>> y_pred = [[1], [1], [0], [0]]
  >>> m = tf.keras.metrics.binary_accuracy(y_true, y_pred)
  >>> assert m.shape == (4,)
  >>> m.numpy()
  array([1., 1., 1., 1.], dtype=float32)

  Args:
    y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`.
    y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.
    threshold: (Optional) Float representing the threshold for deciding whether
      prediction values are 1 or 0.

  Returns:
    Binary accuracy values. shape = `[batch_size, d0, .. dN-1]`
  """
  y_pred = ops.convert_to_tensor_v2_with_dispatch(y_pred)
  threshold = math_ops.cast(threshold, y_pred.dtype)
  y_pred = math_ops.cast(y_pred > threshold, y_pred.dtype)
  return backend.mean(math_ops.equal(y_true, y_pred), axis=-1)


@keras_export('keras.metrics.categorical_accuracy')
@dispatch.add_dispatch_support
def categorical_accuracy(y_true, y_pred):
  """Calculates how often predictions match one-hot labels.

  Standalone usage:
  >>> y_true = [[0, 0, 1], [0, 1, 0]]
  >>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
  >>> m = tf.keras.metrics.categorical_accuracy(y_true, y_pred)
  >>> assert m.shape == (2,)
  >>> m.numpy()
  array([0., 1.], dtype=float32)

  You can provide logits of classes as `y_pred`, since argmax of
  logits and probabilities are same.

  Args:
    y_true: One-hot ground truth values.
    y_pred: The prediction values.

  Returns:
    Categorical accuracy values.
  """
  return math_ops.cast(
      math_ops.equal(
          math_ops.argmax(y_true, axis=-1), math_ops.argmax(y_pred, axis=-1)),
      backend.floatx())


@keras_export('keras.metrics.sparse_categorical_accuracy')
@dispatch.add_dispatch_support
def sparse_categorical_accuracy(y_true, y_pred):
  """Calculates how often predictions match integer labels.

  Standalone usage:
  >>> y_true = [2, 1]
  >>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
  >>> m = tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
  >>> assert m.shape == (2,)
  >>> m.numpy()
  array([0., 1.], dtype=float32)

  You can provide logits of classes as `y_pred`, since argmax of
  logits and probabilities are same.

  Args:
    y_true: Integer ground truth values.
    y_pred: The prediction values.

  Returns:
    Sparse categorical accuracy values.
  """
  y_pred = ops.convert_to_tensor_v2_with_dispatch(y_pred)
  y_true = ops.convert_to_tensor_v2_with_dispatch(y_true)
  y_pred_rank = y_pred.shape.ndims
  y_true_rank = y_true.shape.ndims
  # If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
  if (y_true_rank is not None) and (y_pred_rank is not None) and (len(
      backend.int_shape(y_true)) == len(backend.int_shape(y_pred))):
    y_true = array_ops.squeeze(y_true, [-1])
  y_pred = math_ops.argmax(y_pred, axis=-1)

  # If the predicted output and actual output types don't match, force cast them
  # to match.
  if backend.dtype(y_pred) != backend.dtype(y_true):
    y_pred = math_ops.cast(y_pred, backend.dtype(y_true))

  return math_ops.cast(math_ops.equal(y_true, y_pred), backend.floatx())


@keras_export('keras.metrics.top_k_categorical_accuracy')
@dispatch.add_dispatch_support
def top_k_categorical_accuracy(y_true, y_pred, k=5):
  """Computes how often targets are in the top `K` predictions.

  Standalone usage:
  >>> y_true = [[0, 0, 1], [0, 1, 0]]
  >>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
  >>> m = tf.keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=3)
  >>> assert m.shape == (2,)
  >>> m.numpy()
  array([1., 1.], dtype=float32)

  Args:
    y_true: The ground truth values.
    y_pred: The prediction values.
    k: (Optional) Number of top elements to look at for computing accuracy.
      Defaults to 5.

  Returns:
    Top K categorical accuracy value.
  """
  return math_ops.cast(
      nn.in_top_k(
          y_pred, math_ops.argmax(y_true, axis=-1), k), backend.floatx())


@keras_export('keras.metrics.sparse_top_k_categorical_accuracy')
@dispatch.add_dispatch_support
def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
  """Computes how often integer targets are in the top `K` predictions.

