Module keras.api.keras

Public API for tf.keras namespace.

Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras namespace.
"""

from __future__ import print_function as _print_function

import sys as _sys

from keras import __version__
from keras.api.keras import __internal__
from keras.api.keras import activations
from keras.api.keras import applications
from keras.api.keras import backend
from keras.api.keras import callbacks
from keras.api.keras import constraints
from keras.api.keras import datasets
from keras.api.keras import estimator
from keras.api.keras import experimental
from keras.api.keras import initializers
from keras.api.keras import layers
from keras.api.keras import losses
from keras.api.keras import metrics
from keras.api.keras import mixed_precision
from keras.api.keras import models
from keras.api.keras import optimizers
from keras.api.keras import preprocessing
from keras.api.keras import regularizers
from keras.api.keras import utils
from keras.api.keras import wrappers
from keras.engine.input_layer import Input
from keras.engine.sequential import Sequential
from keras.engine.training import Model

del _print_function

from tensorflow.python.util import module_wrapper as _module_wrapper

if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
  _sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
      _sys.modules[__name__], "keras", public_apis=None, deprecation=True,
      has_lite=False)

Sub-modules

keras.api.keras.activations

Public API for tf.keras.activations namespace.

keras.api.keras.applications

Public API for tf.keras.applications namespace.

keras.api.keras.backend

Public API for tf.keras.backend namespace.

keras.api.keras.callbacks

Public API for tf.keras.callbacks namespace.

keras.api.keras.constraints

Public API for tf.keras.constraints namespace.

keras.api.keras.datasets

Public API for tf.keras.datasets namespace.

keras.api.keras.estimator

Public API for tf.keras.estimator namespace.

keras.api.keras.experimental

Public API for tf.keras.experimental namespace.

keras.api.keras.initializers

Public API for tf.keras.initializers namespace.

keras.api.keras.layers

Public API for tf.keras.layers namespace.

keras.api.keras.losses

Public API for tf.keras.losses namespace.

keras.api.keras.metrics

Public API for tf.keras.metrics namespace.

keras.api.keras.mixed_precision

Public API for tf.keras.mixed_precision namespace.

keras.api.keras.models

Public API for tf.keras.models namespace.

keras.api.keras.optimizers

Public API for tf.keras.optimizers namespace.

keras.api.keras.premade
keras.api.keras.preprocessing

Public API for tf.keras.preprocessing namespace.

keras.api.keras.regularizers

Public API for tf.keras.regularizers namespace.

keras.api.keras.utils

Public API for tf.keras.utils namespace.

keras.api.keras.wrappers

Public API for tf.keras.wrappers namespace.

Functions

def Input(shape=None, batch_size=None, name=None, dtype=None, sparse=None, tensor=None, ragged=None, type_spec=None, **kwargs)

Input() is used to instantiate a Keras tensor.

A Keras tensor is a symbolic tensor-like object, which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model.

For instance, if a, b and c are Keras tensors, it becomes possible to do: model = Model(input=[a, b], output=c)

Args

shape
A shape tuple (integers), not including the batch size. For instance, shape=(32,) indicates that the expected input will be batches of 32-dimensional vectors. Elements of this tuple can be None; 'None' elements represent dimensions where the shape is not known.
batch_size
optional static batch size (integer).
name
An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
dtype
The data type expected by the input, as a string (float32, float64, int32…)
sparse
A boolean specifying whether the placeholder to be created is sparse. Only one of 'ragged' and 'sparse' can be True. Note that, if sparse is False, sparse tensors can still be passed into the input - they will be densified with a default value of 0.
tensor
Optional existing tensor to wrap into the Input() layer. If set, the layer will use the tf.TypeSpec of this tensor rather than creating a new placeholder tensor.
ragged
A boolean specifying whether the placeholder to be created is ragged. Only one of 'ragged' and 'sparse' can be True. In this case, values of 'None' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see this guide.
type_spec
A tf.TypeSpec object to create the input placeholder from. When provided, all other args except name must be None.
**kwargs
deprecated arguments support. Supports batch_shape and batch_input_shape.

Returns

A tensor. Example:

# this is a logistic regression in Keras
x = Input(shape=(32,))
y = Dense(16, activation='softmax')(x)
model = Model(x, y)

Note that even if eager execution is enabled, Input() produces a symbolic tensor-like object (i.e. a placeholder). This symbolic tensor-like object can be used with lower-level TensorFlow ops that take tensors as inputs, as such:

x = Input(shape=(32,))
y = tf.square(x)  # This op will be treated like a layer
model = Model(x, y)

(This behavior does not work for higher-order TensorFlow APIs such as control flow and being directly watched by a tf.GradientTape).

However, the resulting model will not track any variables that were used as inputs to TensorFlow ops. All variable usages must happen within Keras layers to make sure they will be tracked by the model's weights.

The Keras Input can also create a placeholder from an arbitrary tf.TypeSpec, e.g:

x = Input(type_spec=tf.RaggedTensorSpec(shape=[None, None],
                                        dtype=tf.float32, ragged_rank=1))
y = x.values
model = Model(x, y)

When passing an arbitrary tf.TypeSpec, it must represent the signature of an entire batch instead of just one example.

Raises

ValueError
If both sparse and ragged are provided.
ValueError
If both shape and (batch_input_shape or batch_shape) are provided.
ValueError
If shape, tensor and type_spec are None.
ValueError
If arguments besides type_spec are non-None while type_spec is passed.
ValueError
if any unrecognized parameters are provided.
Expand source code
@keras_export('keras.Input', 'keras.layers.Input')
def Input(  # pylint: disable=invalid-name
    shape=None,
    batch_size=None,
    name=None,
    dtype=None,
    sparse=None,
    tensor=None,
    ragged=None,
    type_spec=None,
    **kwargs):
  """`Input()` is used to instantiate a Keras tensor.

  A Keras tensor is a symbolic tensor-like object,
  which we augment with certain attributes that allow us to build a Keras model
  just by knowing the inputs and outputs of the model.

  For instance, if `a`, `b` and `c` are Keras tensors,
  it becomes possible to do:
  `model = Model(input=[a, b], output=c)`

  Args:
      shape: A shape tuple (integers), not including the batch size.
          For instance, `shape=(32,)` indicates that the expected input
          will be batches of 32-dimensional vectors. Elements of this tuple
          can be None; 'None' elements represent dimensions where the shape is
          not known.
      batch_size: optional static batch size (integer).
      name: An optional name string for the layer.
          Should be unique in a model (do not reuse the same name twice).
          It will be autogenerated if it isn't provided.
      dtype: The data type expected by the input, as a string
          (`float32`, `float64`, `int32`...)
      sparse: A boolean specifying whether the placeholder to be created is
          sparse. Only one of 'ragged' and 'sparse' can be True. Note that,
          if `sparse` is False, sparse tensors can still be passed into the
          input - they will be densified with a default value of 0.
      tensor: Optional existing tensor to wrap into the `Input` layer.
          If set, the layer will use the `tf.TypeSpec` of this tensor rather
          than creating a new placeholder tensor.
      ragged: A boolean specifying whether the placeholder to be created is
          ragged. Only one of 'ragged' and 'sparse' can be True. In this case,
          values of 'None' in the 'shape' argument represent ragged dimensions.
          For more information about RaggedTensors, see
          [this guide](https://www.tensorflow.org/guide/ragged_tensors).
      type_spec: A `tf.TypeSpec` object to create the input placeholder from.
          When provided, all other args except name must be None.
      **kwargs: deprecated arguments support. Supports `batch_shape` and
          `batch_input_shape`.

  Returns:
    A `tensor`.

  Example:

  ```python
  # this is a logistic regression in Keras
  x = Input(shape=(32,))
  y = Dense(16, activation='softmax')(x)
  model = Model(x, y)
  ```

  Note that even if eager execution is enabled,
  `Input` produces a symbolic tensor-like object (i.e. a placeholder).
  This symbolic tensor-like object can be used with lower-level
  TensorFlow ops that take tensors as inputs, as such:

  ```python
  x = Input(shape=(32,))
  y = tf.square(x)  # This op will be treated like a layer
  model = Model(x, y)
  ```

  (This behavior does not work for higher-order TensorFlow APIs such as
  control flow and being directly watched by a `tf.GradientTape`).

  However, the resulting model will not track any variables that were
  used as inputs to TensorFlow ops. All variable usages must happen within
  Keras layers to make sure they will be tracked by the model's weights.

  The Keras Input can also create a placeholder from an arbitrary `tf.TypeSpec`,
  e.g:

  ```python
  x = Input(type_spec=tf.RaggedTensorSpec(shape=[None, None],
                                          dtype=tf.float32, ragged_rank=1))
  y = x.values
  model = Model(x, y)
  ```
  When passing an arbitrary `tf.TypeSpec`, it must represent the signature of an
  entire batch instead of just one example.

  Raises:
    ValueError: If both `sparse` and `ragged` are provided.
    ValueError: If both `shape` and (`batch_input_shape` or `batch_shape`) are
      provided.
    ValueError: If `shape`, `tensor` and `type_spec` are None.
    ValueError: If arguments besides `type_spec` are non-None while `type_spec`
                is passed.
    ValueError: if any unrecognized parameters are provided.
  """
  if sparse and ragged:
    raise ValueError(
        'Cannot set both sparse and ragged to True in a Keras input.')

  input_layer_config = {'name': name, 'dtype': dtype, 'sparse': sparse,
                        'ragged': ragged, 'input_tensor': tensor,
                        'type_spec': type_spec}

  batch_input_shape = kwargs.pop('batch_input_shape',
                                 kwargs.pop('batch_shape', None))
  if shape is not None and batch_input_shape is not None:
    raise ValueError('Only provide the `shape` OR `batch_input_shape` argument '
                     'to Input, not both at the same time.')
  if (batch_input_shape is None and shape is None and tensor is None
      and type_spec is None):
    raise ValueError('Please provide to Input a `shape`'
                     ' or a `tensor` or a `type_spec` argument. Note that '
                     '`shape` does not include the batch '
                     'dimension.')
  if kwargs:
    raise ValueError('Unrecognized keyword arguments:', kwargs.keys())

  if batch_input_shape:
    shape = batch_input_shape[1:]
    input_layer_config.update({'batch_input_shape': batch_input_shape})
  else:
    input_layer_config.update(
        {'batch_size': batch_size, 'input_shape': shape})
  input_layer = InputLayer(**input_layer_config)

  # Return tensor including `_keras_history`.
  # Note that in this case train_output and test_output are the same pointer.
  outputs = input_layer._inbound_nodes[0].outputs
  if isinstance(outputs, list) and len(outputs) == 1:
    return outputs[0]
  else:
    return outputs

Classes

class Model (*args, **kwargs)

Model groups layers into an object with training and inference features.

Args

inputs
The input(s) of the model: a keras.Input object or list of keras.Input objects.
outputs
The output(s) of the model. See Functional API example below.
name
String, the name of the model.

There are two ways to instantiate a Model:

1 - With the "Functional API", where you start from Input(), you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs:

import tensorflow as tf

inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)

Note: Only dicts, lists, and tuples of input tensors are supported. Nested inputs are not supported (e.g. lists of list or dicts of dict).

2 - By subclassing the Model class: in that case, you should define your layers in __init__ and you should implement the model's forward pass in call.

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super(MyModel, self).__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)

  def call(self, inputs):
    x = self.dense1(inputs)
    return self.dense2(x)

model = MyModel()

If you subclass Model, you can optionally have a training argument (boolean) in call, which you can use to specify a different behavior in training and inference:

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super(MyModel, self).__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
    self.dropout = tf.keras.layers.Dropout(0.5)

  def call(self, inputs, training=False):
    x = self.dense1(inputs)
    if training:
      x = self.dropout(x, training=training)
    return self.dense2(x)

model = MyModel()

Once the model is created, you can config the model with losses and metrics with model.compile(), train the model with model.fit(), or use the model to do prediction with model.predict().

Expand source code
class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  """`Model` groups layers into an object with training and inference features.

  Args:
      inputs: The input(s) of the model: a `keras.Input` object or list of
          `keras.Input` objects.
      outputs: The output(s) of the model. See Functional API example below.
      name: String, the name of the model.

  There are two ways to instantiate a `Model`:

  1 - With the "Functional API", where you start from `Input`,
  you chain layer calls to specify the model's forward pass,
  and finally you create your model from inputs and outputs:

  ```python
  import tensorflow as tf

  inputs = tf.keras.Input(shape=(3,))
  x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
  outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
  model = tf.keras.Model(inputs=inputs, outputs=outputs)
  ```

  Note: Only dicts, lists, and tuples of input tensors are supported. Nested
  inputs are not supported (e.g. lists of list or dicts of dict).

  2 - By subclassing the `Model` class: in that case, you should define your
  layers in `__init__` and you should implement the model's forward pass
  in `call`.

  ```python
  import tensorflow as tf

  class MyModel(tf.keras.Model):

    def __init__(self):
      super(MyModel, self).__init__()
      self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
      self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)

    def call(self, inputs):
      x = self.dense1(inputs)
      return self.dense2(x)

  model = MyModel()
  ```

  If you subclass `Model`, you can optionally have
  a `training` argument (boolean) in `call`, which you can use to specify
  a different behavior in training and inference:

  ```python
  import tensorflow as tf

  class MyModel(tf.keras.Model):

    def __init__(self):
      super(MyModel, self).__init__()
      self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
      self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
      self.dropout = tf.keras.layers.Dropout(0.5)

    def call(self, inputs, training=False):
      x = self.dense1(inputs)
      if training:
        x = self.dropout(x, training=training)
      return self.dense2(x)

  model = MyModel()
  ```

  Once the model is created, you can config the model with losses and metrics
  with `model.compile()`, train the model with `model.fit()`, or use the model
  to do prediction with `model.predict()`.
  """
  _TF_MODULE_IGNORED_PROPERTIES = frozenset(
      itertools.chain(('_train_counter', '_test_counter', '_predict_counter',
                       '_steps_per_execution'),
                      base_layer.Layer._TF_MODULE_IGNORED_PROPERTIES))  # pylint: disable=protected-access

  def __new__(cls, *args, **kwargs):
    # Signature detection
    if is_functional_model_init_params(args, kwargs) and cls == Model:
      # Functional model
      from keras.engine import functional  # pylint: disable=g-import-not-at-top
      return functional.Functional(skip_init=True, *args, **kwargs)
    else:
      return super(Model, cls).__new__(cls, *args, **kwargs)

  @tf.__internal__.tracking.no_automatic_dependency_tracking
  def __init__(self, *args, **kwargs):
    self._is_model_for_instrumentation = True
    base_layer.keras_api_gauge.get_cell('model').set(True)

    # Special case for Subclassed Functional Model, which we couldn't detect
    # when __new__ is called. We only realize it is a functional model when it
    # calls super.__init__ with input and output tensor.
    from keras.engine import functional  # pylint: disable=g-import-not-at-top
    if (is_functional_model_init_params(args, kwargs) and
        not isinstance(self, functional.Functional)):
      # Filter the kwargs for multiple inheritance.
      supported_kwargs = ['inputs', 'outputs', 'name', 'trainable', 'skip_init']
      model_kwargs = {k: kwargs[k] for k in kwargs if k in supported_kwargs}
      other_kwargs = {k: kwargs[k] for k in kwargs if k not in supported_kwargs}
      inject_functional_model_class(self.__class__)
      functional.Functional.__init__(self, *args, **model_kwargs)

      # In case there is any multiple inheritance here, we need to call the
      # __init__ for any class that appears after the Functional class.
      clz_to_init = []
      found_functional_class = False
      for clz in self.__class__.__bases__:
        if issubclass(clz, functional.Functional):
          found_functional_class = True
          continue
        if found_functional_class:
          clz_to_init.append(clz)

      if clz_to_init:
        for clz in clz_to_init:
          clz.__init__(self, *args, **other_kwargs)
      elif other_kwargs:
        # In case there are unused kwargs, we should raise an error to user, in
        # case they have a typo in the param name.
        raise TypeError(
            'The following keyword arguments aren\'t supported: {}'.format(
                other_kwargs))
      return

    base_layer.keras_api_gauge.get_cell('Model subclass').set(True)
    # The following are implemented as property functions:
    # self.trainable_weights
    # self.non_trainable_weights
    # `inputs` / `outputs` will only appear in kwargs if either are misspelled.
    generic_utils.validate_kwargs(kwargs, {
        'trainable', 'dtype', 'dynamic', 'name', 'autocast', 'inputs', 'outputs'
    })
    super(Model, self).__init__(**kwargs)
    # By default, Model is a subclass model, which is not in graph network.
    self._is_graph_network = False

    self.inputs = None
    self.outputs = None
    self.input_names = None
    self.output_names = None
    # stop_training is used by callback to stop training when error happens
    self.stop_training = False
    self.history = None
    # These objects are used in the default `Model.compile`. They are not
    # guaranteed to be set after `Model.compile` is called, as users can
    # override compile with custom logic.
    self.compiled_loss = None
    self.compiled_metrics = None

    # This is True for Sequential networks and Functional networks.
    self._compute_output_and_mask_jointly = False

    # Don't reset compilation if already done. This may occur if calling
    # `__init__` (or `_init_graph_network`) on an already-compiled model
    # such as a Sequential model. Sequential models may need to rebuild
    # themselves after compilation.
    self._maybe_create_attribute('_is_compiled', False)
    self._maybe_create_attribute('optimizer', None)

    # Model must be created under scope of DistStrat it will be trained with.
    if tf.distribute.has_strategy():
      self._distribution_strategy = tf.distribute.get_strategy()
    else:
      self._distribution_strategy = None

    self._cluster_coordinator = None

    # Defaults to value of `tf.config.experimental_functions_run_eagerly`.
    self._run_eagerly = None
    # Initialize cache attrs.
    self._reset_compile_cache()

    # Fault-tolerance handler. Set in `ModelCheckpoint`.
    self._training_state = None
    self._saved_model_inputs_spec = None
    self._saved_model_arg_spec = None
    self._trackable_saver = saver_with_op_caching(self)

    self._steps_per_execution = None

    self._init_batch_counters()
    self._base_model_initialized = True

  @tf.__internal__.tracking.no_automatic_dependency_tracking
  def _init_batch_counters(self):
    # Untracked Variables, used to keep track of mini-batches seen in `fit`,
    # `evaluate`, and `predict`.
    agg = tf.VariableAggregation.ONLY_FIRST_REPLICA
    self._train_counter = tf.Variable(0, dtype='int64', aggregation=agg)
    self._test_counter = tf.Variable(0, dtype='int64', aggregation=agg)
    self._predict_counter = tf.Variable(
        0, dtype='int64', aggregation=agg)

  def __setattr__(self, name, value):
    if not getattr(self, '_self_setattr_tracking', True):
      super(Model, self).__setattr__(name, value)
      return

    if all(
        isinstance(v, (base_layer.Layer, tf.Variable)) or
        base_layer_utils.has_weights(v) for v in tf.nest.flatten(value)):
      try:
        self._base_model_initialized
      except AttributeError:
        raise RuntimeError(
            'It looks like you are subclassing `Model` and you '
            'forgot to call `super().__init__()`.'
            ' Always start with this line.')

    super(Model, self).__setattr__(name, value)

  @generic_utils.default
  def build(self, input_shape):
    """Builds the model based on input shapes received.

    This is to be used for subclassed models, which do not know at instantiation
    time what their inputs look like.

    This method only exists for users who want to call `model.build()` in a
    standalone way (as a substitute for calling the model on real data to
    build it). It will never be called by the framework (and thus it will
    never throw unexpected errors in an unrelated workflow).

    Args:
     input_shape: Single tuple, TensorShape, or list/dict of shapes, where
         shapes are tuples, integers, or TensorShapes.

    Raises:
      ValueError:
        1. In case of invalid user-provided data (not of type tuple,
           list, TensorShape, or dict).
        2. If the model requires call arguments that are agnostic
           to the input shapes (positional or kwarg in call signature).
        3. If not all layers were properly built.
        4. If float type inputs are not supported within the layers.

