code: # 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=unused-import
  # pylint: disable=g-classes-have-attributes
  """Built-in metrics.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  import abc
  import types
  
  import numpy as np
  import six
  
  from tensorflow.python.autograph.core import ag_ctx
  from tensorflow.python.autograph.impl import api as autograph
  from tensorflow.python.eager import def_function
  from keras import activations
  from keras import backend as K
  from keras.engine import base_layer
  from keras.engine import base_layer_utils
  from keras.engine import keras_tensor
  from keras.losses import binary_crossentropy
  from keras.losses import categorical_crossentropy
  from keras.losses import categorical_hinge
  from keras.losses import hinge
  from keras.losses import kullback_leibler_divergence
  from keras.losses import logcosh
  from keras.losses import mean_absolute_error
  from keras.losses import mean_absolute_percentage_error
  from keras.losses import mean_squared_error
  from keras.losses import mean_squared_logarithmic_error
  from keras.losses import poisson
  from keras.losses import sparse_categorical_crossentropy
  from keras.losses import squared_hinge
  from keras.saving.saved_model import metric_serialization
  from keras.utils import losses_utils
  from keras.utils import metrics_utils
  from keras.utils import tf_inspect
  from keras.utils.generic_utils import deserialize_keras_object
  from keras.utils.generic_utils import serialize_keras_object
  from keras.utils.generic_utils import to_list
  from keras.utils.tf_utils import is_tensor_or_variable
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.util.tf_export import keras_export
  from tensorflow.tools.docs import doc_controls
  
  
  @keras_export('keras.metrics.Metric')
  @six.add_metaclass(abc.ABCMeta)
  class Metric(base_layer.Layer):
    """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 = K.floatx() if dtype is None else tf.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 = tf.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 tf.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 tf.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 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_states(self):
      """Resets all of the metric state variables.
  
      This function is called between epochs/steps,
      when a metric is evaluated during training.
      """
      K.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=tf.compat.v1.VariableAggregation.SUM,
                   synchronization=tf.VariableSynchronization.ON_READ,
                   initializer=None,
                   dtype=None):
      """Adds state variable. Only for use by subclasses."""
      from keras.distribute import distributed_training_utils  # pylint:disable=g-import-not-at-top
  
      if tf.distribute.has_strategy():
        strategy = tf.distribute.get_strategy()
      else:
        strategy = None
  
      # TODO(b/120571621): Make `ON_READ` work with Keras metrics on TPU.
      if distributed_training_utils.is_tpu_strategy(strategy):
        synchronization = tf.VariableSynchronization.ON_WRITE
  
      with tf.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)
  
  
  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=tf.compat.v1.zeros_initializer)
      if reduction in [metrics_utils.Reduction.SUM_OVER_BATCH_SIZE,
                       metrics_utils.Reduction.WEIGHTED_MEAN]:
        self.count = self.add_weight(
            'count', initializer=tf.compat.v1.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)
      values = tf.cast(values, self._dtype)
      if sample_weight is not None:
        sample_weight = tf.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 = tf.__internal__.ops.broadcast_weights(
              sample_weight, values)
        except ValueError:
          # Reduce values to same ndim as weight array
          ndim = K.ndim(values)
          weight_ndim = K.ndim(sample_weight)
          if self.reduction == metrics_utils.Reduction.SUM:
            values = tf.reduce_sum(
                values, axis=list(range(weight_ndim, ndim)))
          else:
            values = tf.reduce_mean(
                values, axis=list(range(weight_ndim, ndim)))
        values = tf.multiply(values, sample_weight)
  
      value_sum = tf.reduce_sum(values)
      with tf.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 = tf.cast(tf.compat.v1.size(values), self._dtype)
      elif self.reduction == metrics_utils.Reduction.WEIGHTED_MEAN:
        if sample_weight is None:
          num_values = tf.cast(tf.compat.v1.size(values), self._dtype)
        else:
          num_values = tf.reduce_sum(sample_weight)
      else:
        raise NotImplementedError(
            'reduction [%s] not implemented' % self.reduction)
  
      with tf.control_dependencies([update_total_op]):
        return self.count.assign_add(num_values)
  