  Standalone usage:
  >>> y_true = [2, 1]
  >>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
  >>> m = tf.keras.metrics.sparse_top_k_categorical_accuracy(
  ...     y_true, y_pred, k=3)
  >>> assert m.shape == (2,)
  >>> m.numpy()
  array([1., 1.], dtype=float32)

  Args:
    y_true: tensor of true targets.
    y_pred: tensor of predicted targets.
    k: (Optional) Number of top elements to look at for computing accuracy.
      Defaults to 5.

  Returns:
    Sparse top K categorical accuracy value.
  """
  y_pred_rank = ops.convert_to_tensor_v2_with_dispatch(y_pred).shape.ndims
  y_true_rank = ops.convert_to_tensor_v2_with_dispatch(y_true).shape.ndims
  # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,)
  if (y_true_rank is not None) and (y_pred_rank is not None):
    if y_pred_rank > 2:
      y_pred = array_ops.reshape(y_pred, [-1, y_pred.shape[-1]])
    if y_true_rank > 1:
      y_true = array_ops.reshape(y_true, [-1])

  return math_ops.cast(
      nn.in_top_k(y_pred, math_ops.cast(y_true, 'int32'), k), backend.floatx())


def cosine_proximity(y_true, y_pred, axis=-1):
  """Computes the cosine similarity between labels and predictions.

  Args:
    y_true: The ground truth values.
    y_pred: The prediction values.
    axis: (Optional) Defaults to -1. The dimension along which the cosine
      similarity is computed.

  Returns:
    Cosine similarity value.
  """
  y_true = nn.l2_normalize(y_true, axis=axis)
  y_pred = nn.l2_normalize(y_pred, axis=axis)
  return math_ops.reduce_sum(y_true * y_pred, axis=axis)

# Aliases

acc = ACC = accuracy
bce = BCE = binary_crossentropy
mse = MSE = mean_squared_error
mae = MAE = mean_absolute_error
mape = MAPE = mean_absolute_percentage_error
msle = MSLE = mean_squared_logarithmic_error
cosine_similarity = cosine_proximity
log_cosh = logcosh


def clone_metric(metric):
  """Returns a clone of the metric if stateful, otherwise returns it as is."""
  if isinstance(metric, Metric):
    with ops.init_scope():
      return metric.__class__.from_config(metric.get_config())
  return metric


def clone_metrics(metrics):
  """Clones the given metric list/dict."""
  return nest.map_structure(clone_metric, metrics)


@keras_export('keras.metrics.serialize')
def serialize(metric):
  """Serializes metric function or `Metric` instance.

  Args:
    metric: A Keras `Metric` instance or a metric function.

  Returns:
    Metric configuration dictionary.
  """
  return serialize_keras_object(metric)


@keras_export('keras.metrics.deserialize')
def deserialize(config, custom_objects=None):
  """Deserializes a serialized metric class/function instance.

  Args:
    config: Metric configuration.
    custom_objects: Optional dictionary mapping names (strings) to custom
      objects (classes and functions) to be considered during deserialization.

  Returns:
      A Keras `Metric` instance or a metric function.
  """
  return deserialize_keras_object(
      config,
      module_objects=globals(),
      custom_objects=custom_objects,
      printable_module_name='metric function')


@keras_export('keras.metrics.get')
def get(identifier):
  """Retrieves a Keras metric as a `function`/`Metric` class instance.

  The `identifier` may be the string name of a metric function or class.

  >>> metric = tf.keras.metrics.get("categorical_crossentropy")
  >>> type(metric)
  <class 'function'>
  >>> metric = tf.keras.metrics.get("CategoricalCrossentropy")
  >>> type(metric)
  <class '...keras.metrics.CategoricalCrossentropy'>

  You can also specify `config` of the metric to this function by passing dict
  containing `class_name` and `config` as an identifier. Also note that the
  `class_name` must map to a `Metric` class

  >>> identifier = {"class_name": "CategoricalCrossentropy",
  ...               "config": {"from_logits": True}}
  >>> metric = tf.keras.metrics.get(identifier)
  >>> type(metric)
  <class '...keras.metrics.CategoricalCrossentropy'>

  Args:
    identifier: A metric identifier. One of None or string name of a metric
      function/class or metric configuration dictionary or a metric function or
      a metric class instance

  Returns:
    A Keras metric as a `function`/ `Metric` class instance.

  Raises:
    ValueError: If `identifier` cannot be interpreted.
  """
  if isinstance(identifier, dict):
    return deserialize(identifier)
  elif isinstance(identifier, str):
    return deserialize(str(identifier))
  elif callable(identifier):
    return identifier
  else:
    raise ValueError(
        'Could not interpret metric function identifier: {}'.format(identifier))


def is_built_in(cls):
  return cls.__module__ == Metric.__module__