      In each of these cases, the user should build their model by calling it
      on real tensor data.
    """
    if self._is_graph_network:
      super(Model, self).build(input_shape)
      return

    if input_shape is None:
      raise ValueError('Input shape must be defined when calling build on a '
                       'model subclass network.')
    valid_types = (tuple, list, tf.TensorShape, dict)
    if not isinstance(input_shape, valid_types):
      raise ValueError('Specified input shape is not one of the valid types. '
                       'Please specify a batch input shape of type tuple or '
                       'list of input shapes. User provided '
                       'input type: {}'.format(type(input_shape)))

    if input_shape and not self.inputs:
      # We create placeholders for the `None`s in the shape and build the model
      # in a Graph. Since tf.Variable is compatible with both eager execution
      # and graph building, the variables created after building the model in
      # a Graph are still valid when executing eagerly.
      if tf.executing_eagerly():
        graph = tf.__internal__.FuncGraph('build_graph')
      else:
        graph = backend.get_graph()
      with graph.as_default():
        if (isinstance(input_shape, list) and
            all(d is None or isinstance(d, int) for d in input_shape)):
          input_shape = tuple(input_shape)
        if isinstance(input_shape, list):
          x = [base_layer_utils.generate_placeholders_from_shape(shape)
               for shape in input_shape]
        elif isinstance(input_shape, dict):
          x = {
              k: base_layer_utils.generate_placeholders_from_shape(shape)
              for k, shape in input_shape.items()
          }
        else:
          x = base_layer_utils.generate_placeholders_from_shape(input_shape)

        kwargs = {}
        call_signature = self._call_full_argspec
        call_args = call_signature.args
        # Exclude `self`, `inputs`, and any argument with a default value.
        if len(call_args) > 2:
          if call_signature.defaults:
            call_args = call_args[2:-len(call_signature.defaults)]
          else:
            call_args = call_args[2:]
          for arg in call_args:
            if arg == 'training':
              # Case where `training` is a positional arg with no default.
              kwargs['training'] = False
            else:
              # Has invalid call signature with unknown positional arguments.
              raise ValueError(
                  'Currently, you cannot build your model if it has '
                  'positional or keyword arguments that are not '
                  'inputs to the model, but are required for its '
                  '`call` method. Instead, in order to instantiate '
                  'and build your model, `call` your model on real '
                  'tensor data with all expected call arguments.')
        elif len(call_args) < 2:
          # Signature without `inputs`.
          raise ValueError('You can only call `build` on a model if its `call` '
                           'method accepts an `inputs` argument.')
        try:
          self.call(x, **kwargs)
        except (tf.errors.InvalidArgumentError, TypeError):
          raise ValueError('You cannot build your model by calling `build` '
                           'if your layers do not support float type inputs. '
                           'Instead, in order to instantiate and build your '
                           'model, `call` your model on real tensor data (of '
                           'the correct dtype).')
    super(Model, self).build(input_shape)

  @doc_controls.doc_in_current_and_subclasses
  def call(self, inputs, training=None, mask=None):
    """Calls the model on new inputs.

    In this case `call` just reapplies
    all ops in the graph to the new inputs
    (e.g. build a new computational graph from the provided inputs).

    Note: This method should not be called directly. It is only meant to be
    overridden when subclassing `tf.keras.Model`.
    To call a model on an input, always use the `__call__` method,
    i.e. `model(inputs)`, which relies on the underlying `call` method.

    Args:
        inputs: Input tensor, or dict/list/tuple of input tensors.
        training: Boolean or boolean scalar tensor, indicating whether to run
          the `Network` in training mode or inference mode.
        mask: A mask or list of masks. A mask can be
            either a tensor or None (no mask).

    Returns:
        A tensor if there is a single output, or
        a list of tensors if there are more than one outputs.
    """
    raise NotImplementedError('When subclassing the `Model` class, you should '
                              'implement a `call` method.')

  def compile(self,
              optimizer='rmsprop',
              loss=None,
              metrics=None,
              loss_weights=None,
              weighted_metrics=None,
              run_eagerly=None,
              steps_per_execution=None,
              **kwargs):
    """Configures the model for training.

    Example:

    ```python
    model.compile(optimizer=tf.keras.optimizer.Adam(learning_rate=1e-3),
                  loss=tf.keras.losses.BinaryCrossentropy(),
                  metrics=[tf.keras.metrics.BinaryAccuracy(),
                           tf.keras.metrics.FalseNegatives()])
    ```

    Args:
        optimizer: String (name of optimizer) or optimizer instance. See
          `tf.keras.optimizers`.
        loss: Loss function. Maybe be a string (name of loss function), or
          a `tf.keras.losses.Loss` instance. See `tf.keras.losses`. A loss
          function is any callable with the signature `loss = fn(y_true,
          y_pred)`, where `y_true` are the ground truth values, and
          `y_pred` are the model's predictions.
          `y_true` should have shape
          `(batch_size, d0, .. dN)` (except in the case of
          sparse loss functions such as
          sparse categorical crossentropy which expects integer arrays of shape
          `(batch_size, d0, .. dN-1)`).
          `y_pred` should have shape `(batch_size, d0, .. dN)`.
          The loss function should return a float tensor.
          If a custom `Loss` instance is
          used and reduction is set to `None`, return value has shape
          `(batch_size, d0, .. dN-1)` i.e. per-sample or per-timestep loss
          values; otherwise, it is a scalar. If the model has multiple outputs,
          you can use a different loss on each output by passing a dictionary
          or a list of losses. The loss value that will be minimized by the
          model will then be the sum of all individual losses, unless
          `loss_weights` is specified.
        metrics: List of metrics to be evaluated by the model during training
          and testing. Each of this can be a string (name of a built-in
          function), function or a `tf.keras.metrics.Metric` instance. See
          `tf.keras.metrics`. Typically you will use `metrics=['accuracy']`. A
          function is any callable with the signature `result = fn(y_true,
          y_pred)`. To specify different metrics for different outputs of a
          multi-output model, you could also pass a dictionary, such as
          `metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}`.
          You can also pass a list to specify a metric or a list of metrics
          for each output, such as `metrics=[['accuracy'], ['accuracy', 'mse']]`
          or `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the
          strings 'accuracy' or 'acc', we convert this to one of
          `tf.keras.metrics.BinaryAccuracy`,
          `tf.keras.metrics.CategoricalAccuracy`,
          `tf.keras.metrics.SparseCategoricalAccuracy` based on the loss
          function used and the model output shape. We do a similar
          conversion for the strings 'crossentropy' and 'ce' as well.
        loss_weights: Optional list or dictionary specifying scalar coefficients
          (Python floats) to weight the loss contributions of different model
          outputs. The loss value that will be minimized by the model will then
          be the *weighted sum* of all individual losses, weighted by the
          `loss_weights` coefficients.
            If a list, it is expected to have a 1:1 mapping to the model's
              outputs. If a dict, it is expected to map output names (strings)
              to scalar coefficients.
        weighted_metrics: List of metrics to be evaluated and weighted by
          `sample_weight` or `class_weight` during training and testing.
        run_eagerly: Bool. Defaults to `False`. If `True`, this `Model`'s
          logic will not be wrapped in a `tf.function`. Recommended to leave
          this as `None` unless your `Model` cannot be run inside a
          `tf.function`. `run_eagerly=True` is not supported when using
          `tf.distribute.experimental.ParameterServerStrategy`.
        steps_per_execution: Int. Defaults to 1. The number of batches to
          run during each `tf.function` call. Running multiple batches
          inside a single `tf.function` call can greatly improve performance
          on TPUs or small models with a large Python overhead.
          At most, one full epoch will be run each
          execution. If a number larger than the size of the epoch is passed,
          the execution will be truncated to the size of the epoch.
          Note that if `steps_per_execution` is set to `N`,
          `Callback.on_batch_begin` and `Callback.on_batch_end` methods
          will only be called every `N` batches
          (i.e. before/after each `tf.function` execution).
        **kwargs: Arguments supported for backwards compatibility only.

    Raises:
        ValueError: In case of invalid arguments for
            `optimizer`, `loss` or `metrics`.
    """
    base_layer.keras_api_gauge.get_cell('compile').set(True)
    with self.distribute_strategy.scope():
      if 'experimental_steps_per_execution' in kwargs:
        logging.warning('The argument `steps_per_execution` is no longer '
                        'experimental. Pass `steps_per_execution` instead of '
                        '`experimental_steps_per_execution`.')
        if not steps_per_execution:
          steps_per_execution = kwargs.pop('experimental_steps_per_execution')

      # When compiling from an already-serialized model, we do not want to
      # reapply some processing steps (e.g. metric renaming for multi-output
      # models, which have prefixes added for each corresponding output name).
      from_serialized = kwargs.pop('from_serialized', False)

      self._validate_compile(optimizer, metrics, **kwargs)
      self._run_eagerly = run_eagerly

      self.optimizer = self._get_optimizer(optimizer)
      self.compiled_loss = compile_utils.LossesContainer(
          loss, loss_weights, output_names=self.output_names)
      self.compiled_metrics = compile_utils.MetricsContainer(
          metrics, weighted_metrics, output_names=self.output_names,
          from_serialized=from_serialized)

      self._configure_steps_per_execution(steps_per_execution or 1)

      # Initializes attrs that are reset each time `compile` is called.
      self._reset_compile_cache()
      self._is_compiled = True

      self.loss = loss or {}  # Backwards compat.

  def _get_optimizer(self, optimizer):
    """Wraps `optimizer` in `LossScaleOptimizer` if necessary."""
    # The deprecated PolicyV1 has a loss_scale, which we use for backwards
    # compatibility to match TF 2.3 behavior. The new Policy does not have a
    # loss_scale, so we use dynamic loss scaling if the mixed_float16 policy is
    # used.
    if isinstance(self._dtype_policy, policy.PolicyV1):
      loss_scale = self._dtype_policy.loss_scale
    elif self._dtype_policy.name == 'mixed_float16':
      loss_scale = 'dynamic'
    else:
      loss_scale = None

    def _get_single_optimizer(opt):
      opt = optimizers.get(opt)
      if (loss_scale is not None and
          not isinstance(opt, lso.LossScaleOptimizer)):
        if loss_scale == 'dynamic':
          opt = lso.LossScaleOptimizer(opt)
        else:
          opt = lso.LossScaleOptimizerV1(opt, loss_scale)
      return opt

    return tf.nest.map_structure(_get_single_optimizer, optimizer)

  @tf.__internal__.tracking.no_automatic_dependency_tracking
  def _reset_compile_cache(self):
    self.train_function = None
    self.test_function = None
    self.predict_function = None
    # Used to cache the `tf.function`'ed `train_function` to be logged in
    # TensorBoard, since the original `train_function` is not necessarily
    # a `tf.function` (e.g., with ParameterServerStrategy, the `train_function`
    # is a scheduling of the actual training function to a remote worker).
    self.train_tf_function = None

    # Used to cache `trainable` attr of `Layer`s for `fit`.
    self._compiled_trainable_state = self._get_trainable_state()

  @tf.__internal__.tracking.no_automatic_dependency_tracking
  def _configure_steps_per_execution(self, steps_per_execution):
    self._steps_per_execution = tf.Variable(
        steps_per_execution,
        dtype='int64',
        aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)

  @property
  def _should_compute_mask(self):
    return False

  @property
  def metrics(self):
    """Returns the model's metrics added using `compile`, `add_metric` APIs.

    Note: Metrics passed to `compile()` are available only after a `keras.Model`
    has been trained/evaluated on actual data.

    Examples:

    >>> inputs = tf.keras.layers.Input(shape=(3,))
    >>> outputs = tf.keras.layers.Dense(2)(inputs)
    >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
    >>> [m.name for m in model.metrics]
    []

    >>> x = np.random.random((2, 3))
    >>> y = np.random.randint(0, 2, (2, 2))
    >>> model.fit(x, y)
    >>> [m.name for m in model.metrics]
    ['loss', 'mae']

    >>> inputs = tf.keras.layers.Input(shape=(3,))
    >>> d = tf.keras.layers.Dense(2, name='out')
    >>> output_1 = d(inputs)
    >>> output_2 = d(inputs)
    >>> model = tf.keras.models.Model(
    ...    inputs=inputs, outputs=[output_1, output_2])
    >>> model.add_metric(
    ...    tf.reduce_sum(output_2), name='mean', aggregation='mean')
    >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
    >>> model.fit(x, (y, y))
    >>> [m.name for m in model.metrics]
    ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
    'out_1_acc', 'mean']

    """
    metrics = []
    if self._is_compiled:
      # TODO(omalleyt): Track `LossesContainer` and `MetricsContainer` objects
      # so that attr names are not load-bearing.
      if self.compiled_loss is not None:
        metrics += self.compiled_loss.metrics
      if self.compiled_metrics is not None:
        metrics += self.compiled_metrics.metrics

    for l in self._flatten_layers():
      metrics.extend(l._metrics)  # pylint: disable=protected-access
    return metrics

  @property
  def metrics_names(self):
    """Returns the model's display labels for all outputs.

    Note: `metrics_names` are available only after a `keras.Model` has been
    trained/evaluated on actual data.

    Examples:

    >>> inputs = tf.keras.layers.Input(shape=(3,))
    >>> outputs = tf.keras.layers.Dense(2)(inputs)
    >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
    >>> model.metrics_names
    []

    >>> x = np.random.random((2, 3))
    >>> y = np.random.randint(0, 2, (2, 2))
    >>> model.fit(x, y)
    >>> model.metrics_names
    ['loss', 'mae']

    >>> inputs = tf.keras.layers.Input(shape=(3,))
    >>> d = tf.keras.layers.Dense(2, name='out')
    >>> output_1 = d(inputs)
    >>> output_2 = d(inputs)
    >>> model = tf.keras.models.Model(
    ...    inputs=inputs, outputs=[output_1, output_2])
    >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
    >>> model.fit(x, (y, y))
    >>> model.metrics_names
    ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
    'out_1_acc']

    """

    # This property includes all output names including `loss` and per-output
    # losses for backward compatibility.
    return [m.name for m in self.metrics]

  @property
  def distribute_strategy(self):
    """The `tf.distribute.Strategy` this model was created under."""
    return self._distribution_strategy or tf.distribute.get_strategy()

  @property
  def run_eagerly(self):
    """Settable attribute indicating whether the model should run eagerly.

    Running eagerly means that your model will be run step by step,
    like Python code. Your model might run slower, but it should become easier
    for you to debug it by stepping into individual layer calls.

    By default, we will attempt to compile your model to a static graph to
    deliver the best execution performance.

    Returns:
      Boolean, whether the model should run eagerly.
    """
    if self.dynamic and self._run_eagerly is False:  # pylint:disable=g-bool-id-comparison
      # TODO(fchollet): consider using py_func to enable this.
      raise ValueError('Your model contains layers that can only be '
                       'successfully run in eager execution (layers '
                       'constructed with `dynamic=True`). '
                       'You cannot set `run_eagerly=False`.')

    if self._cluster_coordinator and self._run_eagerly:
      raise ValueError('When using `Model` with `ParameterServerStrategy`, '
                       '`run_eagerly` is not supported.')

    # Run eagerly logic, by priority:
    # (1) Dynamic models must be run eagerly.
    # (2) Explicitly setting run_eagerly causes a Model to be run eagerly.
    # (3) Not explicitly setting run_eagerly defaults to TF's global setting.
    return (self.dynamic or self._run_eagerly or
            (tf.config.functions_run_eagerly() and
             self._run_eagerly is None))

  @run_eagerly.setter
  def run_eagerly(self, value):
    self._run_eagerly = value

  def train_step(self, data):
    """The logic for one training step.

    This method can be overridden to support custom training logic.
    For concrete examples of how to override this method see
    [Customizing what happends in fit](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit).
    This method is called by `Model.make_train_function`.

    This method should contain the mathematical logic for one step of training.
    This typically includes the forward pass, loss calculation, backpropagation,
    and metric updates.

    Configuration details for *how* this logic is run (e.g. `tf.function` and
    `tf.distribute.Strategy` settings), should be left to
    `Model.make_train_function`, which can also be overridden.

    Args:
      data: A nested structure of `Tensor`s.

    Returns:
      A `dict` containing values that will be passed to
      `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
      values of the `Model`'s metrics are returned. Example:
      `{'loss': 0.2, 'accuracy': 0.7}`.

    """
    # These are the only transformations `Model.fit` applies to user-input
    # data when a `tf.data.Dataset` is provided.
    data = data_adapter.expand_1d(data)
    x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)
    # Run forward pass.
    with tf.GradientTape() as tape:
      y_pred = self(x, training=True)
      loss = self.compiled_loss(
          y, y_pred, sample_weight, regularization_losses=self.losses)
    # Run backwards pass.
    self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
    self.compiled_metrics.update_state(y, y_pred, sample_weight)
    # Collect metrics to return
    return_metrics = {}
    for metric in self.metrics:
      result = metric.result()
      if isinstance(result, dict):
        return_metrics.update(result)
      else:
        return_metrics[metric.name] = result
    return return_metrics

  def make_train_function(self, force=False):
    """Creates a function that executes one step of training.

    This method can be overridden to support custom training logic.
    This method is called by `Model.fit` and `Model.train_on_batch`.

    Typically, this method directly controls `tf.function` and
    `tf.distribute.Strategy` settings, and delegates the actual training
    logic to `Model.train_step`.

    This function is cached the first time `Model.fit` or
    `Model.train_on_batch` is called. The cache is cleared whenever
    `Model.compile` is called. You can skip the cache and generate again the
    function with `force=True`.

    Args:
      force: Whether to regenerate the train function and skip the cached
        function if available.

    Returns:
      Function. The function created by this method should accept a
      `tf.data.Iterator`, and return a `dict` containing values that will
      be passed to `tf.keras.Callbacks.on_train_batch_end`, such as
      `{'loss': 0.2, 'accuracy': 0.7}`.
    """
    if self.train_function is not None and not force:
      return self.train_function

    def step_function(model, iterator):
      """Runs a single training step."""

      def run_step(data):
        outputs = model.train_step(data)
        # Ensure counter is updated only if `train_step` succeeds.
        with tf.control_dependencies(_minimum_control_deps(outputs)):
          model._train_counter.assign_add(1)  # pylint: disable=protected-access
        return outputs

      data = next(iterator)
      outputs = model.distribute_strategy.run(run_step, args=(data,))
      outputs = reduce_per_replica(
          outputs, self.distribute_strategy, reduction='first')
      write_scalar_summaries(outputs, step=model._train_counter)  # pylint: disable=protected-access
      return outputs

    if (self._steps_per_execution is None or
        self._steps_per_execution.numpy().item() == 1):

      def train_function(iterator):
        """Runs a training execution with one step."""
        return step_function(self, iterator)

    else:

      def train_function(iterator):
        """Runs a training execution with multiple steps."""
        for _ in tf.range(self._steps_per_execution):
          outputs = step_function(self, iterator)
        return outputs

    if not self.run_eagerly:
      train_function = tf.function(
          train_function, experimental_relax_shapes=True)
      self.train_tf_function = train_function

    self.train_function = train_function

    if self._cluster_coordinator:
      self.train_function = lambda iterator: self._cluster_coordinator.schedule(  # pylint: disable=g-long-lambda
          train_function, args=(iterator,))

    return self.train_function

  def fit(self,
          x=None,
          y=None,
          batch_size=None,
          epochs=1,
          verbose='auto',
          callbacks=None,
          validation_split=0.,
          validation_data=None,
          shuffle=True,
          class_weight=None,
          sample_weight=None,
          initial_epoch=0,
          steps_per_epoch=None,
          validation_steps=None,
          validation_batch_size=None,
          validation_freq=1,
          max_queue_size=10,
          workers=1,
          use_multiprocessing=False):
    """Trains the model for a fixed number of epochs (iterations on a dataset).