    def result(self):
      if self.reduction == metrics_utils.Reduction.SUM:
        return tf.identity(self.total)
      elif self.reduction in [
          metrics_utils.Reduction.WEIGHTED_MEAN,
          metrics_utils.Reduction.SUM_OVER_BATCH_SIZE
      ]:
        return tf.math.divide_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_states()
    >>> 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 = tf.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 = tf.cast(y_true, self._dtype)
      y_pred = tf.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 = tf.math.divide_no_nan(
          tf.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': K.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()))
  
  
  class MeanMetricWrapper(Mean):
    """Wraps a stateless metric function with the Mean 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: The keyword arguments that are passed 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 = tf.cast(y_true, self._dtype)
      y_pred = tf.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 six.iteritems(self._fn_kwargs):
        config[k] = K.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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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.accumulator = self.add_weight(
          'accumulator',
          shape=(len(self.thresholds),),
          initializer=tf.compat.v1.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,
          sample_weight=sample_weight)
  
    def result(self):
      if len(self.thresholds) == 1:
        result = self.accumulator[0]
      else:
        result = self.accumulator
      return tf.convert_to_tensor(result)
  
    def reset_states(self):
      num_thresholds = len(to_list(self.thresholds))
      K.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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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.true_positives = self.add_weight(
          'true_positives',
          shape=(len(self.thresholds),),
          initializer=tf.compat.v1.zeros_initializer)
      self.false_positives = self.add_weight(
          'false_positives',
          shape=(len(self.thresholds),),
          initializer=tf.compat.v1.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,
          top_k=self.top_k,
          class_id=self.class_id,
          sample_weight=sample_weight)
  
    def result(self):
      result = tf.math.divide_no_nan(self.true_positives,
                                   self.true_positives + self.false_positives)
      return result[0] if len(self.thresholds) == 1 else result
  
    def reset_states(self):
      num_thresholds = len(to_list(self.thresholds))
      K.batch_set_value(
          [(v, np.zeros((num_thresholds,))) for v in self.variables])
  
    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_states()
    >>> 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.true_positives = self.add_weight(
          'true_positives',
          shape=(len(self.thresholds),),
          initializer=tf.compat.v1.zeros_initializer)
      self.false_negatives = self.add_weight(
          'false_negatives',
          shape=(len(self.thresholds),),
          initializer=tf.compat.v1.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,
          top_k=self.top_k,
          class_id=self.class_id,
          sample_weight=sample_weight)
  
    def result(self):
      result = tf.math.divide_no_nan(self.true_positives,
                                   self.true_positives + self.false_negatives)
      return result[0] if len(self.thresholds) == 1 else result
  
    def reset_states(self):
      num_thresholds = len(to_list(self.thresholds))
      K.batch_set_value(
          [(v, np.zeros((num_thresholds,))) for v in self.variables])
  
    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()))
  
  
  @six.add_metaclass(abc.ABCMeta)
  class SensitivitySpecificityBase(Metric):
    """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, 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.true_positives = self.add_weight(
          'true_positives',
          shape=(num_thresholds,),
          initializer=tf.compat.v1.zeros_initializer)
      self.true_negatives = self.add_weight(
          'true_negatives',
          shape=(num_thresholds,),
          initializer=tf.compat.v1.zeros_initializer)
      self.false_positives = self.add_weight(
          'false_positives',
          shape=(num_thresholds,),
          initializer=tf.compat.v1.zeros_initializer)
      self.false_negatives = self.add_weight(
          'false_negatives',
          shape=(num_thresholds,),
          initializer=tf.compat.v1.zeros_initializer)
  
      # Compute `num_thresholds` thresholds in [0, 1]
      if num_thresholds == 1:
        self.thresholds = [0.5]
      else:
        thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
                      for i in range(num_thresholds - 2)]
        self.thresholds = [0.0] + thresholds + [1.0]
  
    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,
          sample_weight=sample_weight)
  
    def reset_states(self):
      num_thresholds = len(self.thresholds)
      K.batch_set_value(
          [(v, np.zeros((num_thresholds,))) for v in self.variables])
  
    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 = tf.compat.v1.where(predicate(constrained, self.value))
      feasible_exists = tf.greater(tf.compat.v1.size(feasible), 0)
  
      def get_max():
        return tf.reduce_max(tf.compat.v1.gather(dependent, feasible))
  
      return tf.compat.v1.cond(feasible_exists, get_max, lambda: 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.
  
    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.
      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_states()
    >>> 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, 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, name=name, dtype=dtype)
  
    def result(self):
      specificities = tf.math.divide_no_nan(
          self.true_negatives, self.true_negatives + self.false_positives)
      sensitivities = tf.math.divide_no_nan(
          self.true_positives, self.true_positives + self.false_negatives)
      return self._find_max_under_constraint(
          specificities, sensitivities, tf.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.
  
    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.
      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_states()
    >>> 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, 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, name=name, dtype=dtype)
  
    def result(self):
      sensitivities = tf.math.divide_no_nan(
          self.true_positives, self.true_positives + self.false_negatives)
      specificities = tf.math.divide_no_nan(
          self.true_negatives, self.true_negatives + self.false_positives)
      return self._find_max_under_constraint(
          sensitivities, specificities, tf.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.
  
    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.
      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_states()
    >>> 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, 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,
          name=name,
          dtype=dtype)
  
    def result(self):
      recalls = tf.math.divide_no_nan(
          self.true_positives, self.true_positives + self.false_negatives)
      precisions = tf.math.divide_no_nan(
          self.true_positives, self.true_positives + self.false_positives)
      return self._find_max_under_constraint(
          recalls, precisions, tf.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.
  
    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.
      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_states()
    >>> 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, 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,
          name=name,
          dtype=dtype)
  
    def result(self):
      precisions = tf.math.divide_no_nan(
          self.true_positives, self.true_positives + self.false_positives)
      recalls = tf.math.divide_no_nan(
          self.true_positives, self.true_positives + self.false_negatives)
      return self._find_max_under_constraint(
          precisions, recalls, tf.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 classes 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]
    >>> # 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_states()
    >>> 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)
      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)]
  
      # 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 - K.epsilon()] + thresholds +
                                  [1.0 + K.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 = tf.constant(label_weights, dtype=self.dtype)
        checks = [
            tf.compat.v1.assert_non_negative(
                label_weights,
                message='All values of `label_weights` must be non-negative.')
        ]
        self.label_weights = control_flow_ops.with_dependencies(
            checks, label_weights)
  
      else:
        self.label_weights = None
  
      self._from_logits = from_logits
  
      self._built = False
      if self.multi_label:
        if num_labels:
          shape = tf.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 = tf.TensorShape(
            [tf.compat.v1.Dimension(self.num_thresholds), self._num_labels])
  
      else:
        variable_shape = tf.TensorShape(
            [tf.compat.v1.Dimension(self.num_thresholds)])
      self._build_input_shape = shape
      # Create metric variables
      self.true_positives = self.add_weight(
          'true_positives',
          shape=variable_shape,
          initializer=tf.compat.v1.zeros_initializer)
      self.true_negatives = self.add_weight(
          'true_negatives',
          shape=variable_shape,
          initializer=tf.compat.v1.zeros_initializer)
      self.false_positives = self.add_weight(
          'false_positives',
          shape=variable_shape,
          initializer=tf.compat.v1.zeros_initializer)
      self.false_negatives = self.add_weight(
          'false_negatives',
          shape=variable_shape,
          initializer=tf.compat.v1.zeros_initializer)
  
      if self.multi_label:
        with tf.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 tf.executing_eagerly():
            K._initialize_variables(K._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(tf.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 = [
            tf.compat.v1.debugging.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 tf.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,
            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 = tf.math.divide_no_nan(
          dtp, tf.maximum(dp, 0), name='prec_slope')
      intercept = self.true_positives[1:] - tf.multiply(prec_slope, p[1:])
  
      safe_p_ratio = tf.compat.v1.where(
          tf.logical_and(p[:self.num_thresholds - 1] > 0, p[1:] > 0),
          tf.math.divide_no_nan(
              p[:self.num_thresholds - 1],
              tf.maximum(p[1:], 0),
              name='recall_relative_ratio'),
          tf.compat.v1.ones_like(p[1:]))
  
      pr_auc_increment = tf.math.divide_no_nan(
          prec_slope * (dtp + intercept * tf.math.log(safe_p_ratio)),
          tf.maximum(self.true_positives[1:] + self.false_negatives[1:], 0),
          name='pr_auc_increment')
  