    Args:
        x: Input data. It could be:
          - A Numpy array (or array-like), or a list of arrays
            (in case the model has multiple inputs).
          - A TensorFlow tensor, or a list of tensors
            (in case the model has multiple inputs).
          - A dict mapping input names to the corresponding array/tensors,
            if the model has named inputs.
          - A `tf.data` dataset. Should return a tuple
            of either `(inputs, targets)` or
            `(inputs, targets, sample_weights)`.
          - A generator or `keras.utils.Sequence` returning `(inputs, targets)`
            or `(inputs, targets, sample_weights)`.
          - A `tf.keras.utils.experimental.DatasetCreator`, which wraps a
            callable that takes a single argument of type
            `tf.distribute.InputContext`, and returns a `tf.data.Dataset`.
            `DatasetCreator` should be used when users prefer to specify the
            per-replica batching and sharding logic for the `Dataset`.
            See `tf.keras.utils.experimental.DatasetCreator` doc for more
            information.
          A more detailed description of unpacking behavior for iterator types
          (Dataset, generator, Sequence) is given below. If using
          `tf.distribute.experimental.ParameterServerStrategy`, only
          `DatasetCreator` type is supported for `x`.
        y: Target data. Like the input data `x`,
          it could be either Numpy array(s) or TensorFlow tensor(s).
          It should be consistent with `x` (you cannot have Numpy inputs and
          tensor targets, or inversely). If `x` is a dataset, generator,
          or `keras.utils.Sequence` instance, `y` should
          not be specified (since targets will be obtained from `x`).
        batch_size: Integer or `None`.
            Number of samples per gradient update.
            If unspecified, `batch_size` will default to 32.
            Do not specify the `batch_size` if your data is in the
            form of datasets, generators, or `keras.utils.Sequence` instances
            (since they generate batches).
        epochs: Integer. Number of epochs to train the model.
            An epoch is an iteration over the entire `x` and `y`
            data provided.
            Note that in conjunction with `initial_epoch`,
            `epochs` is to be understood as "final epoch".
            The model is not trained for a number of iterations
            given by `epochs`, but merely until the epoch
            of index `epochs` is reached.
        verbose: 'auto', 0, 1, or 2. Verbosity mode.
            0 = silent, 1 = progress bar, 2 = one line per epoch.
            'auto' defaults to 1 for most cases, but 2 when used with
            `ParameterServerStrategy`. Note that the progress bar is not
            particularly useful when logged to a file, so verbose=2 is
            recommended when not running interactively (eg, in a production
            environment).
        callbacks: List of `keras.callbacks.Callback` instances.
            List of callbacks to apply during training.
            See `tf.keras.callbacks`. Note `tf.keras.callbacks.ProgbarLogger`
            and `tf.keras.callbacks.History` callbacks are created automatically
            and need not be passed into `model.fit`.
            `tf.keras.callbacks.ProgbarLogger` is created or not based on
            `verbose` argument to `model.fit`.
            Callbacks with batch-level calls are currently unsupported with
            `tf.distribute.experimental.ParameterServerStrategy`, and users are
            advised to implement epoch-level calls instead with an appropriate
            `steps_per_epoch` value.
        validation_split: Float between 0 and 1.
            Fraction of the training data to be used as validation data.
            The model will set apart this fraction of the training data,
            will not train on it, and will evaluate
            the loss and any model metrics
            on this data at the end of each epoch.
            The validation data is selected from the last samples
            in the `x` and `y` data provided, before shuffling. This argument is
            not supported when `x` is a dataset, generator or
           `keras.utils.Sequence` instance.
            `validation_split` is not yet supported with
            `tf.distribute.experimental.ParameterServerStrategy`.
        validation_data: Data on which to evaluate
            the loss and any model metrics at the end of each epoch.
            The model will not be trained on this data. Thus, note the fact
            that the validation loss of data provided using `validation_split`
            or `validation_data` is not affected by regularization layers like
            noise and dropout.
            `validation_data` will override `validation_split`.
            `validation_data` could be:
              - A tuple `(x_val, y_val)` of Numpy arrays or tensors.
              - A tuple `(x_val, y_val, val_sample_weights)` of NumPy arrays.
              - A `tf.data.Dataset`.
              - A Python generator or `keras.utils.Sequence` returning
              `(inputs, targets)` or `(inputs, targets, sample_weights)`.
            `validation_data` is not yet supported with
            `tf.distribute.experimental.ParameterServerStrategy`.
        shuffle: Boolean (whether to shuffle the training data
            before each epoch) or str (for 'batch'). This argument is ignored
            when `x` is a generator or an object of tf.data.Dataset.
            'batch' is a special option for dealing
            with the limitations of HDF5 data; it shuffles in batch-sized
            chunks. Has no effect when `steps_per_epoch` is not `None`.
        class_weight: Optional dictionary mapping class indices (integers)
            to a weight (float) value, used for weighting the loss function
            (during training only).
            This can be useful to tell the model to
            "pay more attention" to samples from
            an under-represented class.
        sample_weight: Optional Numpy array of weights for
            the training samples, used for weighting the loss function
            (during training only). You can either pass a flat (1D)
            Numpy array with the same length as the input samples
            (1:1 mapping between weights and samples),
            or in the case of temporal data,
            you can pass a 2D array with shape
            `(samples, sequence_length)`,
            to apply a different weight to every timestep of every sample. This
            argument is not supported when `x` is a dataset, generator, or
           `keras.utils.Sequence` instance, instead provide the sample_weights
            as the third element of `x`.
        initial_epoch: Integer.
            Epoch at which to start training
            (useful for resuming a previous training run).
        steps_per_epoch: Integer or `None`.
            Total number of steps (batches of samples)
            before declaring one epoch finished and starting the
            next epoch. When training with input tensors such as
            TensorFlow data tensors, the default `None` is equal to
            the number of samples in your dataset divided by
            the batch size, or 1 if that cannot be determined. If x is a
            `tf.data` dataset, and 'steps_per_epoch'
            is None, the epoch will run until the input dataset is exhausted.
            When passing an infinitely repeating dataset, you must specify the
            `steps_per_epoch` argument. If `steps_per_epoch=-1` the training
            will run indefinitely with an infinitely repeating dataset.
            This argument is not supported with array inputs.
            When using `tf.distribute.experimental.ParameterServerStrategy`:
              * `steps_per_epoch=None` is not supported.
        validation_steps: Only relevant if `validation_data` is provided and
            is a `tf.data` dataset. Total number of steps (batches of
            samples) to draw before stopping when performing validation
            at the end of every epoch. If 'validation_steps' is None, validation
            will run until the `validation_data` dataset is exhausted. In the
            case of an infinitely repeated dataset, it will run into an
            infinite loop. If 'validation_steps' is specified and only part of
            the dataset will be consumed, the evaluation will start from the
            beginning of the dataset at each epoch. This ensures that the same
            validation samples are used every time.
        validation_batch_size: Integer or `None`.
            Number of samples per validation batch.
            If unspecified, will default to `batch_size`.
            Do not specify the `validation_batch_size` if your data is in the
            form of datasets, generators, or `keras.utils.Sequence` instances
            (since they generate batches).
        validation_freq: Only relevant if validation data is provided. Integer
            or `collections.abc.Container` instance (e.g. list, tuple, etc.).
            If an integer, specifies how many training epochs to run before a
            new validation run is performed, e.g. `validation_freq=2` runs
            validation every 2 epochs. If a Container, specifies the epochs on
            which to run validation, e.g. `validation_freq=[1, 2, 10]` runs
            validation at the end of the 1st, 2nd, and 10th epochs.
        max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
            input only. Maximum size for the generator queue.
            If unspecified, `max_queue_size` will default to 10.
        workers: Integer. Used for generator or `keras.utils.Sequence` input
            only. Maximum number of processes to spin up
            when using process-based threading. If unspecified, `workers`
            will default to 1.
        use_multiprocessing: Boolean. Used for generator or
            `keras.utils.Sequence` input only. If `True`, use process-based
            threading. If unspecified, `use_multiprocessing` will default to
            `False`. Note that because this implementation relies on
            multiprocessing, you should not pass non-picklable arguments to
            the generator as they can't be passed easily to children processes.

    Unpacking behavior for iterator-like inputs:
        A common pattern is to pass a tf.data.Dataset, generator, or
      tf.keras.utils.Sequence to the `x` argument of fit, which will in fact
      yield not only features (x) but optionally targets (y) and sample weights.
      Keras requires that the output of such iterator-likes be unambiguous. The
      iterator should return a tuple of length 1, 2, or 3, where the optional
      second and third elements will be used for y and sample_weight
      respectively. Any other type provided will be wrapped in a length one
      tuple, effectively treating everything as 'x'. When yielding dicts, they
      should still adhere to the top-level tuple structure.
      e.g. `({"x0": x0, "x1": x1}, y)`. Keras will not attempt to separate
      features, targets, and weights from the keys of a single dict.
        A notable unsupported data type is the namedtuple. The reason is that
      it behaves like both an ordered datatype (tuple) and a mapping
      datatype (dict). So given a namedtuple of the form:
          `namedtuple("example_tuple", ["y", "x"])`
      it is ambiguous whether to reverse the order of the elements when
      interpreting the value. Even worse is a tuple of the form:
          `namedtuple("other_tuple", ["x", "y", "z"])`
      where it is unclear if the tuple was intended to be unpacked into x, y,
      and sample_weight or passed through as a single element to `x`. As a
      result the data processing code will simply raise a ValueError if it
      encounters a namedtuple. (Along with instructions to remedy the issue.)

    Returns:
        A `History` object. Its `History.history` attribute is
        a record of training loss values and metrics values
        at successive epochs, as well as validation loss values
        and validation metrics values (if applicable).

    Raises:
        RuntimeError: 1. If the model was never compiled or,
        2. If `model.fit` is  wrapped in `tf.function`.

        ValueError: In case of mismatch between the provided input data
            and what the model expects or when the input data is empty.
    """
    base_layer.keras_api_gauge.get_cell('fit').set(True)
    # Legacy graph support is contained in `training_v1.Model`.
    version_utils.disallow_legacy_graph('Model', 'fit')
    self._assert_compile_was_called()
    self._check_call_args('fit')
    _disallow_inside_tf_function('fit')

    if verbose == 'auto':
      if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
        verbose = 2  # Default to epoch-level logging for PSStrategy.
      else:
        verbose = 1  # Default to batch-level logging otherwise.

    if validation_split:
      # Create the validation data using the training data. Only supported for
      # `Tensor` and `NumPy` input.
      (x, y, sample_weight), validation_data = (
          data_adapter.train_validation_split(
              (x, y, sample_weight), validation_split=validation_split))

    if validation_data:
      val_x, val_y, val_sample_weight = (
          data_adapter.unpack_x_y_sample_weight(validation_data))

    if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
      self._cluster_coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
          self.distribute_strategy)

    with self.distribute_strategy.scope(), \
         training_utils.RespectCompiledTrainableState(self):
      # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
      data_handler = data_adapter.get_data_handler(
          x=x,
          y=y,
          sample_weight=sample_weight,
          batch_size=batch_size,
          steps_per_epoch=steps_per_epoch,
          initial_epoch=initial_epoch,
          epochs=epochs,
          shuffle=shuffle,
          class_weight=class_weight,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          model=self,
          steps_per_execution=self._steps_per_execution)

      # Container that configures and calls `tf.keras.Callback`s.
      if not isinstance(callbacks, callbacks_module.CallbackList):
        callbacks = callbacks_module.CallbackList(
            callbacks,
            add_history=True,
            add_progbar=verbose != 0,
            model=self,
            verbose=verbose,
            epochs=epochs,
            steps=data_handler.inferred_steps)

      self.stop_training = False
      self.train_function = self.make_train_function()
      self._train_counter.assign(0)
      callbacks.on_train_begin()
      training_logs = None
      # Handle fault-tolerance for multi-worker.
      # TODO(omalleyt): Fix the ordering issues that mean this has to
      # happen after `callbacks.on_train_begin`.
      data_handler._initial_epoch = (  # pylint: disable=protected-access
          self._maybe_load_initial_epoch_from_ckpt(initial_epoch))
      logs = None
      for epoch, iterator in data_handler.enumerate_epochs():
        self.reset_metrics()
        callbacks.on_epoch_begin(epoch)
        with data_handler.catch_stop_iteration():
          for step in data_handler.steps():
            with tf.profiler.experimental.Trace(
                'train',
                epoch_num=epoch,
                step_num=step,
                batch_size=batch_size,
                _r=1):
              callbacks.on_train_batch_begin(step)
              tmp_logs = self.train_function(iterator)
              if data_handler.should_sync:
                context.async_wait()
              logs = tmp_logs  # No error, now safe to assign to logs.
              end_step = step + data_handler.step_increment
              callbacks.on_train_batch_end(end_step, logs)
              if self.stop_training:
                break

        logs = tf_utils.sync_to_numpy_or_python_type(logs)
        if logs is None:
          raise ValueError('Expect x to be a non-empty array or dataset.')
        epoch_logs = copy.copy(logs)

        # Run validation.
        if validation_data and self._should_eval(epoch, validation_freq):
          # Create data_handler for evaluation and cache it.
          if getattr(self, '_eval_data_handler', None) is None:
            self._eval_data_handler = data_adapter.get_data_handler(
                x=val_x,
                y=val_y,
                sample_weight=val_sample_weight,
                batch_size=validation_batch_size or batch_size,
                steps_per_epoch=validation_steps,
                initial_epoch=0,
                epochs=1,
                max_queue_size=max_queue_size,
                workers=workers,
                use_multiprocessing=use_multiprocessing,
                model=self,
                steps_per_execution=self._steps_per_execution)
          val_logs = self.evaluate(
              x=val_x,
              y=val_y,
              sample_weight=val_sample_weight,
              batch_size=validation_batch_size or batch_size,
              steps=validation_steps,
              callbacks=callbacks,
              max_queue_size=max_queue_size,
              workers=workers,
              use_multiprocessing=use_multiprocessing,
              return_dict=True,
              _use_cached_eval_dataset=True)
          val_logs = {'val_' + name: val for name, val in val_logs.items()}
          epoch_logs.update(val_logs)

        callbacks.on_epoch_end(epoch, epoch_logs)
        training_logs = epoch_logs
        if self.stop_training:
          break

      # If eval data_hanlder exists, delete it after all epochs are done.
      if getattr(self, '_eval_data_handler', None) is not None:
        del self._eval_data_handler
      callbacks.on_train_end(logs=training_logs)
      return self.history

  def test_step(self, data):
    """The logic for one evaluation step.

    This method can be overridden to support custom evaluation logic.
    This method is called by `Model.make_test_function`.

    This function should contain the mathematical logic for one step of
    evaluation.
    This typically includes the forward pass, loss calculation, and metrics
    updates.

    Configuration details for *how* this logic is run (e.g. `tf.function` and
    `tf.distribute.Strategy` settings), should be left to
    `Model.make_test_function`, which can also be overridden.

    Args:
      data: A nested structure of `Tensor`s.

    Returns:
      A `dict` containing values that will be passed to
      `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
      values of the `Model`'s metrics are returned.
    """
    data = data_adapter.expand_1d(data)
    x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)

    y_pred = self(x, training=False)
    # Updates stateful loss metrics.
    self.compiled_loss(
        y, y_pred, sample_weight, regularization_losses=self.losses)
    self.compiled_metrics.update_state(y, y_pred, sample_weight)
    # Collect metrics to return
    return_metrics = {}
    for metric in self.metrics:
      result = metric.result()
      if isinstance(result, dict):
        return_metrics.update(result)
      else:
        return_metrics[metric.name] = result
    return return_metrics

  def make_test_function(self, force=False):
    """Creates a function that executes one step of evaluation.

    This method can be overridden to support custom evaluation logic.
    This method is called by `Model.evaluate` and `Model.test_on_batch`.

    Typically, this method directly controls `tf.function` and
    `tf.distribute.Strategy` settings, and delegates the actual evaluation
    logic to `Model.test_step`.

    This function is cached the first time `Model.evaluate` or
    `Model.test_on_batch` is called. The cache is cleared whenever
    `Model.compile` is called. You can skip the cache and generate again the
    function with `force=True`.

    Args:
      force: Whether to regenerate the test function and skip the cached
        function if available.

    Returns:
      Function. The function created by this method should accept a
      `tf.data.Iterator`, and return a `dict` containing values that will
      be passed to `tf.keras.Callbacks.on_test_batch_end`.
    """
    if self.test_function is not None and not force:
      return self.test_function

    def step_function(model, iterator):
      """Runs a single evaluation step."""

      def run_step(data):
        outputs = model.test_step(data)
        # Ensure counter is updated only if `test_step` succeeds.
        with tf.control_dependencies(_minimum_control_deps(outputs)):
          model._test_counter.assign_add(1)  # pylint: disable=protected-access
        return outputs

      data = next(iterator)
      outputs = model.distribute_strategy.run(run_step, args=(data,))
      outputs = reduce_per_replica(
          outputs, self.distribute_strategy, reduction='first')
      return outputs

    if (self._steps_per_execution is None or
        self._steps_per_execution.numpy().item() == 1):

      def test_function(iterator):
        """Runs an evaluation execution with one step."""
        return step_function(self, iterator)

    else:

      def test_function(iterator):
        """Runs an evaluation execution with multiple steps."""
        for _ in tf.range(self._steps_per_execution):
          outputs = step_function(self, iterator)
        return outputs

    if not self.run_eagerly:
      test_function = tf.function(
          test_function, experimental_relax_shapes=True)

    self.test_function = test_function

    if self._cluster_coordinator:
      self.test_function = lambda iterator: self._cluster_coordinator.schedule(  # pylint: disable=g-long-lambda
          test_function, args=(iterator,))

    return self.test_function

  def evaluate(self,
               x=None,
               y=None,
               batch_size=None,
               verbose=1,
               sample_weight=None,
               steps=None,
               callbacks=None,
               max_queue_size=10,
               workers=1,
               use_multiprocessing=False,
               return_dict=False,
               **kwargs):
    """Returns the loss value & metrics values for the model in test mode.

    Computation is done in batches (see the `batch_size` arg.)

    Args:
        x: Input data. It could be:
          - A Numpy array (or array-like), or a list of arrays
            (in case the model has multiple inputs).
          - A TensorFlow tensor, or a list of tensors
            (in case the model has multiple inputs).
          - A dict mapping input names to the corresponding array/tensors,
            if the model has named inputs.
          - A `tf.data` dataset. Should return a tuple
            of either `(inputs, targets)` or
            `(inputs, targets, sample_weights)`.
          - A generator or `keras.utils.Sequence` returning `(inputs, targets)`
            or `(inputs, targets, sample_weights)`.
          A more detailed description of unpacking behavior for iterator types
          (Dataset, generator, Sequence) is given in the `Unpacking behavior
          for iterator-like inputs` section of `Model.fit`.
        y: Target data. Like the input data `x`, it could be either Numpy
          array(s) or TensorFlow tensor(s). It should be consistent with `x`
          (you cannot have Numpy inputs and tensor targets, or inversely). If
          `x` is a dataset, generator or `keras.utils.Sequence` instance, `y`
          should not be specified (since targets will be obtained from the
          iterator/dataset).
        batch_size: Integer or `None`. Number of samples per batch of
          computation. If unspecified, `batch_size` will default to 32. Do not
          specify the `batch_size` if your data is in the form of a dataset,
          generators, or `keras.utils.Sequence` instances (since they generate
          batches).
        verbose: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
        sample_weight: Optional Numpy array of weights for the test samples,
          used for weighting the loss function. You can either pass a flat (1D)
          Numpy array with the same length as the input samples
            (1:1 mapping between weights and samples), or in the case of
              temporal data, you can pass a 2D array with shape `(samples,
              sequence_length)`, to apply a different weight to every timestep
              of every sample. This argument is not supported when `x` is a
              dataset, instead pass sample weights as the third element of `x`.
        steps: Integer or `None`. Total number of steps (batches of samples)
          before declaring the evaluation round finished. Ignored with the
          default value of `None`. If x is a `tf.data` dataset and `steps` is
          None, 'evaluate' will run until the dataset is exhausted. This
          argument is not supported with array inputs.
        callbacks: List of `keras.callbacks.Callback` instances. List of
          callbacks to apply during evaluation. See
          [callbacks](/api_docs/python/tf/keras/callbacks).
        max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
          input only. Maximum size for the generator queue. If unspecified,
          `max_queue_size` will default to 10.
        workers: Integer. Used for generator or `keras.utils.Sequence` input
          only. Maximum number of processes to spin up when using process-based
          threading. If unspecified, `workers` will default to 1.
        use_multiprocessing: Boolean. Used for generator or
          `keras.utils.Sequence` input only. If `True`, use process-based
          threading. If unspecified, `use_multiprocessing` will default to
          `False`. Note that because this implementation relies on
          multiprocessing, you should not pass non-picklable arguments to the
          generator as they can't be passed easily to children processes.
        return_dict: If `True`, loss and metric results are returned as a dict,
          with each key being the name of the metric. If `False`, they are
          returned as a list.
        **kwargs: Unused at this time.

    See the discussion of `Unpacking behavior for iterator-like inputs` for
    `Model.fit`.

    `Model.evaluate` is not yet supported with
    `tf.distribute.experimental.ParameterServerStrategy`.

    Returns:
        Scalar test loss (if the model has a single output and no metrics)
        or list of scalars (if the model has multiple outputs
        and/or metrics). The attribute `model.metrics_names` will give you
        the display labels for the scalar outputs.

    Raises:
        RuntimeError: If `model.evaluate` is wrapped in `tf.function`.
        ValueError: in case of invalid arguments.
    """
    base_layer.keras_api_gauge.get_cell('evaluate').set(True)
    version_utils.disallow_legacy_graph('Model', 'evaluate')
    self._assert_compile_was_called()
    self._check_call_args('evaluate')
    _disallow_inside_tf_function('evaluate')
    use_cached_eval_dataset = kwargs.pop('_use_cached_eval_dataset', False)
    if kwargs:
      raise TypeError('Invalid keyword arguments: %s' % (kwargs,))

    if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
      self._cluster_coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
          self.distribute_strategy)

    with self.distribute_strategy.scope():
      # Use cached evaluation data only when it's called in `Model.fit`
      if (use_cached_eval_dataset
          and getattr(self, '_eval_data_handler', None) is not None):
        data_handler = self._eval_data_handler
      else:
        # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
        data_handler = data_adapter.get_data_handler(
            x=x,
            y=y,
            sample_weight=sample_weight,
            batch_size=batch_size,
            steps_per_epoch=steps,
            initial_epoch=0,
            epochs=1,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
            model=self,
            steps_per_execution=self._steps_per_execution)

      # Container that configures and calls `tf.keras.Callback`s.
      if not isinstance(callbacks, callbacks_module.CallbackList):
        callbacks = callbacks_module.CallbackList(
            callbacks,
            add_history=True,
            add_progbar=verbose != 0,
            model=self,
            verbose=verbose,
            epochs=1,
            steps=data_handler.inferred_steps)

      logs = {}
      self.test_function = self.make_test_function()
      self._test_counter.assign(0)
      callbacks.on_test_begin()
      for _, iterator in data_handler.enumerate_epochs():  # Single epoch.
        self.reset_metrics()
        with data_handler.catch_stop_iteration():
          for step in data_handler.steps():
            with tf.profiler.experimental.Trace('test', step_num=step, _r=1):
              callbacks.on_test_batch_begin(step)
              tmp_logs = self.test_function(iterator)
              if data_handler.should_sync:
                context.async_wait()
              logs = tmp_logs  # No error, now safe to assign to logs.
              end_step = step + data_handler.step_increment
              callbacks.on_test_batch_end(end_step, logs)
      logs = tf_utils.sync_to_numpy_or_python_type(logs)
      callbacks.on_test_end(logs=logs)

      if return_dict:
        return logs
      else:
        return flatten_metrics_in_order(logs, self.metrics_names)

  def predict_step(self, data):
    """The logic for one inference step.

    This method can be overridden to support custom inference logic.
    This method is called by `Model.make_predict_function`.

    This method should contain the mathematical logic for one step of inference.
    This typically includes the forward pass.

    Configuration details for *how* this logic is run (e.g. `tf.function` and
    `tf.distribute.Strategy` settings), should be left to
    `Model.make_predict_function`, which can also be overridden.

    Args:
      data: A nested structure of `Tensor`s.

    Returns:
      The result of one inference step, typically the output of calling the
      `Model` on data.
    """
    data = data_adapter.expand_1d(data)
    x, _, _ = data_adapter.unpack_x_y_sample_weight(data)
    return self(x, training=False)

  def make_predict_function(self, force=False):
    """Creates a function that executes one step of inference.

    This method can be overridden to support custom inference logic.
    This method is called by `Model.predict` and `Model.predict_on_batch`.

    Typically, this method directly controls `tf.function` and
    `tf.distribute.Strategy` settings, and delegates the actual evaluation
    logic to `Model.predict_step`.

    This function is cached the first time `Model.predict` or
    `Model.predict_on_batch` is called. The cache is cleared whenever
    `Model.compile` is called. You can skip the cache and generate again the
    function with `force=True`.

    Args:
      force: Whether to regenerate the predict function and skip the cached
        function if available.

    Returns:
      Function. The function created by this method should accept a
      `tf.data.Iterator`, and return the outputs of the `Model`.
    """
    if self.predict_function is not None and not force:
      return self.predict_function

    def step_function(model, iterator):
      """Runs a single evaluation step."""

      def run_step(data):
        outputs = model.predict_step(data)
        # Ensure counter is updated only if `test_step` succeeds.
        with tf.control_dependencies(_minimum_control_deps(outputs)):
          model._predict_counter.assign_add(1)  # pylint: disable=protected-access
        return outputs

      data = next(iterator)
      outputs = model.distribute_strategy.run(run_step, args=(data,))
      outputs = reduce_per_replica(
          outputs, self.distribute_strategy, reduction='concat')
      return outputs

    if (self._steps_per_execution is None or
        self._steps_per_execution.numpy().item() == 1):

      def predict_function(iterator):
        """Runs an evaluation execution with one step."""
        return step_function(self, iterator)

    else:

      def predict_function(iterator):
        """Runs an evaluation execution with multiple steps."""
        outputs = step_function(self, iterator)
        for _ in tf.range(self._steps_per_execution - 1):
          tf.autograph.experimental.set_loop_options(
              shape_invariants=[(
                  t, tf_utils.get_tensor_spec(t, dynamic_batch=True).shape)
                                for t in tf.nest.flatten(outputs)])
          step_outputs = step_function(self, iterator)
          outputs = tf.nest.map_structure(lambda t1, t2: concat([t1, t2]), outputs,
                                       step_outputs)
        return outputs

    if not self.run_eagerly:
      predict_function = tf.function(
          predict_function, experimental_relax_shapes=True)

    self.predict_function = predict_function
    return self.predict_function

  def predict(self,
              x,
              batch_size=None,
              verbose=0,
              steps=None,
              callbacks=None,
              max_queue_size=10,
              workers=1,
              use_multiprocessing=False):
    """Generates output predictions for the input samples.

    Computation is done in batches. This method is designed for performance in
    large scale inputs. For small amount of inputs that fit in one batch,
    directly using `__call__` is recommended for faster execution, e.g.,
    `model(x)`, or `model(x, training=False)` if you have layers such as
    `tf.keras.layers.BatchNormalization` that behaves differently during
    inference. Also, note the fact that test loss is not affected by
    regularization layers like noise and dropout.

    Args:
        x: Input samples. It could be:
          - A Numpy array (or array-like), or a list of arrays
            (in case the model has multiple inputs).
          - A TensorFlow tensor, or a list of tensors
            (in case the model has multiple inputs).
          - A `tf.data` dataset.
          - A generator or `keras.utils.Sequence` instance.
          A more detailed description of unpacking behavior for iterator types
          (Dataset, generator, Sequence) is given in the `Unpacking behavior
          for iterator-like inputs` section of `Model.fit`.
        batch_size: Integer or `None`.
            Number of samples per batch.
            If unspecified, `batch_size` will default to 32.
            Do not specify the `batch_size` if your data is in the
            form of dataset, generators, or `keras.utils.Sequence` instances
            (since they generate batches).
        verbose: Verbosity mode, 0 or 1.
        steps: Total number of steps (batches of samples)
            before declaring the prediction round finished.
            Ignored with the default value of `None`. If x is a `tf.data`
            dataset and `steps` is None, `predict` will
            run until the input dataset is exhausted.
        callbacks: List of `keras.callbacks.Callback` instances.
            List of callbacks to apply during prediction.
            See [callbacks](/api_docs/python/tf/keras/callbacks).
        max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
            input only. Maximum size for the generator queue.
            If unspecified, `max_queue_size` will default to 10.
        workers: Integer. Used for generator or `keras.utils.Sequence` input
            only. Maximum number of processes to spin up when using
            process-based threading. If unspecified, `workers` will default
            to 1.
        use_multiprocessing: Boolean. Used for generator or
            `keras.utils.Sequence` input only. If `True`, use process-based
            threading. If unspecified, `use_multiprocessing` will default to
            `False`. Note that because this implementation relies on
            multiprocessing, you should not pass non-picklable arguments to
            the generator as they can't be passed easily to children processes.

    See the discussion of `Unpacking behavior for iterator-like inputs` for
    `Model.fit`. Note that Model.predict uses the same interpretation rules as
    `Model.fit` and `Model.evaluate`, so inputs must be unambiguous for all
    three methods.

    Returns:
        Numpy array(s) of predictions.

    Raises:
        RuntimeError: If `model.predict` is wrapped in `tf.function`.
        ValueError: In case of mismatch between the provided
            input data and the model's expectations,
            or in case a stateful model receives a number of samples
            that is not a multiple of the batch size.
    """
    base_layer.keras_api_gauge.get_cell('predict').set(True)
    version_utils.disallow_legacy_graph('Model', 'predict')
    self._check_call_args('predict')
    _disallow_inside_tf_function('predict')

    # TODO(yashkatariya): Cache model on the coordinator for faster prediction.
    # If running under PSS, then swap it with OneDeviceStrategy so that
    # execution will run on the coordinator.
    original_pss_strategy = None
    if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
      original_pss_strategy = self.distribute_strategy
      self._distribution_strategy = None

    # Cluster coordinator is set by `.fit()` and `.evaluate()` which is not
    # needed in `.predict()` because all the predictions happen on the
    # coordinator/locally.
    if self._cluster_coordinator:
      self._cluster_coordinator = None

    outputs = None
    with self.distribute_strategy.scope():
      # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
      dataset_types = (tf.compat.v1.data.Dataset, tf.data.Dataset)
      if (self._in_multi_worker_mode() or _is_tpu_multi_host(
          self.distribute_strategy)) and isinstance(x, dataset_types):
        try:
          options = tf.data.Options()
          data_option = tf.data.experimental.AutoShardPolicy.DATA
          options.experimental_distribute.auto_shard_policy = data_option
          x = x.with_options(options)
        except ValueError:
          warnings.warn('Using Model.predict with '
                        'MultiWorkerDistributionStrategy or TPUStrategy and '
                        'AutoShardPolicy.FILE might lead to out-of-order result'
                        '. Consider setting it to AutoShardPolicy.DATA.')

      data_handler = data_adapter.get_data_handler(
          x=x,
          batch_size=batch_size,
          steps_per_epoch=steps,
          initial_epoch=0,
          epochs=1,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          model=self,
          steps_per_execution=self._steps_per_execution)

      # Container that configures and calls `tf.keras.Callback`s.
      if not isinstance(callbacks, callbacks_module.CallbackList):
        callbacks = callbacks_module.CallbackList(
            callbacks,
            add_history=True,
            add_progbar=verbose != 0,
            model=self,
            verbose=verbose,
            epochs=1,
            steps=data_handler.inferred_steps)

      self.predict_function = self.make_predict_function()
      self._predict_counter.assign(0)
      callbacks.on_predict_begin()
      batch_outputs = None
      for _, iterator in data_handler.enumerate_epochs():  # Single epoch.
        with data_handler.catch_stop_iteration():
          for step in data_handler.steps():
            callbacks.on_predict_batch_begin(step)
            tmp_batch_outputs = self.predict_function(iterator)
            if data_handler.should_sync:
              context.async_wait()
            batch_outputs = tmp_batch_outputs  # No error, now safe to assign.
            if outputs is None:
              outputs = tf.nest.map_structure(lambda batch_output: [batch_output],
                                           batch_outputs)
            else:
              tf.__internal__.nest.map_structure_up_to(
                  batch_outputs,
                  lambda output, batch_output: output.append(batch_output),
                  outputs, batch_outputs)
            end_step = step + data_handler.step_increment
            callbacks.on_predict_batch_end(end_step, {'outputs': batch_outputs})
      if batch_outputs is None:
        raise ValueError('Expect x to be a non-empty array or dataset.')
      callbacks.on_predict_end()
    all_outputs = tf.__internal__.nest.map_structure_up_to(batch_outputs, concat, outputs)

    # If originally PSS strategy was used, then replace it back since predict
    # is running under `OneDeviceStrategy` after the swap and once its done
    # we need to replace it back to PSS again.
    if original_pss_strategy is not None:
      self._distribution_strategy = original_pss_strategy

    return tf_utils.sync_to_numpy_or_python_type(all_outputs)

  def reset_metrics(self):
    """Resets the state of all the metrics in the model.

    Examples:

    >>> inputs = tf.keras.layers.Input(shape=(3,))
    >>> outputs = tf.keras.layers.Dense(2)(inputs)
    >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])

    >>> x = np.random.random((2, 3))
    >>> y = np.random.randint(0, 2, (2, 2))
    >>> _ = model.fit(x, y, verbose=0)
    >>> assert all(float(m.result()) for m in model.metrics)

    >>> model.reset_metrics()
    >>> assert all(float(m.result()) == 0 for m in model.metrics)

    """
    for m in self.metrics:
      m.reset_state()

  def train_on_batch(self,
                     x,
                     y=None,
                     sample_weight=None,
                     class_weight=None,
                     reset_metrics=True,
                     return_dict=False):
    """Runs a single gradient update on a single batch of data.

    Args:
        x: Input data. It could be:
          - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
          - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
          - A dict mapping input names to the corresponding array/tensors,
              if the model has named inputs.
        y: Target data. Like the input data `x`, it could be either Numpy
          array(s) or TensorFlow tensor(s). It should be consistent with `x`
          (you cannot have Numpy inputs and tensor targets, or inversely).
        sample_weight: Optional array of the same length as x, containing
          weights to apply to the model's loss for each sample. In the case of
          temporal data, you can pass a 2D array with shape (samples,
          sequence_length), to apply a different weight to every timestep of
          every sample.
        class_weight: Optional dictionary mapping class indices (integers) to a
          weight (float) to apply to the model's loss for the samples from this
          class during training. This can be useful to tell the model to "pay
          more attention" to samples from an under-represented class.
        reset_metrics: If `True`, the metrics returned will be only for this
          batch. If `False`, the metrics will be statefully accumulated across
          batches.
        return_dict: If `True`, loss and metric results are returned as a dict,
          with each key being the name of the metric. If `False`, they are
          returned as a list.

    Returns:
        Scalar training loss
        (if the model has a single output and no metrics)
        or list of scalars (if the model has multiple outputs
        and/or metrics). The attribute `model.metrics_names` will give you
        the display labels for the scalar outputs.

    Raises:
      RuntimeError: If `model.train_on_batch` is wrapped in `tf.function`.
      ValueError: In case of invalid user-provided arguments.
    """
    self._assert_compile_was_called()
    self._check_call_args('train_on_batch')
    _disallow_inside_tf_function('train_on_batch')
    with self.distribute_strategy.scope(), \
         training_utils.RespectCompiledTrainableState(self):
      iterator = data_adapter.single_batch_iterator(self.distribute_strategy, x,
                                                    y, sample_weight,
                                                    class_weight)
      self.train_function = self.make_train_function()
      logs = self.train_function(iterator)

    if reset_metrics:
      self.reset_metrics()
    logs = tf_utils.sync_to_numpy_or_python_type(logs)
    if return_dict:
      return logs
    else:
      return flatten_metrics_in_order(logs, self.metrics_names)

  def test_on_batch(self,
                    x,
                    y=None,
                    sample_weight=None,
                    reset_metrics=True,
                    return_dict=False):
    """Test the model on a single batch of samples.

    Args:
        x: Input data. It could be:
          - A Numpy array (or array-like), or a list of arrays (in case the
              model has multiple inputs).
          - A TensorFlow tensor, or a list of tensors (in case the model has
              multiple inputs).
          - A dict mapping input names to the corresponding array/tensors, if
              the model has named inputs.
        y: Target data. Like the input data `x`, it could be either Numpy
          array(s) or TensorFlow tensor(s). It should be consistent with `x`
          (you cannot have Numpy inputs and tensor targets, or inversely).
        sample_weight: Optional array of the same length as x, containing
          weights to apply to the model's loss for each sample. In the case of
          temporal data, you can pass a 2D array with shape (samples,
          sequence_length), to apply a different weight to every timestep of
          every sample.
        reset_metrics: If `True`, the metrics returned will be only for this
          batch. If `False`, the metrics will be statefully accumulated across
          batches.
        return_dict: If `True`, loss and metric results are returned as a dict,
          with each key being the name of the metric. If `False`, they are
          returned as a list.

    Returns:
        Scalar test loss (if the model has a single output and no metrics)
        or list of scalars (if the model has multiple outputs
        and/or metrics). The attribute `model.metrics_names` will give you
        the display labels for the scalar outputs.

    Raises:
        RuntimeError: If `model.test_on_batch` is wrapped in `tf.function`.
        ValueError: In case of invalid user-provided arguments.
    """
    self._assert_compile_was_called()
    self._check_call_args('test_on_batch')
    _disallow_inside_tf_function('test_on_batch')
    with self.distribute_strategy.scope():
      iterator = data_adapter.single_batch_iterator(self.distribute_strategy, x,
                                                    y, sample_weight)
      self.test_function = self.make_test_function()
      logs = self.test_function(iterator)

    if reset_metrics:
      self.reset_metrics()
    logs = tf_utils.sync_to_numpy_or_python_type(logs)
    if return_dict:
      return logs
    else:
      return flatten_metrics_in_order(logs, self.metrics_names)

  def predict_on_batch(self, x):
    """Returns predictions for a single batch of samples.

    Args:
        x: Input data. It could be:
          - A Numpy array (or array-like), or a list of arrays (in case the
              model has multiple inputs).
          - A TensorFlow tensor, or a list of tensors (in case the model has
              multiple inputs).

    Returns:
        Numpy array(s) of predictions.

    Raises:
        RuntimeError: If `model.predict_on_batch` is wrapped in `tf.function`.
        ValueError: In case of mismatch between given number of inputs and
          expectations of the model.
    """
    self._check_call_args('predict_on_batch')
    _disallow_inside_tf_function('predict_on_batch')
    with self.distribute_strategy.scope():
      iterator = data_adapter.single_batch_iterator(self.distribute_strategy, x)
      self.predict_function = self.make_predict_function()
      outputs = self.predict_function(iterator)
    return tf_utils.sync_to_numpy_or_python_type(outputs)

  @doc_controls.do_not_generate_docs
  def fit_generator(self,
                    generator,
                    steps_per_epoch=None,
                    epochs=1,
                    verbose=1,
                    callbacks=None,
                    validation_data=None,
                    validation_steps=None,
                    validation_freq=1,
                    class_weight=None,
                    max_queue_size=10,
                    workers=1,
                    use_multiprocessing=False,
                    shuffle=True,
                    initial_epoch=0):
    """Fits the model on data yielded batch-by-batch by a Python generator.

    DEPRECATED:
      `Model.fit` now supports generators, so there is no longer any need to use
      this endpoint.
    """
    warnings.warn('`Model.fit_generator` is deprecated and '
                  'will be removed in a future version. '
                  'Please use `Model.fit`, which supports generators.')
    return self.fit(
        generator,
        steps_per_epoch=steps_per_epoch,
        epochs=epochs,
        verbose=verbose,
        callbacks=callbacks,
        validation_data=validation_data,
        validation_steps=validation_steps,
        validation_freq=validation_freq,
        class_weight=class_weight,
        max_queue_size=max_queue_size,
        workers=workers,
        use_multiprocessing=use_multiprocessing,
        shuffle=shuffle,
        initial_epoch=initial_epoch)

  @doc_controls.do_not_generate_docs
  def evaluate_generator(self,
                         generator,
                         steps=None,
                         callbacks=None,
                         max_queue_size=10,
                         workers=1,
                         use_multiprocessing=False,
                         verbose=0):
    """Evaluates the model on a data generator.

    DEPRECATED:
      `Model.evaluate` now supports generators, so there is no longer any need
      to use this endpoint.
    """
    warnings.warn('`Model.evaluate_generator` is deprecated and '
                  'will be removed in a future version. '
                  'Please use `Model.evaluate`, which supports generators.')
    self._check_call_args('evaluate_generator')

    return self.evaluate(
        generator,
        steps=steps,
        max_queue_size=max_queue_size,
        workers=workers,
        use_multiprocessing=use_multiprocessing,
        verbose=verbose,
        callbacks=callbacks)

  @doc_controls.do_not_generate_docs
  def predict_generator(self,
                        generator,
                        steps=None,
                        callbacks=None,
                        max_queue_size=10,
                        workers=1,
                        use_multiprocessing=False,
                        verbose=0):
    """Generates predictions for the input samples from a data generator.

    DEPRECATED:
      `Model.predict` now supports generators, so there is no longer any need
      to use this endpoint.
    """
    warnings.warn('`Model.predict_generator` is deprecated and '
                  'will be removed in a future version. '
                  'Please use `Model.predict`, which supports generators.')
    return self.predict(
        generator,
        steps=steps,
        max_queue_size=max_queue_size,
        workers=workers,
        use_multiprocessing=use_multiprocessing,
        verbose=verbose,
        callbacks=callbacks)

  ######################################################################
  # Functions below are not training related. They are for model weights
  # tracking, save/load, serialization, etc.
  ######################################################################

  @property
  def trainable_weights(self):
    self._assert_weights_created()
    if not self._trainable:
      return []
    trainable_variables = []
    for trackable_obj in self._self_tracked_trackables:
      trainable_variables += trackable_obj.trainable_variables
    trainable_variables += self._trainable_weights
    return self._dedup_weights(trainable_variables)

  @property
  def non_trainable_weights(self):
    self._assert_weights_created()
    non_trainable_variables = []
    for trackable_obj in self._self_tracked_trackables:
      non_trainable_variables += trackable_obj.non_trainable_variables

    if not self._trainable:
      # Return order is all trainable vars, then all non-trainable vars.
      trainable_variables = []
      for trackable_obj in self._self_tracked_trackables:
        trainable_variables += trackable_obj.trainable_variables

      non_trainable_variables = (
          trainable_variables + self._trainable_weights +
          non_trainable_variables + self._non_trainable_weights)
    else:
      non_trainable_variables = (
          non_trainable_variables + self._non_trainable_weights)

    return self._dedup_weights(non_trainable_variables)

  def get_weights(self):
    """Retrieves the weights of the model.

    Returns:
        A flat list of Numpy arrays.
    """
    with self.distribute_strategy.scope():
      return super(Model, self).get_weights()

  def save(self,
           filepath,
           overwrite=True,
           include_optimizer=True,
           save_format=None,
           signatures=None,
           options=None,
           save_traces=True):
    # pylint: disable=line-too-long
    """Saves the model to Tensorflow SavedModel or a single HDF5 file.