      if self.multi_label:
        by_label_auc = tf.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 tf.reduce_mean(by_label_auc, name=self.name)
        else:
          # Weighted average of the label AUCs.
          return tf.math.divide_no_nan(
              tf.reduce_sum(
                  tf.multiply(by_label_auc, self.label_weights)),
              tf.reduce_sum(self.label_weights),
              name=self.name)
      else:
        return tf.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 = tf.math.divide_no_nan(self.true_positives,
                                   self.true_positives + self.false_negatives)
      if self.curve == metrics_utils.AUCCurve.ROC:
        fp_rate = tf.math.divide_no_nan(self.false_positives,
                                      self.false_positives + self.true_negatives)
        x = fp_rate
        y = recall
      else:  # curve == 'PR'.
        precision = tf.math.divide_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 = tf.minimum(y[:self.num_thresholds - 1], y[1:])
      else:  # self.summation_method = metrics_utils.AUCSummationMethod.MAJORING:
        heights = tf.maximum(y[:self.num_thresholds - 1], y[1:])
  
      # Sum up the areas of all the rectangles.
      if self.multi_label:
        riemann_terms = tf.multiply(x[:self.num_thresholds - 1] - x[1:],
                                          heights)
        by_label_auc = tf.reduce_sum(
            riemann_terms, name=self.name + '_by_label', axis=0)
  
        if self.label_weights is None:
          # Unweighted average of the label AUCs.
          return tf.reduce_mean(by_label_auc, name=self.name)
        else:
          # Weighted average of the label AUCs.
          return tf.math.divide_no_nan(
              tf.reduce_sum(
                  tf.multiply(by_label_auc, self.label_weights)),
              tf.reduce_sum(self.label_weights),
              name=self.name)
      else:
        return tf.reduce_sum(
            tf.multiply(x[:self.num_thresholds - 1] - x[1:], heights),
            name=self.name)
  
    def reset_states(self):
      if self.multi_label:
        K.batch_set_value([(v, np.zeros((self.num_thresholds, self._num_labels)))
                           for v in self.variables])
      else:
        K.batch_set_value([
            (v, np.zeros((self.num_thresholds,))) for v in self.variables
        ])
  
    def get_config(self):
      if is_tensor_or_variable(self.label_weights):
        label_weights = K.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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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 = tf.cast(y_true, self._dtype)
      y_pred = tf.cast(y_pred, self._dtype)
      y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
          y_pred, y_true)
      error_sq = tf.math.squared_difference(y_pred, y_true)
      return super(RootMeanSquaredError, self).update_state(
          error_sq, sample_weight=sample_weight)
  
    def result(self):
      return tf.sqrt(tf.math.divide_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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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=tf.compat.v1.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 = tf.cast(y_true, self._dtype)
      y_pred = tf.cast(y_pred, self._dtype)
  
      # Flatten the input if its rank > 1.
      if y_pred.shape.ndims > 1:
        y_pred = tf.reshape(y_pred, [-1])
  
      if y_true.shape.ndims > 1:
        y_true = tf.reshape(y_true, [-1])
  
      if sample_weight is not None:
        sample_weight = tf.cast(sample_weight, self._dtype)
        if sample_weight.shape.ndims > 1:
          sample_weight = tf.reshape(sample_weight, [-1])
  
      # Accumulate the prediction to current confusion matrix.
      current_cm = tf.math.confusion_matrix(
          y_true,
          y_pred,
          self.num_classes,
          weights=sample_weight,
          dtype=self._dtype)
      return self.total_cm.assign_add(current_cm)
  
    def result(self):
      """Compute the mean intersection-over-union via the confusion matrix."""
      sum_over_row = tf.cast(
          tf.reduce_sum(self.total_cm, axis=0), dtype=self._dtype)
      sum_over_col = tf.cast(
          tf.reduce_sum(self.total_cm, axis=1), dtype=self._dtype)
      true_positives = tf.cast(
          tf.linalg.tensor_diag_part(self.total_cm), dtype=self._dtype)
  
      # sum_over_row + sum_over_col =
      #     2 * true_positives + false_positives + false_negatives.
      denominator = sum_over_row + sum_over_col - true_positives
  