    Please see `tf.keras.models.save_model` or the
    [Serialization and Saving guide](https://keras.io/guides/serialization_and_saving/)
    for details.

    Args:
        filepath: String, PathLike, path to SavedModel or H5 file to save the
            model.
        overwrite: Whether to silently overwrite any existing file at the
            target location, or provide the user with a manual prompt.
        include_optimizer: If True, save optimizer's state together.
        save_format: Either `'tf'` or `'h5'`, indicating whether to save the
            model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X,
            and 'h5' in TF 1.X.
        signatures: Signatures to save with the SavedModel. Applicable to the
            'tf' format only. Please see the `signatures` argument in
            `tf.saved_model.save` for details.
        options: (only applies to SavedModel format)
            `tf.saved_model.SaveOptions` object that specifies options for
            saving to SavedModel.
        save_traces: (only applies to SavedModel format) When enabled, the
            SavedModel will store the function traces for each layer. This
            can be disabled, so that only the configs of each layer are stored.
            Defaults to `True`. Disabling this will decrease serialization time
            and reduce file size, but it requires that all custom layers/models
            implement a `get_config()` method.

    Example:

    ```python
    from keras.models import load_model

    model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
    del model  # deletes the existing model

    # returns a compiled model
    # identical to the previous one
    model = load_model('my_model.h5')
    ```
    """
    # pylint: enable=line-too-long
    save.save_model(self, filepath, overwrite, include_optimizer, save_format,
                    signatures, options, save_traces)

  def save_weights(self,
                   filepath,
                   overwrite=True,
                   save_format=None,
                   options=None):
    """Saves all layer weights.

    Either saves in HDF5 or in TensorFlow format based on the `save_format`
    argument.

    When saving in HDF5 format, the weight file has:
      - `layer_names` (attribute), a list of strings
          (ordered names of model layers).
      - For every layer, a `group` named `layer.name`
          - For every such layer group, a group attribute `weight_names`,
              a list of strings
              (ordered names of weights tensor of the layer).
          - For every weight in the layer, a dataset
              storing the weight value, named after the weight tensor.

    When saving in TensorFlow format, all objects referenced by the network are
    saved in the same format as `tf.train.Checkpoint`, including any `Layer`
    instances or `Optimizer` instances assigned to object attributes. For
    networks constructed from inputs and outputs using `tf.keras.Model(inputs,
    outputs)`, `Layer` instances used by the network are tracked/saved
    automatically. For user-defined classes which inherit from `tf.keras.Model`,
    `Layer` instances must be assigned to object attributes, typically in the
    constructor. See the documentation of `tf.train.Checkpoint` and
    `tf.keras.Model` for details.

    While the formats are the same, do not mix `save_weights` and
    `tf.train.Checkpoint`. Checkpoints saved by `Model.save_weights` should be
    loaded using `Model.load_weights`. Checkpoints saved using
    `tf.train.Checkpoint.save` should be restored using the corresponding
    `tf.train.Checkpoint.restore`. Prefer `tf.train.Checkpoint` over
    `save_weights` for training checkpoints.

    The TensorFlow format matches objects and variables by starting at a root
    object, `self` for `save_weights`, and greedily matching attribute
    names. For `Model.save` this is the `Model`, and for `Checkpoint.save` this
    is the `Checkpoint` even if the `Checkpoint` has a model attached. This
    means saving a `tf.keras.Model` using `save_weights` and loading into a
    `tf.train.Checkpoint` with a `Model` attached (or vice versa) will not match
    the `Model`'s variables. See the [guide to training
    checkpoints](https://www.tensorflow.org/guide/checkpoint) for details
    on the TensorFlow format.

    Args:
        filepath: String or PathLike, path to the file to save the weights to.
            When saving in TensorFlow format, this is the prefix used for
            checkpoint files (multiple files are generated). Note that the '.h5'
            suffix causes weights to be saved in HDF5 format.
        overwrite: Whether to silently overwrite any existing file at the
            target location, or provide the user with a manual prompt.
        save_format: Either 'tf' or 'h5'. A `filepath` ending in '.h5' or
            '.keras' will default to HDF5 if `save_format` is `None`. Otherwise
            `None` defaults to 'tf'.
        options: Optional `tf.train.CheckpointOptions` object that specifies
            options for saving weights.

    Raises:
        ImportError: If h5py is not available when attempting to save in HDF5
            format.
        ValueError: For invalid/unknown format arguments.
    """
    self._assert_weights_created()
    filepath = path_to_string(filepath)
    filepath_is_h5 = saving_utils.is_hdf5_filepath(filepath)
    if save_format is None:
      if filepath_is_h5:
        save_format = 'h5'
      else:
        save_format = 'tf'
    else:
      user_format = save_format.lower().strip()
      if user_format in ('tensorflow', 'tf'):
        save_format = 'tf'
      elif user_format in ('hdf5', 'h5', 'keras'):
        save_format = 'h5'
      else:
        raise ValueError(
            'Unknown format "%s". Was expecting one of {"tf", "h5"}.' % (
                save_format,))
    if save_format == 'tf' and filepath_is_h5:
      raise ValueError(
          ('save_weights got save_format="tf"/"tensorflow", but the '
           'filepath ("%s") looks like an HDF5 file. Omit the ".h5"/".keras" '
           'when saving in TensorFlow format.')
          % filepath)

    if save_format == 'h5' and h5py is None:
      raise ImportError(
          '`save_weights` requires h5py when saving in hdf5.')
    if save_format == 'tf':
      check_filepath = filepath + '.index'
    else:
      check_filepath = filepath
    # If file exists and should not be overwritten:
    if not overwrite and os.path.isfile(check_filepath):
      proceed = ask_to_proceed_with_overwrite(check_filepath)
      if not proceed:
        return
    if save_format == 'h5':
      with h5py.File(filepath, 'w') as f:
        hdf5_format.save_weights_to_hdf5_group(f, self.layers)
    else:
      if tf.executing_eagerly():
        session = None
      else:
        session = backend.get_session()
      self._trackable_saver.save(filepath, session=session, options=options)
      # Record this checkpoint so it's visible from tf.train.latest_checkpoint.
      tf.__internal__.train.update_checkpoint_state(
          save_dir=os.path.dirname(filepath),
          model_checkpoint_path=filepath,
          save_relative_paths=True,
          all_model_checkpoint_paths=[filepath])

  def load_weights(self,
                   filepath,
                   by_name=False,
                   skip_mismatch=False,
                   options=None):
    """Loads all layer weights, either from a TensorFlow or an HDF5 weight file.

    If `by_name` is False weights are loaded based on the network's
    topology. This means the architecture should be the same as when the weights
    were saved.  Note that layers that don't have weights are not taken into
    account in the topological ordering, so adding or removing layers is fine as
    long as they don't have weights.

    If `by_name` is True, weights are loaded into layers only if they share the
    same name. This is useful for fine-tuning or transfer-learning models where
    some of the layers have changed.

    Only topological loading (`by_name=False`) is supported when loading weights
    from the TensorFlow format. Note that topological loading differs slightly
    between TensorFlow and HDF5 formats for user-defined classes inheriting from
    `tf.keras.Model`: HDF5 loads based on a flattened list of weights, while the
    TensorFlow format loads based on the object-local names of attributes to
    which layers are assigned in the `Model`'s constructor.

    Args:
        filepath: String, path to the weights file to load. For weight files in
            TensorFlow format, this is the file prefix (the same as was passed
            to `save_weights`). This can also be a path to a SavedModel
            saved from `model.save`.
        by_name: Boolean, whether to load weights by name or by topological
            order. Only topological loading is supported for weight files in
            TensorFlow format.
        skip_mismatch: Boolean, whether to skip loading of layers where there is
            a mismatch in the number of weights, or a mismatch in the shape of
            the weight (only valid when `by_name=True`).
        options: Optional `tf.train.CheckpointOptions` object that specifies
            options for loading weights.

    Returns:
        When loading a weight file in TensorFlow format, returns the same status
        object as `tf.train.Checkpoint.restore`. When graph building, restore
        ops are run automatically as soon as the network is built (on first call
        for user-defined classes inheriting from `Model`, immediately if it is
        already built).

        When loading weights in HDF5 format, returns `None`.

    Raises:
        ImportError: If h5py is not available and the weight file is in HDF5
            format.
        ValueError: If `skip_mismatch` is set to `True` when `by_name` is
          `False`.
    """
    if backend.is_tpu_strategy(self._distribution_strategy):
      if (self._distribution_strategy.extended.steps_per_run > 1 and
          (not saving_utils.is_hdf5_filepath(filepath))):
        raise ValueError('Load weights is not yet supported with TPUStrategy '
                         'with steps_per_run greater than 1.')
    if skip_mismatch and not by_name:
      raise ValueError(
          'When calling model.load_weights, skip_mismatch can only be set to '
          'True when by_name is True.')

    filepath, save_format = _detect_save_format(filepath)
    if save_format == 'tf':
      status = self._trackable_saver.restore(filepath, options)
      if by_name:
        raise NotImplementedError(
            'Weights may only be loaded based on topology into Models when '
            'loading TensorFlow-formatted weights (got by_name=True to '
            'load_weights).')
      if not tf.executing_eagerly():
        session = backend.get_session()
        # Restore existing variables (if any) immediately, and set up a
        # streaming restore for any variables created in the future.
        tf.__internal__.tracking.streaming_restore(status=status, session=session)
      status.assert_nontrivial_match()
    else:
      status = None
      if h5py is None:
        raise ImportError(
            '`load_weights` requires h5py when loading weights from HDF5.')
      if not self._is_graph_network and not self.built:
        raise ValueError(
            'Unable to load weights saved in HDF5 format into a subclassed '
            'Model which has not created its variables yet. Call the Model '
            'first, then load the weights.')
      self._assert_weights_created()
      with h5py.File(filepath, 'r') as f:
        if 'layer_names' not in f.attrs and 'model_weights' in f:
          f = f['model_weights']
        if by_name:
          hdf5_format.load_weights_from_hdf5_group_by_name(
              f, self.layers, skip_mismatch=skip_mismatch)
        else:
          hdf5_format.load_weights_from_hdf5_group(f, self.layers)

    # Perform any layer defined finalization of the layer state.
    for layer in self.layers:
      layer.finalize_state()
    return status

  def _updated_config(self):
    """Util shared between different serialization methods.

    Returns:
        Model config with Keras version information added.
    """
    from keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top

    config = self.get_config()
    model_config = {
        'class_name': self.__class__.__name__,
        'config': config,
        'keras_version': keras_version,
        'backend': backend.backend()
    }
    return model_config

  def get_config(self):
    raise NotImplementedError

  @classmethod
  def from_config(cls, config, custom_objects=None):
    # `from_config` assumes `cls` is either `Functional` or a child class of
    # `Functional`. In the case that `cls` is meant to behave like a child class
    # of `Functional` but only inherits from the `Model` class, we have to call
    # `cls(...)` instead of `Functional.from_config`.
    from keras.engine import functional  # pylint: disable=g-import-not-at-top
    with generic_utils.SharedObjectLoadingScope():
      input_tensors, output_tensors, created_layers = (
          functional.reconstruct_from_config(config, custom_objects))
      # Initialize a model belonging to `cls`, which can be user-defined or
      # `Functional`.
      model = cls(inputs=input_tensors, outputs=output_tensors,
                  name=config.get('name'))
      functional.connect_ancillary_layers(model, created_layers)
      return model

  def to_json(self, **kwargs):
    """Returns a JSON string containing the network configuration.

    To load a network from a JSON save file, use
    `keras.models.model_from_json(json_string, custom_objects={})`.

    Args:
        **kwargs: Additional keyword arguments
            to be passed to `json.dumps()`.

    Returns:
        A JSON string.
    """
    model_config = self._updated_config()
    return json.dumps(
        model_config, default=json_utils.get_json_type, **kwargs)

  def to_yaml(self, **kwargs):
    """Returns a yaml string containing the network configuration.

    Note: Since TF 2.6, this method is no longer supported and will raise a
    RuntimeError.

    To load a network from a yaml save file, use
    `keras.models.model_from_yaml(yaml_string, custom_objects={})`.

    `custom_objects` should be a dictionary mapping
    the names of custom losses / layers / etc to the corresponding
    functions / classes.

    Args:
        **kwargs: Additional keyword arguments
            to be passed to `yaml.dump()`.

    Returns:
        A YAML string.

    Raises:
        RuntimeError: announces that the method poses a security risk
          (Use the safer `safe_load` function instead of `unsafe_load` when possible)
    """
    raise RuntimeError(
        'Method `model.to_yaml()` has been removed due to security risk of '
        'arbitrary code execution. Please use `model.to_json()` instead.'
    )

  def reset_states(self):
    for layer in self.layers:
      if hasattr(layer, 'reset_states') and getattr(layer, 'stateful', False):
        layer.reset_states()

  @property
  @doc_controls.do_not_generate_docs
  def state_updates(self):
    """Deprecated, do NOT use!

    Returns the `updates` from all layers that are stateful.

    This is useful for separating training updates and
    state updates, e.g. when we need to update a layer's internal state
    during prediction.

    Returns:
        A list of update ops.
    """
    warnings.warn('`Model.state_updates` will be removed in a future version. '
                  'This property should not be used in TensorFlow 2.0, '
                  'as `updates` are applied automatically.')
    state_updates = []
    for layer in self.layers:
      if getattr(layer, 'stateful', False):
        if hasattr(layer, 'updates'):
          state_updates += layer.updates
    return state_updates

  @property
  def weights(self):
    """Returns the list of all layer variables/weights.

    Note: This will not track the weights of nested `tf.Modules` that are not
    themselves Keras layers.

    Returns:
      A list of variables.
    """
    return self._dedup_weights(self._undeduplicated_weights)

  @property
  def _undeduplicated_weights(self):
    """Returns the undeduplicated list of all layer variables/weights."""
    self._assert_weights_created()
    weights = []
    for layer in self._self_tracked_trackables:
      weights += layer.variables
    weights += (self._trainable_weights + self._non_trainable_weights)
    return weights

  def summary(self, line_length=None, positions=None, print_fn=None):
    """Prints a string summary of the network.

    Args:
        line_length: Total length of printed lines
            (e.g. set this to adapt the display to different
            terminal window sizes).
        positions: Relative or absolute positions of log elements
            in each line. If not provided,
            defaults to `[.33, .55, .67, 1.]`.
        print_fn: Print function to use. Defaults to `print`.
            It will be called on each line of the summary.
            You can set it to a custom function
            in order to capture the string summary.

    Raises:
        ValueError: if `summary()` is called before the model is built.
    """
    if not self.built:
      raise ValueError('This model has not yet been built. '
                       'Build the model first by calling `build()` or calling '
                       '`fit()` with some data, or specify '
                       'an `input_shape` argument in the first layer(s) for '
                       'automatic build.')
    layer_utils.print_summary(self,
                              line_length=line_length,
                              positions=positions,
                              print_fn=print_fn)

  @property
  def layers(self):
    return list(self._flatten_layers(include_self=False, recursive=False))

  def get_layer(self, name=None, index=None):
    """Retrieves a layer based on either its name (unique) or index.

    If `name` and `index` are both provided, `index` will take precedence.
    Indices are based on order of horizontal graph traversal (bottom-up).

    Args:
        name: String, name of layer.
        index: Integer, index of layer.

    Returns:
        A layer instance.

    Raises:
        ValueError: In case of invalid layer name or index.
    """
    # TODO(fchollet): We could build a dictionary based on layer names
    # since they are constant, but we have not done that yet.
    if index is not None and name is not None:
      raise ValueError('Provide only a layer name or a layer index.')

    if index is not None:
      if len(self.layers) <= index:
        raise ValueError('Was asked to retrieve layer at index ' + str(index) +
                         ' but model only has ' + str(len(self.layers)) +
                         ' layers.')
      else:
        return self.layers[index]

    if name is not None:
      for layer in self.layers:
        if layer.name == name:
          return layer
      raise ValueError('No such layer: ' + name + '.')
    raise ValueError('Provide either a layer name or layer index.')

  @tf.__internal__.tracking.no_automatic_dependency_tracking
  def _set_save_spec(self, inputs, args=None, kwargs=None):
    """Defines the save spec so that serialization is able to trace model call.

    The TensorSpecs of the call function `inputs`, `args`, and `kwargs` are
    saved into a tuple of `([inputs] + args, kwargs)`. The input `TensorSpec`
    names are updated to match the built `input_names`.

    The specs can be retrieved with the `save_spec` property.

    Args:
      inputs: possibly nested inputs passed into the call function.
      args: a list of positional arguments passed into call.
      kwargs: a dictionary of keyword arguments passed into call.
    """
    if self._saved_model_inputs_spec is not None:
      return  # Already set.
    args = args or []
    kwargs = kwargs or {}

    input_names = self.input_names
    if not input_names:
      input_names = compile_utils.create_pseudo_input_names(inputs)

    flat_inputs = tf.nest.flatten(inputs)
    inputs_spec = []
    for name, tensor in zip(input_names, flat_inputs):
      inputs_spec.append(
          tf_utils.get_tensor_spec(tensor, dynamic_batch=False, name=name))
    inputs_spec = tf.nest.pack_sequence_as(inputs, inputs_spec)
    super(Model, self)._set_save_spec(inputs_spec, args, kwargs)

    # Store the input shapes
    if (self.__class__.__name__ == 'Sequential' and
        self._build_input_shape is None):
      self._build_input_shape = tf.nest.map_structure(
          lambda x: None if x is None else x.shape, inputs_spec)

  def save_spec(self, dynamic_batch=True):
    """Returns the `tf.TensorSpec` of call inputs as a tuple `(args, kwargs)`.

    This value is automatically defined after calling the model for the first
    time. Afterwards, you can use it when exporting the model for serving:

    ```python
    model = tf.keras.Model(...)

    @tf.function
    def serve(*args, **kwargs):
      outputs = model(*args, **kwargs)
      # Apply postprocessing steps, or add additional outputs.
      ...
      return outputs

    # arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this example, is
    # an empty dict since functional models do not use keyword arguments.
    arg_specs, kwarg_specs = model.save_spec()

    model.save(path, signatures={
      'serving_default': serve.get_concrete_function(*arg_specs, **kwarg_specs)
    })
    ```

    Args:
      dynamic_batch: Whether to set the batch sizes of all the returned
        `tf.TensorSpec` to `None`. (Note that when defining functional or
        Sequential models with `tf.keras.Input([...], batch_size=X)`, the
        batch size will always be preserved). Defaults to `True`.
    Returns:
      If the model inputs are defined, returns a tuple `(args, kwargs)`. All
      elements in `args` and `kwargs` are `tf.TensorSpec`.
      If the model inputs are not defined, returns `None`.
      The model inputs are automatically set when calling the model,
      `model.fit`, `model.evaluate` or `model.predict`.
    """
    return self._get_save_spec(dynamic_batch, inputs_only=False)

  def _assert_weights_created(self):
    """Asserts that all the weights for the model have been created.

    For a non-dynamic model, the weights must already be created after the
    layer has been called. For a dynamic model, the exact list of weights can
    never be known for certain since it may change at any time during execution.

    We run this check right before accessing weights or getting the Numpy value
    for the current weights. Otherwise, if the layer has never been called,
    the user would just get an empty list, which is misleading.

    Raises:
      ValueError: if the weights of the network has not yet been created.
    """
    if self.dynamic:
      return

    if ('build' in self.__class__.__dict__ and
        self.__class__ != Model and
        not self.built):
      # For any model that has customized build() method but hasn't
      # been invoked yet, this will cover both sequential and subclass model.
      # Also make sure to exclude Model class itself which has build() defined.
      raise ValueError('Weights for model %s have not yet been created. '
                       'Weights are created when the Model is first called on '
                       'inputs or `build()` is called with an `input_shape`.' %
                       self.name)

  def _check_call_args(self, method_name):
    """Check that `call` has only one positional arg."""
    # Always allow first arg, regardless of arg name.
    fullargspec = self._call_full_argspec
    if fullargspec.defaults:
      positional_args = fullargspec.args[:-len(fullargspec.defaults)]
    else:
      positional_args = fullargspec.args
    if 'training' in positional_args:
      positional_args.remove('training')

    # self and first arg can be positional.
    if len(positional_args) > 2:
      extra_args = positional_args[2:]
      raise ValueError(
          'Models passed to `' + method_name + '` can only have `training` '
          'and the first argument in `call` as positional arguments, '
          'found: ' + str(extra_args) + '.')

  def _validate_compile(self, optimizer, metrics, **kwargs):
    """Performs validation checks for the default `compile`."""
    if any(
        isinstance(opt, optimizer_v1.Optimizer)
        for opt in tf.nest.flatten(optimizer)):
      raise ValueError(
          '`tf.compat.v1.keras` Optimizer (', optimizer, ') is '
          'not supported when eager execution is enabled. Use a '
          '`tf.keras` Optimizer instead, or disable eager '
          'execution.')

    kwargs.pop('cloning', None)  # Legacy DistStrat argument, never used.
    kwargs.pop('experimental_run_tf_function', None)  # Always `True`.
    if kwargs.pop('distribute', None) is not None:
      raise ValueError(
          'Distribute argument in compile is not available in TF 2.0 please '
          'create the model under the distribution strategy scope.')
    if kwargs.pop('target_tensors', None) is not None:
      raise ValueError(
          'target_tensors argument is not supported when executing eagerly.')
    invalid_kwargs = set(kwargs) - {'sample_weight_mode'}
    if invalid_kwargs:
      raise TypeError('Invalid keyword argument(s) in `compile`: %s' %
                      (invalid_kwargs,))

    # Model must be created and compiled with the same DistStrat.
    if self.built and tf.distribute.has_strategy():
      strategy = tf.distribute.get_strategy()
      for v in self.variables:
        if not strategy.extended.variable_created_in_scope(v):
          raise ValueError(
              'Variable (%s) was not created in the distribution strategy '
              'scope of (%s). It is most likely due to not all layers or '
              'the model or optimizer being created outside the distribution '
              'strategy scope. Try to make sure your code looks similar '
              'to the following.\n'
              'with strategy.scope():\n'
              '  model=_create_model()\n'
              '  model.compile(...)' % (v, strategy))

    # Model metrics must be created in the same distribution strategy scope
    # as the model.
    strategy = self.distribute_strategy
    for metric in tf.nest.flatten(metrics):
      for v in getattr(metric, 'variables', []):
        if not strategy.extended.variable_created_in_scope(v):
          raise ValueError(
              'Metric (%s) passed to model.compile was created inside of a '
              'different distribution strategy scope than the model. All '
              'metrics must be created in the same distribution strategy '
              'scope as the model (in this case %s). If you pass in a string '
              'identifier for a metric to compile the metric will '
              'automatically be created in the correct distribution '
              'strategy scope.' % (metric, strategy)
          )

    # Model metrics must be created in the same distribution strategy scope
    # as the model.
    for opt in tf.nest.flatten(optimizer):
      for v in getattr(opt, '_weights', []):
        if not strategy.extended.variable_created_in_scope(v):
          raise ValueError(
              'Optimizer (%s) passed to model.compile was created inside of a '
              'different distribution strategy scope than the model. All '
              'optimizers must be created in the same distribution strategy '
              'scope as the model (in this case %s). If you pass in a string '
              'identifier for an optimizer to compile the optimizer will '
              'automatically be created in the correct distribution '
              'strategy scope.' % (opt, strategy))

  def _maybe_load_initial_epoch_from_ckpt(self, initial_epoch):
    """Maybe load initial epoch from ckpt considering possible worker recovery.

    Refer to tensorflow/python/keras/distribute/worker_training_state.py
    for more information.

    Args:
      initial_epoch: The original initial_epoch user passes in in `fit()`.

    Returns:
      If the training is recovering from previous failure under multi-worker
      training setting, return the epoch the training is supposed to continue
      at. Otherwise, return the `initial_epoch` the user passes in.
    """
    if self._training_state is not None:
      return self._training_state.maybe_load_initial_epoch_from_ckpt(
          initial_epoch, mode=ModeKeys.TRAIN)
    return initial_epoch

  def _assert_compile_was_called(self):
    # Checks whether `compile` has been called. If it has been called,
    # then the optimizer is set. This is different from whether the
    # model is compiled
    # (i.e. whether the model is built and its inputs/outputs are set).
    if not self._is_compiled:
      raise RuntimeError('You must compile your model before '
                         'training/testing. '
                         'Use `model.compile(optimizer, loss)`.')

  def _set_inputs(self, inputs, outputs=None, training=None):
    """This method is for compat with Modelv1. Only inputs are needed here."""
    self._set_save_spec(inputs)

  @property
  def _trackable_saved_model_saver(self):
    return model_serialization.ModelSavedModelSaver(self)

  def _list_functions_for_serialization(self, serialization_cache):
    # SavedModel needs to ignore the execution functions.
    train_function = self.train_function
    test_function = self.test_function
    predict_function = self.predict_function
    train_tf_function = self.train_tf_function
    self.train_function = None
    self.test_function = None
    self.predict_function = None
    self.train_tf_function = None
    functions = super(
        Model, self)._list_functions_for_serialization(serialization_cache)
    self.train_function = train_function
    self.test_function = test_function
    self.predict_function = predict_function
    self.train_tf_function = train_tf_function
    return functions

  def _should_eval(self, epoch, validation_freq):
    epoch = epoch + 1  # one-index the user-facing epoch.
    if isinstance(validation_freq, int):
      return epoch % validation_freq == 0
    elif isinstance(validation_freq, list):
      return epoch in validation_freq
    else:
      raise ValueError('Expected `validation_freq` to be a list or int.')

  ######################################################################
  # Functions below exist only as v1 / v2 compatibility shims.
  ######################################################################

  def _get_compile_args(self, user_metrics=True):
    """Used for saving or cloning a Model.

    Args:
      user_metrics: Whether to return user-supplied metrics or `Metric` objects.
        Defaults to returning the user-supplied metrics.

    Returns:
      Dictionary of arguments that were used when compiling the model.
    """
    self._assert_compile_was_called()
    # pylint: disable=protected-access

    saved_metrics = self.compiled_metrics._user_metrics
    saved_weighted_metrics = self.compiled_metrics._user_weighted_metrics

    if not user_metrics:
      if saved_metrics is not None:
        saved_metrics = self.compiled_metrics._metrics
      if saved_weighted_metrics is not None:
        saved_weighted_metrics = self.compiled_metrics._weighted_metrics

    compile_args = {
        'optimizer': self.optimizer,
        'loss': self.compiled_loss._user_losses,
        'metrics': saved_metrics,
        'weighted_metrics': saved_weighted_metrics,
        'loss_weights': self.compiled_loss._user_loss_weights,
    }
    # pylint: enable=protected-access
    return compile_args

  def _get_callback_model(self):
    return self

  def _in_multi_worker_mode(self):
    return self.distribute_strategy.extended._in_multi_worker_mode()  # pylint: disable=protected-access

  @property
  def _compile_was_called(self):
    return self._is_compiled

Ancestors

Subclasses

Instance variables

var distribute_strategy

The tf.distribute.Strategy this model was created under.

Expand source code
@property
def distribute_strategy(self):
  """The `tf.distribute.Strategy` this model was created under."""
  return self._distribution_strategy or tf.distribute.get_strategy()
var layers
Expand source code
@property
def layers(self):
  return list(self._flatten_layers(include_self=False, recursive=False))
var metrics

Returns the model's metrics added using compile, add_metric APIs.

Note: Metrics passed to compile() are available only after a keras.Model has been trained/evaluated on actual data.

Examples:

>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> outputs = tf.keras.layers.Dense(2)(inputs)
>>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
>>> [m.name for m in model.metrics]
[]
>>> x = np.random.random((2, 3))
>>> y = np.random.randint(0, 2, (2, 2))
>>> model.fit(x, y)
>>> [m.name for m in model.metrics]
['loss', 'mae']
>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2, name='out')
>>> output_1 = d(inputs)
>>> output_2 = d(inputs)
>>> model = tf.keras.models.Model(
...    inputs=inputs, outputs=[output_1, output_2])
>>> model.add_metric(
...    tf.reduce_sum(output_2), name='mean', aggregation='mean')
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
>>> model.fit(x, (y, y))
>>> [m.name for m in model.metrics]
['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
'out_1_acc', 'mean']
Expand source code
@property
def metrics(self):
  """Returns the model's metrics added using `compile`, `add_metric` APIs.

  Note: Metrics passed to `compile()` are available only after a `keras.Model`
  has been trained/evaluated on actual data.

  Examples:

  >>> inputs = tf.keras.layers.Input(shape=(3,))
  >>> outputs = tf.keras.layers.Dense(2)(inputs)
  >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
  >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
  >>> [m.name for m in model.metrics]
  []

  >>> x = np.random.random((2, 3))
  >>> y = np.random.randint(0, 2, (2, 2))
  >>> model.fit(x, y)
  >>> [m.name for m in model.metrics]
  ['loss', 'mae']

  >>> inputs = tf.keras.layers.Input(shape=(3,))
  >>> d = tf.keras.layers.Dense(2, name='out')
  >>> output_1 = d(inputs)
  >>> output_2 = d(inputs)
  >>> model = tf.keras.models.Model(
  ...    inputs=inputs, outputs=[output_1, output_2])
  >>> model.add_metric(
  ...    tf.reduce_sum(output_2), name='mean', aggregation='mean')
  >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
  >>> model.fit(x, (y, y))
  >>> [m.name for m in model.metrics]
  ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
  'out_1_acc', 'mean']

  """
  metrics = []
  if self._is_compiled:
    # TODO(omalleyt): Track `LossesContainer` and `MetricsContainer` objects
    # so that attr names are not load-bearing.
    if self.compiled_loss is not None:
      metrics += self.compiled_loss.metrics
    if self.compiled_metrics is not None:
      metrics += self.compiled_metrics.metrics

  for l in self._flatten_layers():
    metrics.extend(l._metrics)  # pylint: disable=protected-access
  return metrics
var metrics_names

Returns the model's display labels for all outputs.

Note: metrics_names are available only after a keras.Model has been trained/evaluated on actual data.

Examples:

>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> outputs = tf.keras.layers.Dense(2)(inputs)
>>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
>>> model.metrics_names
[]
>>> x = np.random.random((2, 3))
>>> y = np.random.randint(0, 2, (2, 2))
>>> model.fit(x, y)
>>> model.metrics_names
['loss', 'mae']
>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2, name='out')
>>> output_1 = d(inputs)
>>> output_2 = d(inputs)
>>> model = tf.keras.models.Model(
...    inputs=inputs, outputs=[output_1, output_2])
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
>>> model.fit(x, (y, y))
>>> model.metrics_names
['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
'out_1_acc']
Expand source code
@property
def metrics_names(self):
  """Returns the model's display labels for all outputs.

  Note: `metrics_names` are available only after a `keras.Model` has been
  trained/evaluated on actual data.

  Examples:

  >>> inputs = tf.keras.layers.Input(shape=(3,))
  >>> outputs = tf.keras.layers.Dense(2)(inputs)
  >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
  >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
  >>> model.metrics_names
  []

  >>> x = np.random.random((2, 3))
  >>> y = np.random.randint(0, 2, (2, 2))
  >>> model.fit(x, y)
  >>> model.metrics_names
  ['loss', 'mae']

  >>> inputs = tf.keras.layers.Input(shape=(3,))
  >>> d = tf.keras.layers.Dense(2, name='out')
  >>> output_1 = d(inputs)
  >>> output_2 = d(inputs)
  >>> model = tf.keras.models.Model(
  ...    inputs=inputs, outputs=[output_1, output_2])
  >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
  >>> model.fit(x, (y, y))
  >>> model.metrics_names
  ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
  'out_1_acc']

  """

  # This property includes all output names including `loss` and per-output
  # losses for backward compatibility.
  return [m.name for m in self.metrics]
var run_eagerly

Settable attribute indicating whether the model should run eagerly.

Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls.

By default, we will attempt to compile your model to a static graph to deliver the best execution performance.

Returns

Boolean, whether the model should run eagerly.

Expand source code
@property
def run_eagerly(self):
  """Settable attribute indicating whether the model should run eagerly.

  Running eagerly means that your model will be run step by step,
  like Python code. Your model might run slower, but it should become easier
  for you to debug it by stepping into individual layer calls.

  By default, we will attempt to compile your model to a static graph to
  deliver the best execution performance.

  Returns:
    Boolean, whether the model should run eagerly.
  """
  if self.dynamic and self._run_eagerly is False:  # pylint:disable=g-bool-id-comparison
    # TODO(fchollet): consider using py_func to enable this.
    raise ValueError('Your model contains layers that can only be '
                     'successfully run in eager execution (layers '
                     'constructed with `dynamic=True`). '
                     'You cannot set `run_eagerly=False`.')

  if self._cluster_coordinator and self._run_eagerly:
    raise ValueError('When using `Model` with `ParameterServerStrategy`, '
                     '`run_eagerly` is not supported.')

  # Run eagerly logic, by priority:
  # (1) Dynamic models must be run eagerly.
  # (2) Explicitly setting run_eagerly causes a Model to be run eagerly.
  # (3) Not explicitly setting run_eagerly defaults to TF's global setting.
  return (self.dynamic or self._run_eagerly or
          (tf.config.functions_run_eagerly() and
           self._run_eagerly is None))
var state_updates

Deprecated, do NOT use!

Returns the updates from all layers that are stateful.

This is useful for separating training updates and state updates, e.g. when we need to update a layer's internal state during prediction.

Returns

A list of update ops.

Expand source code
@property
@doc_controls.do_not_generate_docs
def state_updates(self):
  """Deprecated, do NOT use!

  Returns the `updates` from all layers that are stateful.

  This is useful for separating training updates and
  state updates, e.g. when we need to update a layer's internal state
  during prediction.

  Returns:
      A list of update ops.
  """
  warnings.warn('`Model.state_updates` will be removed in a future version. '
                'This property should not be used in TensorFlow 2.0, '
                'as `updates` are applied automatically.')
  state_updates = []
  for layer in self.layers:
    if getattr(layer, 'stateful', False):
      if hasattr(layer, 'updates'):
        state_updates += layer.updates
  return state_updates
var weights

Returns the list of all layer variables/weights.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns

A list of variables.

Expand source code
@property
def weights(self):
  """Returns the list of all layer variables/weights.

  Note: This will not track the weights of nested `tf.Modules` that are not
  themselves Keras layers.

  Returns:
    A list of variables.
  """
  return self._dedup_weights(self._undeduplicated_weights)

Methods

def build(self, input_shape)

Builds the model based on input shapes received.

This is to be used for subclassed models, which do not know at instantiation time what their inputs look like.

This method only exists for users who want to call model.build() in a standalone way (as a substitute for calling the model on real data to build it). It will never be called by the framework (and thus it will never throw unexpected errors in an unrelated workflow).

Args: input_shape: Single tuple, TensorShape, or list/dict of shapes, where shapes are tuples, integers, or TensorShapes.

Raises

ValueError: 1. In case of invalid user-provided data (not of type tuple, list, TensorShape, or dict). 2. If the model requires call arguments that are agnostic to the input shapes (positional or kwarg in call signature). 3. If not all layers were properly built. 4. If float type inputs are not supported within the layers.

In each of these cases, the user should build their model by calling it on real tensor data.

Expand source code
@generic_utils.default
def build(self, input_shape):
  """Builds the model based on input shapes received.

  This is to be used for subclassed models, which do not know at instantiation
  time what their inputs look like.

  This method only exists for users who want to call `model.build()` in a
  standalone way (as a substitute for calling the model on real data to
  build it). It will never be called by the framework (and thus it will
  never throw unexpected errors in an unrelated workflow).

  Args:
   input_shape: Single tuple, TensorShape, or list/dict of shapes, where
       shapes are tuples, integers, or TensorShapes.

  Raises:
    ValueError:
      1. In case of invalid user-provided data (not of type tuple,
         list, TensorShape, or dict).
      2. If the model requires call arguments that are agnostic
         to the input shapes (positional or kwarg in call signature).
      3. If not all layers were properly built.
      4. If float type inputs are not supported within the layers.

    In each of these cases, the user should build their model by calling it
    on real tensor data.
  """
  if self._is_graph_network:
    super(Model, self).build(input_shape)
    return

  if input_shape is None:
    raise ValueError('Input shape must be defined when calling build on a '
                     'model subclass network.')
  valid_types = (tuple, list, tf.TensorShape, dict)
  if not isinstance(input_shape, valid_types):
    raise ValueError('Specified input shape is not one of the valid types. '
                     'Please specify a batch input shape of type tuple or '
                     'list of input shapes. User provided '
                     'input type: {}'.format(type(input_shape)))

  if input_shape and not self.inputs:
    # We create placeholders for the `None`s in the shape and build the model
    # in a Graph. Since tf.Variable is compatible with both eager execution
    # and graph building, the variables created after building the model in
    # a Graph are still valid when executing eagerly.
    if tf.executing_eagerly():
      graph = tf.__internal__.FuncGraph('build_graph')
    else:
      graph = backend.get_graph()
    with graph.as_default():
      if (isinstance(input_shape, list) and
          all(d is None or isinstance(d, int) for d in input_shape)):
        input_shape = tuple(input_shape)
      if isinstance(input_shape, list):
        x = [base_layer_utils.generate_placeholders_from_shape(shape)
             for shape in input_shape]
      elif isinstance(input_shape, dict):
        x = {
            k: base_layer_utils.generate_placeholders_from_shape(shape)
            for k, shape in input_shape.items()
        }
      else:
        x = base_layer_utils.generate_placeholders_from_shape(input_shape)

      kwargs = {}
      call_signature = self._call_full_argspec
      call_args = call_signature.args
      # Exclude `self`, `inputs`, and any argument with a default value.
      if len(call_args) > 2:
        if call_signature.defaults:
          call_args = call_args[2:-len(call_signature.defaults)]
        else:
          call_args = call_args[2:]
        for arg in call_args:
          if arg == 'training':
            # Case where `training` is a positional arg with no default.
            kwargs['training'] = False
          else:
            # Has invalid call signature with unknown positional arguments.
            raise ValueError(
                'Currently, you cannot build your model if it has '
                'positional or keyword arguments that are not '
                'inputs to the model, but are required for its '
                '`call` method. Instead, in order to instantiate '
                'and build your model, `call` your model on real '
                'tensor data with all expected call arguments.')
      elif len(call_args) < 2:
        # Signature without `inputs`.
        raise ValueError('You can only call `build` on a model if its `call` '
                         'method accepts an `inputs` argument.')
      try:
        self.call(x, **kwargs)
      except (tf.errors.InvalidArgumentError, TypeError):
        raise ValueError('You cannot build your model by calling `build` '
                         'if your layers do not support float type inputs. '
                         'Instead, in order to instantiate and build your '
                         'model, `call` your model on real tensor data (of '
                         'the correct dtype).')
  super(Model, self).build(input_shape)
def call(self, inputs, training=None, mask=None)

Calls the model on new inputs.

In this case call just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Note: This method should not be called directly. It is only meant to be overridden when subclassing tf.keras.Model. To call a model on an input, always use the __call__ method, i.e. model(inputs), which relies on the underlying call method.

Args

inputs
Input tensor, or dict/list/tuple of input tensors.
training
Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask
A mask or list of masks. A mask can be either a tensor or None (no mask).

Returns

A tensor if there is a single output, or a list of tensors if there are more than one outputs.

Expand source code
@doc_controls.doc_in_current_and_subclasses
def call(self, inputs, training=None, mask=None):
  """Calls the model on new inputs.

  In this case `call` just reapplies
  all ops in the graph to the new inputs
  (e.g. build a new computational graph from the provided inputs).

  Note: This method should not be called directly. It is only meant to be
  overridden when subclassing `tf.keras.Model`.
  To call a model on an input, always use the `__call__` method,
  i.e. `model(inputs)`, which relies on the underlying `call` method.

  Args:
      inputs: Input tensor, or dict/list/tuple of input tensors.
      training: Boolean or boolean scalar tensor, indicating whether to run
        the `Network` in training mode or inference mode.
      mask: A mask or list of masks. A mask can be
          either a tensor or None (no mask).

  Returns:
      A tensor if there is a single output, or
      a list of tensors if there are more than one outputs.
  """
  raise NotImplementedError('When subclassing the `Model` class, you should '
                            'implement a `call` method.')
def compile(self, optimizer='rmsprop', loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, steps_per_execution=None, **kwargs)

Configures the model for training.