      # 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 = tf.reduce_sum(
          tf.cast(tf.not_equal(denominator, 0), dtype=self._dtype))
  
      iou = tf.math.divide_no_nan(true_positives, denominator)
  
      return tf.math.divide_no_nan(
          tf.reduce_sum(iou, name='mean_iou'), num_valid_entries)
  
    def reset_states(self):
      K.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 = tf.TensorShape(shape)
      self._build_input_shape = self._shape
      # Create new state variables
      self._total = self.add_weight(
          'total', shape=shape, initializer=tf.compat.v1.zeros_initializer)
      self._count = self.add_weight(
          'count', shape=shape, initializer=tf.compat.v1.zeros_initializer)
      with tf.init_scope():
        if not tf.executing_eagerly():
          K._initialize_variables(K._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 = tf.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 = tf.compat.v1.ones_like(values)
      if sample_weight is not None:
        sample_weight = tf.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 = tf.__internal__.ops.broadcast_weights(
              sample_weight, values)
        except ValueError:
          # Reduce values to same ndim as weight array
          ndim = K.ndim(values)
          weight_ndim = K.ndim(sample_weight)
          values = tf.reduce_mean(
              values, axis=list(range(weight_ndim, ndim)))
  
        num_values = tf.multiply(num_values, sample_weight)
        values = tf.multiply(values, sample_weight)
  
      update_total_op = self._total.assign_add(values)
      with tf.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 tf.math.divide_no_nan(self.total, self.count)
  
    def reset_states(self):
      if self._built:
        K.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_states()
    >>> 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_states()
    >>> 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_states()
    >>> 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 = tf.cast(y_true, self._dtype)
      y_pred = tf.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 six.iteritems(self._fn_kwargs):
        config[k] = K.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_pred.shape.assert_is_compatible_with(y_true.shape)
    if y_true.dtype != y_pred.dtype:
      y_pred = tf.cast(y_pred, y_true.dtype)
    return tf.cast(tf.equal(y_true, y_pred), K.floatx())
  
  
  @keras_export('keras.metrics.binary_accuracy')
  @tf.__internal__.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 = tf.convert_to_tensor(y_pred)
    threshold = tf.cast(threshold, y_pred.dtype)
    y_pred = tf.cast(y_pred > threshold, y_pred.dtype)
    return K.mean(tf.equal(y_true, y_pred), axis=-1)
  
  
  @keras_export('keras.metrics.categorical_accuracy')
  @tf.__internal__.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 tf.cast(
        tf.equal(
            tf.compat.v1.argmax(y_true, axis=-1), tf.compat.v1.argmax(y_pred, axis=-1)),
        K.floatx())
  
  
  @keras_export('keras.metrics.sparse_categorical_accuracy')
  @tf.__internal__.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 = tf.convert_to_tensor(y_pred)
    y_true = tf.convert_to_tensor(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(
        K.int_shape(y_true)) == len(K.int_shape(y_pred))):
      y_true = tf.compat.v1.squeeze(y_true, [-1])
    y_pred = tf.compat.v1.argmax(y_pred, axis=-1)
  
    # If the predicted output and actual output types don't match, force cast them
    # to match.
    if K.dtype(y_pred) != K.dtype(y_true):
      y_pred = tf.cast(y_pred, K.dtype(y_true))
  
    return tf.cast(tf.equal(y_true, y_pred), K.floatx())
  
  
  @keras_export('keras.metrics.top_k_categorical_accuracy')
  @tf.__internal__.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 tf.cast(
        tf.compat.v1.math.in_top_k(y_pred, tf.compat.v1.argmax(y_true, axis=-1), k), K.floatx())
  
  
  @keras_export('keras.metrics.sparse_top_k_categorical_accuracy')
  @tf.__internal__.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 = tf.convert_to_tensor(y_pred).shape.ndims
    y_true_rank = tf.convert_to_tensor(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 = tf.reshape(y_pred, [-1, y_pred.shape[-1]])
      if y_true_rank > 1:
        y_true = tf.reshape(y_true, [-1])
  
    return tf.cast(
        tf.compat.v1.math.in_top_k(y_pred, tf.cast(y_true, 'int32'), k), K.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 = tf.compat.v1.linalg.l2_normalize(y_true, axis=axis)
    y_pred = tf.compat.v1.linalg.l2_normalize(y_pred, axis=axis)
    return tf.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 tf.init_scope():
        return metric.__class__.from_config(metric.get_config())
    return metric
  
  
  def clone_metrics(metrics):
    """Clones the given metric list/dict."""
    return tf.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 '...tensorflow.python.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 '...tensorflow.python.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, six.string_types):
      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__
  