Example:

model.compile(optimizer=tf.keras.optimizer.Adam(learning_rate=1e-3),
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=[tf.keras.metrics.BinaryAccuracy(),
                       tf.keras.metrics.FalseNegatives()])

Args

optimizer
String (name of optimizer) or optimizer instance. See tf.keras.optimizers.
loss
Loss function. Maybe be a string (name of loss function), or a tf.keras.losses.Loss instance. See tf.keras.losses. A loss function is any callable with the signature loss = fn(y_true, y_pred)<code>, where </code>y_true are the ground truth values, and y_pred are the model's predictions. y_true should have shape (batch_size, d0, .. dN) (except in the case of sparse loss functions such as sparse categorical crossentropy which expects integer arrays of shape (batch_size, d0, .. dN-1)). y_pred should have shape (batch_size, d0, .. dN). The loss function should return a float tensor. If a custom Loss instance is used and reduction is set to None, return value has shape (batch_size, d0, .. dN-1) i.e. per-sample or per-timestep loss values; otherwise, it is a scalar. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses, unless loss_weights is specified.
metrics
List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a built-in function), function or a tf.keras.metrics.Metric instance. See tf.keras.metrics. Typically you will use metrics=['accuracy']. A function is any callable with the signature result = fn(y_true, y_pred). To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}. You can also pass a list to specify a metric or a list of metrics for each output, such as metrics=[['accuracy'], ['accuracy', 'mse']] or metrics=['accuracy', ['accuracy', 'mse']]. When you pass the strings 'accuracy' or 'acc', we convert this to one of tf.keras.metrics.BinaryAccuracy, tf.keras.metrics.CategoricalAccuracy, tf.keras.metrics.SparseCategoricalAccuracy based on the loss function used and the model output shape. We do a similar conversion for the strings 'crossentropy' and 'ce' as well.
loss_weights
Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses, weighted by the loss_weights coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a dict, it is expected to map output names (strings) to scalar coefficients.
weighted_metrics
List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.
run_eagerly
Bool. Defaults to False. If True, this Model's logic will not be wrapped in a tf.function. Recommended to leave this as None unless your Model cannot be run inside a tf.function. run_eagerly=True is not supported when using tf.distribute.experimental.ParameterServerStrategy.
steps_per_execution
Int. Defaults to 1. The number of batches to run during each tf.function call. Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead. At most, one full epoch will be run each execution. If a number larger than the size of the epoch is passed, the execution will be truncated to the size of the epoch. Note that if steps_per_execution is set to N, Callback.on_batch_begin and Callback.on_batch_end methods will only be called every N batches (i.e. before/after each tf.function execution).
**kwargs
Arguments supported for backwards compatibility only.

Raises

ValueError
In case of invalid arguments for optimizer, loss or keras.api.keras.metrics.
Expand source code
def compile(self,
            optimizer='rmsprop',
            loss=None,
            metrics=None,
            loss_weights=None,
            weighted_metrics=None,
            run_eagerly=None,
            steps_per_execution=None,
            **kwargs):
  """Configures the model for training.

  Example:

  ```python
  model.compile(optimizer=tf.keras.optimizer.Adam(learning_rate=1e-3),
                loss=tf.keras.losses.BinaryCrossentropy(),
                metrics=[tf.keras.metrics.BinaryAccuracy(),
                         tf.keras.metrics.FalseNegatives()])
  ```

  Args:
      optimizer: String (name of optimizer) or optimizer instance. See
        `tf.keras.optimizers`.
      loss: Loss function. Maybe be a string (name of loss function), or
        a `tf.keras.losses.Loss` instance. See `tf.keras.losses`. A loss
        function is any callable with the signature `loss = fn(y_true,
        y_pred)`, where `y_true` are the ground truth values, and
        `y_pred` are the model's predictions.
        `y_true` should have shape
        `(batch_size, d0, .. dN)` (except in the case of
        sparse loss functions such as
        sparse categorical crossentropy which expects integer arrays of shape
        `(batch_size, d0, .. dN-1)`).
        `y_pred` should have shape `(batch_size, d0, .. dN)`.
        The loss function should return a float tensor.
        If a custom `Loss` instance is
        used and reduction is set to `None`, return value has shape
        `(batch_size, d0, .. dN-1)` i.e. per-sample or per-timestep loss
        values; otherwise, it is a scalar. If the model has multiple outputs,
        you can use a different loss on each output by passing a dictionary
        or a list of losses. The loss value that will be minimized by the
        model will then be the sum of all individual losses, unless
        `loss_weights` is specified.
      metrics: List of metrics to be evaluated by the model during training
        and testing. Each of this can be a string (name of a built-in
        function), function or a `tf.keras.metrics.Metric` instance. See
        `tf.keras.metrics`. Typically you will use `metrics=['accuracy']`. A
        function is any callable with the signature `result = fn(y_true,
        y_pred)`. To specify different metrics for different outputs of a
        multi-output model, you could also pass a dictionary, such as
        `metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}`.
        You can also pass a list to specify a metric or a list of metrics
        for each output, such as `metrics=[['accuracy'], ['accuracy', 'mse']]`
        or `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the
        strings 'accuracy' or 'acc', we convert this to one of
        `tf.keras.metrics.BinaryAccuracy`,
        `tf.keras.metrics.CategoricalAccuracy`,
        `tf.keras.metrics.SparseCategoricalAccuracy` based on the loss
        function used and the model output shape. We do a similar
        conversion for the strings 'crossentropy' and 'ce' as well.
      loss_weights: Optional list or dictionary specifying scalar coefficients
        (Python floats) to weight the loss contributions of different model
        outputs. The loss value that will be minimized by the model will then
        be the *weighted sum* of all individual losses, weighted by the
        `loss_weights` coefficients.
          If a list, it is expected to have a 1:1 mapping to the model's
            outputs. If a dict, it is expected to map output names (strings)
            to scalar coefficients.
      weighted_metrics: List of metrics to be evaluated and weighted by
        `sample_weight` or `class_weight` during training and testing.
      run_eagerly: Bool. Defaults to `False`. If `True`, this `Model`'s
        logic will not be wrapped in a `tf.function`. Recommended to leave
        this as `None` unless your `Model` cannot be run inside a
        `tf.function`. `run_eagerly=True` is not supported when using
        `tf.distribute.experimental.ParameterServerStrategy`.
      steps_per_execution: Int. Defaults to 1. The number of batches to
        run during each `tf.function` call. Running multiple batches
        inside a single `tf.function` call can greatly improve performance
        on TPUs or small models with a large Python overhead.
        At most, one full epoch will be run each
        execution. If a number larger than the size of the epoch is passed,
        the execution will be truncated to the size of the epoch.
        Note that if `steps_per_execution` is set to `N`,
        `Callback.on_batch_begin` and `Callback.on_batch_end` methods
        will only be called every `N` batches
        (i.e. before/after each `tf.function` execution).
      **kwargs: Arguments supported for backwards compatibility only.

  Raises:
      ValueError: In case of invalid arguments for
          `optimizer`, `loss` or `metrics`.
  """
  base_layer.keras_api_gauge.get_cell('compile').set(True)
  with self.distribute_strategy.scope():
    if 'experimental_steps_per_execution' in kwargs:
      logging.warning('The argument `steps_per_execution` is no longer '
                      'experimental. Pass `steps_per_execution` instead of '
                      '`experimental_steps_per_execution`.')
      if not steps_per_execution:
        steps_per_execution = kwargs.pop('experimental_steps_per_execution')

    # When compiling from an already-serialized model, we do not want to
    # reapply some processing steps (e.g. metric renaming for multi-output
    # models, which have prefixes added for each corresponding output name).
    from_serialized = kwargs.pop('from_serialized', False)

    self._validate_compile(optimizer, metrics, **kwargs)
    self._run_eagerly = run_eagerly

    self.optimizer = self._get_optimizer(optimizer)
    self.compiled_loss = compile_utils.LossesContainer(
        loss, loss_weights, output_names=self.output_names)
    self.compiled_metrics = compile_utils.MetricsContainer(
        metrics, weighted_metrics, output_names=self.output_names,
        from_serialized=from_serialized)

    self._configure_steps_per_execution(steps_per_execution or 1)

    # Initializes attrs that are reset each time `compile` is called.
    self._reset_compile_cache()
    self._is_compiled = True

    self.loss = loss or {}  # Backwards compat.
def evaluate(self, x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, return_dict=False, **kwargs)

Returns the loss value & metrics values for the model in test mode.

Computation is done in batches (see the batch_size arg.)

Args

x
Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A tf.data dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights). - A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample_weights). A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the Unpacking behavior for iterator-like inputs<code> section of </code>Model.fit.
y
Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). If x is a dataset, generator or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from the iterator/dataset).
batch_size
Integer or None. Number of samples per batch of computation. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of a dataset, generators, or keras.utils.Sequence instances (since they generate batches).
verbose
0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
sample_weight
Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. This argument is not supported when x is a dataset, instead pass sample weights as the third element of x.
steps
Integer or None. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of None. If x is a tf.data dataset and steps is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs.
callbacks
List of Callback instances. List of callbacks to apply during evaluation. See callbacks.
max_queue_size
Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
workers
Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.
use_multiprocessing
Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.
return_dict
If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.
**kwargs
Unused at this time.

See the discussion of Unpacking behavior for iterator-like inputs for Model.fit().

Model.evaluate() is not yet supported with tf.distribute.experimental.ParameterServerStrategy.

Returns

Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

Raises

RuntimeError
If model.evaluate is wrapped in tf.function.
ValueError
in case of invalid arguments.
Expand source code
def evaluate(self,
             x=None,
             y=None,
             batch_size=None,
             verbose=1,
             sample_weight=None,
             steps=None,
             callbacks=None,
             max_queue_size=10,
             workers=1,
             use_multiprocessing=False,
             return_dict=False,
             **kwargs):
  """Returns the loss value & metrics values for the model in test mode.

  Computation is done in batches (see the `batch_size` arg.)

  Args:
      x: Input data. It could be:
        - A Numpy array (or array-like), or a list of arrays
          (in case the model has multiple inputs).
        - A TensorFlow tensor, or a list of tensors
          (in case the model has multiple inputs).
        - A dict mapping input names to the corresponding array/tensors,
          if the model has named inputs.
        - A `tf.data` dataset. Should return a tuple
          of either `(inputs, targets)` or
          `(inputs, targets, sample_weights)`.
        - A generator or `keras.utils.Sequence` returning `(inputs, targets)`
          or `(inputs, targets, sample_weights)`.
        A more detailed description of unpacking behavior for iterator types
        (Dataset, generator, Sequence) is given in the `Unpacking behavior
        for iterator-like inputs` section of `Model.fit`.
      y: Target data. Like the input data `x`, it could be either Numpy
        array(s) or TensorFlow tensor(s). It should be consistent with `x`
        (you cannot have Numpy inputs and tensor targets, or inversely). If
        `x` is a dataset, generator or `keras.utils.Sequence` instance, `y`
        should not be specified (since targets will be obtained from the
        iterator/dataset).
      batch_size: Integer or `None`. Number of samples per batch of
        computation. If unspecified, `batch_size` will default to 32. Do not
        specify the `batch_size` if your data is in the form of a dataset,
        generators, or `keras.utils.Sequence` instances (since they generate
        batches).
      verbose: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
      sample_weight: Optional Numpy array of weights for the test samples,
        used for weighting the loss function. You can either pass a flat (1D)
        Numpy array with the same length as the input samples
          (1:1 mapping between weights and samples), or in the case of
            temporal data, you can pass a 2D array with shape `(samples,
            sequence_length)`, to apply a different weight to every timestep
            of every sample. This argument is not supported when `x` is a
            dataset, instead pass sample weights as the third element of `x`.
      steps: Integer or `None`. Total number of steps (batches of samples)
        before declaring the evaluation round finished. Ignored with the
        default value of `None`. If x is a `tf.data` dataset and `steps` is
        None, 'evaluate' will run until the dataset is exhausted. This
        argument is not supported with array inputs.
      callbacks: List of `keras.callbacks.Callback` instances. List of
        callbacks to apply during evaluation. See
        [callbacks](/api_docs/python/tf/keras/callbacks).
      max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
        input only. Maximum size for the generator queue. If unspecified,
        `max_queue_size` will default to 10.
      workers: Integer. Used for generator or `keras.utils.Sequence` input
        only. Maximum number of processes to spin up when using process-based
        threading. If unspecified, `workers` will default to 1.
      use_multiprocessing: Boolean. Used for generator or
        `keras.utils.Sequence` input only. If `True`, use process-based
        threading. If unspecified, `use_multiprocessing` will default to
        `False`. Note that because this implementation relies on
        multiprocessing, you should not pass non-picklable arguments to the
        generator as they can't be passed easily to children processes.
      return_dict: If `True`, loss and metric results are returned as a dict,
        with each key being the name of the metric. If `False`, they are
        returned as a list.
      **kwargs: Unused at this time.

  See the discussion of `Unpacking behavior for iterator-like inputs` for
  `Model.fit`.

  `Model.evaluate` is not yet supported with
  `tf.distribute.experimental.ParameterServerStrategy`.

  Returns:
      Scalar test loss (if the model has a single output and no metrics)
      or list of scalars (if the model has multiple outputs
      and/or metrics). The attribute `model.metrics_names` will give you
      the display labels for the scalar outputs.

  Raises:
      RuntimeError: If `model.evaluate` is wrapped in `tf.function`.
      ValueError: in case of invalid arguments.
  """
  base_layer.keras_api_gauge.get_cell('evaluate').set(True)
  version_utils.disallow_legacy_graph('Model', 'evaluate')
  self._assert_compile_was_called()
  self._check_call_args('evaluate')
  _disallow_inside_tf_function('evaluate')
  use_cached_eval_dataset = kwargs.pop('_use_cached_eval_dataset', False)
  if kwargs:
    raise TypeError('Invalid keyword arguments: %s' % (kwargs,))

  if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
    self._cluster_coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
        self.distribute_strategy)

  with self.distribute_strategy.scope():
    # Use cached evaluation data only when it's called in `Model.fit`
    if (use_cached_eval_dataset
        and getattr(self, '_eval_data_handler', None) is not None):
      data_handler = self._eval_data_handler
    else:
      # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
      data_handler = data_adapter.get_data_handler(
          x=x,
          y=y,
          sample_weight=sample_weight,
          batch_size=batch_size,
          steps_per_epoch=steps,
          initial_epoch=0,
          epochs=1,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          model=self,
          steps_per_execution=self._steps_per_execution)

    # Container that configures and calls `tf.keras.Callback`s.
    if not isinstance(callbacks, callbacks_module.CallbackList):
      callbacks = callbacks_module.CallbackList(
          callbacks,
          add_history=True,
          add_progbar=verbose != 0,
          model=self,
          verbose=verbose,
          epochs=1,
          steps=data_handler.inferred_steps)

    logs = {}
    self.test_function = self.make_test_function()
    self._test_counter.assign(0)
    callbacks.on_test_begin()
    for _, iterator in data_handler.enumerate_epochs():  # Single epoch.
      self.reset_metrics()
      with data_handler.catch_stop_iteration():
        for step in data_handler.steps():
          with tf.profiler.experimental.Trace('test', step_num=step, _r=1):
            callbacks.on_test_batch_begin(step)
            tmp_logs = self.test_function(iterator)
            if data_handler.should_sync:
              context.async_wait()
            logs = tmp_logs  # No error, now safe to assign to logs.
            end_step = step + data_handler.step_increment
            callbacks.on_test_batch_end(end_step, logs)
    logs = tf_utils.sync_to_numpy_or_python_type(logs)
    callbacks.on_test_end(logs=logs)

    if return_dict:
      return logs
    else:
      return flatten_metrics_in_order(logs, self.metrics_names)
def evaluate_generator(self, generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)

Evaluates the model on a data generator.

Deprecated

Model.evaluate() now supports generators, so there is no longer any need to use this endpoint.

Expand source code
@doc_controls.do_not_generate_docs
def evaluate_generator(self,
                       generator,
                       steps=None,
                       callbacks=None,
                       max_queue_size=10,
                       workers=1,
                       use_multiprocessing=False,
                       verbose=0):
  """Evaluates the model on a data generator.

  DEPRECATED:
    `Model.evaluate` now supports generators, so there is no longer any need
    to use this endpoint.
  """
  warnings.warn('`Model.evaluate_generator` is deprecated and '
                'will be removed in a future version. '
                'Please use `Model.evaluate`, which supports generators.')
  self._check_call_args('evaluate_generator')

  return self.evaluate(
      generator,
      steps=steps,
      max_queue_size=max_queue_size,
      workers=workers,
      use_multiprocessing=use_multiprocessing,
      verbose=verbose,
      callbacks=callbacks)
def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose='auto', callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False)

Trains the model for a fixed number of epochs (iterations on a dataset).

Args

x
Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A tf.data dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights). - A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample_weights). - A tf.keras.utils.experimental.DatasetCreator, which wraps a callable that takes a single argument of type tf.distribute.InputContext, and returns a tf.data.Dataset. DatasetCreator should be used when users prefer to specify the per-replica batching and sharding logic for the Dataset. See tf.keras.utils.experimental.DatasetCreator doc for more information. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given below. If using tf.distribute.experimental.ParameterServerStrategy, only DatasetCreator type is supported for x.
y
Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). If x is a dataset, generator, or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from x).
batch_size
Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).
epochs
Integer. Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided. Note that in conjunction with initial_epoch, epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.
verbose
'auto', 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. 'auto' defaults to 1 for most cases, but 2 when used with ParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment).
callbacks
List of Callback instances. List of callbacks to apply during training. See tf.keras.callbacks. Note tf.keras.callbacks.ProgbarLogger and tf.keras.callbacks.History callbacks are created automatically and need not be passed into model.fit. tf.keras.callbacks.ProgbarLogger is created or not based on verbose argument to model.fit. Callbacks with batch-level calls are currently unsupported with tf.distribute.experimental.ParameterServerStrategy, and users are advised to implement epoch-level calls instead with an appropriate steps_per_epoch value.
validation_split
Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling. This argument is not supported when x is a dataset, generator or keras.utils.Sequence instance. validation_split is not yet supported with tf.distribute.experimental.ParameterServerStrategy.
validation_data
Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. Thus, note the fact that the validation loss of data provided using validation_split or validation_data is not affected by regularization layers like noise and dropout. validation_data will override validation_split. validation_data could be: - A tuple (x_val, y_val) of Numpy arrays or tensors. - A tuple (x_val, y_val, val_sample_weights) of NumPy arrays. - A tf.data.Dataset. - A Python generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample_weights). validation_data is not yet supported with tf.distribute.experimental.ParameterServerStrategy.
shuffle
Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). This argument is ignored when x is a generator or an object of tf.data.Dataset. 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when steps_per_epoch is not None.
class_weight
Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
sample_weight
Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. This argument is not supported when x is a dataset, generator, or keras.utils.Sequence instance, instead provide the sample_weights as the third element of x.
initial_epoch
Integer. Epoch at which to start training (useful for resuming a previous training run).
steps_per_epoch
Integer or None. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a tf.data dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps_per_epoch argument. If steps_per_epoch=-1 the training will run indefinitely with an infinitely repeating dataset. This argument is not supported with array inputs. When using tf.distribute.experimental.ParameterServerStrategy: * steps_per_epoch=None is not supported.
validation_steps
Only relevant if validation_data is provided and is a tf.data dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If 'validation_steps' is None, validation will run until the validation_data dataset is exhausted. In the case of an infinitely repeated dataset, it will run into an infinite loop. If 'validation_steps' is specified and only part of the dataset will be consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time.
validation_batch_size
Integer or None. Number of samples per validation batch. If unspecified, will default to batch_size. Do not specify the validation_batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).
validation_freq
Only relevant if validation data is provided. Integer or collections.abc.Container instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. validation_freq=2 runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. validation_freq=[1, 2, 10] runs validation at the end of the 1st, 2nd, and 10th epochs.
max_queue_size
Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
workers
Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.
use_multiprocessing
Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.

Unpacking behavior for iterator-like inputs: A common pattern is to pass a tf.data.Dataset, generator, or tf.keras.utils.Sequence to the x argument of fit, which will in fact yield not only features (x) but optionally targets (y) and sample weights. Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight respectively. Any other type provided will be wrapped in a length one tuple, effectively treating everything as 'x'. When yielding dicts, they should still adhere to the top-level tuple structure. e.g. ({"x0": x0, "x1": x1}, y). Keras will not attempt to separate features, targets, and weights from the keys of a single dict. A notable unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype (tuple) and a mapping datatype (dict). So given a namedtuple of the form: namedtuple("example_tuple", ["y", "x"]) it is ambiguous whether to reverse the order of the elements when interpreting the value. Even worse is a tuple of the form: namedtuple("other_tuple", ["x", "y", "z"]) where it is unclear if the tuple was intended to be unpacked into x, y, and sample_weight or passed through as a single element to x. As a result the data processing code will simply raise a ValueError if it encounters a namedtuple. (Along with instructions to remedy the issue.)

Returns

A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Raises

RuntimeError
  1. If the model was never compiled or,
  1. If model.fit is wrapped in tf.function.
ValueError
In case of mismatch between the provided input data and what the model expects or when the input data is empty.
Expand source code
def fit(self,
        x=None,
        y=None,
        batch_size=None,
        epochs=1,
        verbose='auto',
        callbacks=None,
        validation_split=0.,
        validation_data=None,
        shuffle=True,
        class_weight=None,
        sample_weight=None,
        initial_epoch=0,
        steps_per_epoch=None,
        validation_steps=None,
        validation_batch_size=None,
        validation_freq=1,
        max_queue_size=10,
        workers=1,
        use_multiprocessing=False):
  """Trains the model for a fixed number of epochs (iterations on a dataset).

  Args:
      x: Input data. It could be:
        - A Numpy array (or array-like), or a list of arrays
          (in case the model has multiple inputs).
        - A TensorFlow tensor, or a list of tensors
          (in case the model has multiple inputs).
        - A dict mapping input names to the corresponding array/tensors,
          if the model has named inputs.
        - A `tf.data` dataset. Should return a tuple
          of either `(inputs, targets)` or
          `(inputs, targets, sample_weights)`.
        - A generator or `keras.utils.Sequence` returning `(inputs, targets)`
          or `(inputs, targets, sample_weights)`.
        - A `tf.keras.utils.experimental.DatasetCreator`, which wraps a
          callable that takes a single argument of type
          `tf.distribute.InputContext`, and returns a `tf.data.Dataset`.
          `DatasetCreator` should be used when users prefer to specify the
          per-replica batching and sharding logic for the `Dataset`.
          See `tf.keras.utils.experimental.DatasetCreator` doc for more
          information.
        A more detailed description of unpacking behavior for iterator types
        (Dataset, generator, Sequence) is given below. If using
        `tf.distribute.experimental.ParameterServerStrategy`, only
        `DatasetCreator` type is supported for `x`.
      y: Target data. Like the input data `x`,
        it could be either Numpy array(s) or TensorFlow tensor(s).
        It should be consistent with `x` (you cannot have Numpy inputs and
        tensor targets, or inversely). If `x` is a dataset, generator,
        or `keras.utils.Sequence` instance, `y` should
        not be specified (since targets will be obtained from `x`).
      batch_size: Integer or `None`.
          Number of samples per gradient update.
          If unspecified, `batch_size` will default to 32.
          Do not specify the `batch_size` if your data is in the
          form of datasets, generators, or `keras.utils.Sequence` instances
          (since they generate batches).
      epochs: Integer. Number of epochs to train the model.
          An epoch is an iteration over the entire `x` and `y`
          data provided.
          Note that in conjunction with `initial_epoch`,
          `epochs` is to be understood as "final epoch".
          The model is not trained for a number of iterations
          given by `epochs`, but merely until the epoch
          of index `epochs` is reached.
      verbose: 'auto', 0, 1, or 2. Verbosity mode.
          0 = silent, 1 = progress bar, 2 = one line per epoch.
          'auto' defaults to 1 for most cases, but 2 when used with
          `ParameterServerStrategy`. Note that the progress bar is not
          particularly useful when logged to a file, so verbose=2 is
          recommended when not running interactively (eg, in a production
          environment).
      callbacks: List of `keras.callbacks.Callback` instances.
          List of callbacks to apply during training.
          See `tf.keras.callbacks`. Note `tf.keras.callbacks.ProgbarLogger`
          and `tf.keras.callbacks.History` callbacks are created automatically
          and need not be passed into `model.fit`.
          `tf.keras.callbacks.ProgbarLogger` is created or not based on
          `verbose` argument to `model.fit`.
          Callbacks with batch-level calls are currently unsupported with
          `tf.distribute.experimental.ParameterServerStrategy`, and users are
          advised to implement epoch-level calls instead with an appropriate
          `steps_per_epoch` value.
      validation_split: Float between 0 and 1.
          Fraction of the training data to be used as validation data.
          The model will set apart this fraction of the training data,
          will not train on it, and will evaluate
          the loss and any model metrics
          on this data at the end of each epoch.
          The validation data is selected from the last samples
          in the `x` and `y` data provided, before shuffling. This argument is
          not supported when `x` is a dataset, generator or
         `keras.utils.Sequence` instance.
          `validation_split` is not yet supported with
          `tf.distribute.experimental.ParameterServerStrategy`.
      validation_data: Data on which to evaluate
          the loss and any model metrics at the end of each epoch.
          The model will not be trained on this data. Thus, note the fact
          that the validation loss of data provided using `validation_split`
          or `validation_data` is not affected by regularization layers like
          noise and dropout.
          `validation_data` will override `validation_split`.
          `validation_data` could be:
            - A tuple `(x_val, y_val)` of Numpy arrays or tensors.
            - A tuple `(x_val, y_val, val_sample_weights)` of NumPy arrays.
            - A `tf.data.Dataset`.
            - A Python generator or `keras.utils.Sequence` returning
            `(inputs, targets)` or `(inputs, targets, sample_weights)`.
          `validation_data` is not yet supported with
          `tf.distribute.experimental.ParameterServerStrategy`.
      shuffle: Boolean (whether to shuffle the training data
          before each epoch) or str (for 'batch'). This argument is ignored
          when `x` is a generator or an object of tf.data.Dataset.
          'batch' is a special option for dealing
          with the limitations of HDF5 data; it shuffles in batch-sized
          chunks. Has no effect when `steps_per_epoch` is not `None`.
      class_weight: Optional dictionary mapping class indices (integers)
          to a weight (float) value, used for weighting the loss function
          (during training only).
          This can be useful to tell the model to
          "pay more attention" to samples from
          an under-represented class.
      sample_weight: Optional Numpy array of weights for
          the training samples, used for weighting the loss function
          (during training only). You can either pass a flat (1D)
          Numpy array with the same length as the input samples
          (1:1 mapping between weights and samples),
          or in the case of temporal data,
          you can pass a 2D array with shape
          `(samples, sequence_length)`,
          to apply a different weight to every timestep of every sample. This
          argument is not supported when `x` is a dataset, generator, or
         `keras.utils.Sequence` instance, instead provide the sample_weights
          as the third element of `x`.
      initial_epoch: Integer.
          Epoch at which to start training
          (useful for resuming a previous training run).
      steps_per_epoch: Integer or `None`.
          Total number of steps (batches of samples)
          before declaring one epoch finished and starting the
          next epoch. When training with input tensors such as
          TensorFlow data tensors, the default `None` is equal to
          the number of samples in your dataset divided by
          the batch size, or 1 if that cannot be determined. If x is a
          `tf.data` dataset, and 'steps_per_epoch'
          is None, the epoch will run until the input dataset is exhausted.
          When passing an infinitely repeating dataset, you must specify the
          `steps_per_epoch` argument. If `steps_per_epoch=-1` the training
          will run indefinitely with an infinitely repeating dataset.
          This argument is not supported with array inputs.
          When using `tf.distribute.experimental.ParameterServerStrategy`:
            * `steps_per_epoch=None` is not supported.
      validation_steps: Only relevant if `validation_data` is provided and
          is a `tf.data` dataset. Total number of steps (batches of
          samples) to draw before stopping when performing validation
          at the end of every epoch. If 'validation_steps' is None, validation
          will run until the `validation_data` dataset is exhausted. In the
          case of an infinitely repeated dataset, it will run into an
          infinite loop. If 'validation_steps' is specified and only part of
          the dataset will be consumed, the evaluation will start from the
          beginning of the dataset at each epoch. This ensures that the same
          validation samples are used every time.
      validation_batch_size: Integer or `None`.
          Number of samples per validation batch.
          If unspecified, will default to `batch_size`.
          Do not specify the `validation_batch_size` if your data is in the
          form of datasets, generators, or `keras.utils.Sequence` instances
          (since they generate batches).
      validation_freq: Only relevant if validation data is provided. Integer
          or `collections.abc.Container` instance (e.g. list, tuple, etc.).
          If an integer, specifies how many training epochs to run before a
          new validation run is performed, e.g. `validation_freq=2` runs
          validation every 2 epochs. If a Container, specifies the epochs on
          which to run validation, e.g. `validation_freq=[1, 2, 10]` runs
          validation at the end of the 1st, 2nd, and 10th epochs.
      max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
          input only. Maximum size for the generator queue.
          If unspecified, `max_queue_size` will default to 10.
      workers: Integer. Used for generator or `keras.utils.Sequence` input
          only. Maximum number of processes to spin up
          when using process-based threading. If unspecified, `workers`
          will default to 1.
      use_multiprocessing: Boolean. Used for generator or
          `keras.utils.Sequence` input only. If `True`, use process-based
          threading. If unspecified, `use_multiprocessing` will default to
          `False`. Note that because this implementation relies on
          multiprocessing, you should not pass non-picklable arguments to
          the generator as they can't be passed easily to children processes.

  Unpacking behavior for iterator-like inputs:
      A common pattern is to pass a tf.data.Dataset, generator, or
    tf.keras.utils.Sequence to the `x` argument of fit, which will in fact
    yield not only features (x) but optionally targets (y) and sample weights.
    Keras requires that the output of such iterator-likes be unambiguous. The
    iterator should return a tuple of length 1, 2, or 3, where the optional
    second and third elements will be used for y and sample_weight
    respectively. Any other type provided will be wrapped in a length one
    tuple, effectively treating everything as 'x'. When yielding dicts, they
    should still adhere to the top-level tuple structure.
    e.g. `({"x0": x0, "x1": x1}, y)`. Keras will not attempt to separate
    features, targets, and weights from the keys of a single dict.
      A notable unsupported data type is the namedtuple. The reason is that
    it behaves like both an ordered datatype (tuple) and a mapping
    datatype (dict). So given a namedtuple of the form:
        `namedtuple("example_tuple", ["y", "x"])`
    it is ambiguous whether to reverse the order of the elements when
    interpreting the value. Even worse is a tuple of the form:
        `namedtuple("other_tuple", ["x", "y", "z"])`
    where it is unclear if the tuple was intended to be unpacked into x, y,
    and sample_weight or passed through as a single element to `x`. As a
    result the data processing code will simply raise a ValueError if it
    encounters a namedtuple. (Along with instructions to remedy the issue.)

  Returns:
      A `History` object. Its `History.history` attribute is
      a record of training loss values and metrics values
      at successive epochs, as well as validation loss values
      and validation metrics values (if applicable).

  Raises:
      RuntimeError: 1. If the model was never compiled or,
      2. If `model.fit` is  wrapped in `tf.function`.

      ValueError: In case of mismatch between the provided input data
          and what the model expects or when the input data is empty.
  """
  base_layer.keras_api_gauge.get_cell('fit').set(True)
  # Legacy graph support is contained in `training_v1.Model`.
  version_utils.disallow_legacy_graph('Model', 'fit')
  self._assert_compile_was_called()
  self._check_call_args('fit')
  _disallow_inside_tf_function('fit')

  if verbose == 'auto':
    if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
      verbose = 2  # Default to epoch-level logging for PSStrategy.
    else:
      verbose = 1  # Default to batch-level logging otherwise.

  if validation_split:
    # Create the validation data using the training data. Only supported for
    # `Tensor` and `NumPy` input.
    (x, y, sample_weight), validation_data = (
        data_adapter.train_validation_split(
            (x, y, sample_weight), validation_split=validation_split))

  if validation_data:
    val_x, val_y, val_sample_weight = (
        data_adapter.unpack_x_y_sample_weight(validation_data))

  if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
    self._cluster_coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
        self.distribute_strategy)

  with self.distribute_strategy.scope(), \
       training_utils.RespectCompiledTrainableState(self):
    # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
    data_handler = data_adapter.get_data_handler(
        x=x,
        y=y,
        sample_weight=sample_weight,
        batch_size=batch_size,
        steps_per_epoch=steps_per_epoch,
        initial_epoch=initial_epoch,
        epochs=epochs,
        shuffle=shuffle,
        class_weight=class_weight,
        max_queue_size=max_queue_size,
        workers=workers,
        use_multiprocessing=use_multiprocessing,
        model=self,
        steps_per_execution=self._steps_per_execution)

    # Container that configures and calls `tf.keras.Callback`s.
    if not isinstance(callbacks, callbacks_module.CallbackList):
      callbacks = callbacks_module.CallbackList(
          callbacks,
          add_history=True,
          add_progbar=verbose != 0,
          model=self,
          verbose=verbose,
          epochs=epochs,
          steps=data_handler.inferred_steps)

    self.stop_training = False
    self.train_function = self.make_train_function()
    self._train_counter.assign(0)
    callbacks.on_train_begin()
    training_logs = None
    # Handle fault-tolerance for multi-worker.
    # TODO(omalleyt): Fix the ordering issues that mean this has to
    # happen after `callbacks.on_train_begin`.
    data_handler._initial_epoch = (  # pylint: disable=protected-access
        self._maybe_load_initial_epoch_from_ckpt(initial_epoch))
    logs = None
    for epoch, iterator in data_handler.enumerate_epochs():
      self.reset_metrics()
      callbacks.on_epoch_begin(epoch)
      with data_handler.catch_stop_iteration():
        for step in data_handler.steps():
          with tf.profiler.experimental.Trace(
              'train',
              epoch_num=epoch,
              step_num=step,
              batch_size=batch_size,
              _r=1):
            callbacks.on_train_batch_begin(step)
            tmp_logs = self.train_function(iterator)
            if data_handler.should_sync:
              context.async_wait()
            logs = tmp_logs  # No error, now safe to assign to logs.
            end_step = step + data_handler.step_increment
            callbacks.on_train_batch_end(end_step, logs)
            if self.stop_training:
              break

      logs = tf_utils.sync_to_numpy_or_python_type(logs)
      if logs is None:
        raise ValueError('Expect x to be a non-empty array or dataset.')
      epoch_logs = copy.copy(logs)

      # Run validation.
      if validation_data and self._should_eval(epoch, validation_freq):
        # Create data_handler for evaluation and cache it.
        if getattr(self, '_eval_data_handler', None) is None:
          self._eval_data_handler = data_adapter.get_data_handler(
              x=val_x,
              y=val_y,
              sample_weight=val_sample_weight,
              batch_size=validation_batch_size or batch_size,
              steps_per_epoch=validation_steps,
              initial_epoch=0,
              epochs=1,
              max_queue_size=max_queue_size,
              workers=workers,
              use_multiprocessing=use_multiprocessing,
              model=self,
              steps_per_execution=self._steps_per_execution)
        val_logs = self.evaluate(
            x=val_x,
            y=val_y,
            sample_weight=val_sample_weight,
            batch_size=validation_batch_size or batch_size,
            steps=validation_steps,
            callbacks=callbacks,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
            return_dict=True,
            _use_cached_eval_dataset=True)
        val_logs = {'val_' + name: val for name, val in val_logs.items()}
        epoch_logs.update(val_logs)

      callbacks.on_epoch_end(epoch, epoch_logs)
      training_logs = epoch_logs
      if self.stop_training:
        break

    # If eval data_hanlder exists, delete it after all epochs are done.
    if getattr(self, '_eval_data_handler', None) is not None:
      del self._eval_data_handler
    callbacks.on_train_end(logs=training_logs)
    return self.history
def fit_generator(self, generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0)

Fits the model on data yielded batch-by-batch by a Python generator.

Deprecated

Model.fit() now supports generators, so there is no longer any need to use this endpoint.

Expand source code
@doc_controls.do_not_generate_docs
def fit_generator(self,
                  generator,
                  steps_per_epoch=None,
                  epochs=1,
                  verbose=1,
                  callbacks=None,
                  validation_data=None,
                  validation_steps=None,
                  validation_freq=1,
                  class_weight=None,
                  max_queue_size=10,
                  workers=1,
                  use_multiprocessing=False,
                  shuffle=True,
                  initial_epoch=0):
  """Fits the model on data yielded batch-by-batch by a Python generator.

  DEPRECATED:
    `Model.fit` now supports generators, so there is no longer any need to use
    this endpoint.
  """
  warnings.warn('`Model.fit_generator` is deprecated and '
                'will be removed in a future version. '
                'Please use `Model.fit`, which supports generators.')
  return self.fit(
      generator,
      steps_per_epoch=steps_per_epoch,
      epochs=epochs,
      verbose=verbose,
      callbacks=callbacks,
      validation_data=validation_data,
      validation_steps=validation_steps,
      validation_freq=validation_freq,
      class_weight=class_weight,
      max_queue_size=max_queue_size,
      workers=workers,
      use_multiprocessing=use_multiprocessing,
      shuffle=shuffle,
      initial_epoch=initial_epoch)
def get_layer(self, name=None, index=None)

Retrieves a layer based on either its name (unique) or index.

If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).

Args

name
String, name of layer.
index
Integer, index of layer.

Returns

A layer instance.

Raises

ValueError
In case of invalid layer name or index.
Expand source code
def get_layer(self, name=None, index=None):
  """Retrieves a layer based on either its name (unique) or index.

  If `name` and `index` are both provided, `index` will take precedence.
  Indices are based on order of horizontal graph traversal (bottom-up).

  Args:
      name: String, name of layer.
      index: Integer, index of layer.

  Returns:
      A layer instance.

  Raises:
      ValueError: In case of invalid layer name or index.
  """
  # TODO(fchollet): We could build a dictionary based on layer names
  # since they are constant, but we have not done that yet.
  if index is not None and name is not None:
    raise ValueError('Provide only a layer name or a layer index.')

  if index is not None:
    if len(self.layers) <= index:
      raise ValueError('Was asked to retrieve layer at index ' + str(index) +
                       ' but model only has ' + str(len(self.layers)) +
                       ' layers.')
    else:
      return self.layers[index]

  if name is not None:
    for layer in self.layers:
      if layer.name == name:
        return layer
    raise ValueError('No such layer: ' + name + '.')
  raise ValueError('Provide either a layer name or layer index.')
def get_weights(self)

Retrieves the weights of the model.

Returns

A flat list of Numpy arrays.

Expand source code
def get_weights(self):
  """Retrieves the weights of the model.

  Returns:
      A flat list of Numpy arrays.
  """
  with self.distribute_strategy.scope():
    return super(Model, self).get_weights()
def load_weights(self, filepath, by_name=False, skip_mismatch=False, options=None)

Loads all layer weights, either from a TensorFlow or an HDF5 weight file.

If by_name is False weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights.

If by_name is True, weights are loaded into layers only if they share the same name. This is useful for fine-tuning or transfer-learning models where some of the layers have changed.

Only topological loading (by_name=False) is supported when loading weights from the TensorFlow format. Note that topological loading differs slightly between TensorFlow and HDF5 formats for user-defined classes inheriting from tf.keras.Model: HDF5 loads based on a flattened list of weights, while the TensorFlow format loads based on the object-local names of attributes to which layers are assigned in the Model's constructor.

Args

filepath
String, path to the weights file to load. For weight files in TensorFlow format, this is the file prefix (the same as was passed to save_weights). This can also be a path to a SavedModel saved from model.save.
by_name
Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in TensorFlow format.
skip_mismatch
Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weight (only valid when by_name=True).
options
Optional tf.train.CheckpointOptions object that specifies options for loading weights.

Returns

When loading a weight file in TensorFlow format, returns the same status object as tf.train.Checkpoint.restore. When graph building, restore ops are run automatically as soon as the network is built (on first call for user-defined classes inheriting from Model, immediately if it is already built).

When loading weights in HDF5 format, returns None.

Raises

ImportError
If h5py is not available and the weight file is in HDF5 format.
ValueError
If skip_mismatch is set to True when by_name is False.
Expand source code
def load_weights(self,
                 filepath,
                 by_name=False,
                 skip_mismatch=False,
                 options=None):
  """Loads all layer weights, either from a TensorFlow or an HDF5 weight file.

  If `by_name` is False weights are loaded based on the network's
  topology. This means the architecture should be the same as when the weights
  were saved.  Note that layers that don't have weights are not taken into
  account in the topological ordering, so adding or removing layers is fine as
  long as they don't have weights.

  If `by_name` is True, weights are loaded into layers only if they share the
  same name. This is useful for fine-tuning or transfer-learning models where
  some of the layers have changed.

  Only topological loading (`by_name=False`) is supported when loading weights
  from the TensorFlow format. Note that topological loading differs slightly
  between TensorFlow and HDF5 formats for user-defined classes inheriting from
  `tf.keras.Model`: HDF5 loads based on a flattened list of weights, while the
  TensorFlow format loads based on the object-local names of attributes to
  which layers are assigned in the `Model`'s constructor.

  Args:
      filepath: String, path to the weights file to load. For weight files in
          TensorFlow format, this is the file prefix (the same as was passed
          to `save_weights`). This can also be a path to a SavedModel
          saved from `model.save`.
      by_name: Boolean, whether to load weights by name or by topological
          order. Only topological loading is supported for weight files in
          TensorFlow format.
      skip_mismatch: Boolean, whether to skip loading of layers where there is
          a mismatch in the number of weights, or a mismatch in the shape of
          the weight (only valid when `by_name=True`).
      options: Optional `tf.train.CheckpointOptions` object that specifies
          options for loading weights.

  Returns:
      When loading a weight file in TensorFlow format, returns the same status
      object as `tf.train.Checkpoint.restore`. When graph building, restore
      ops are run automatically as soon as the network is built (on first call
      for user-defined classes inheriting from `Model`, immediately if it is
      already built).

      When loading weights in HDF5 format, returns `None`.

  Raises:
      ImportError: If h5py is not available and the weight file is in HDF5
          format.
      ValueError: If `skip_mismatch` is set to `True` when `by_name` is
        `False`.
  """
  if backend.is_tpu_strategy(self._distribution_strategy):
    if (self._distribution_strategy.extended.steps_per_run > 1 and
        (not saving_utils.is_hdf5_filepath(filepath))):
      raise ValueError('Load weights is not yet supported with TPUStrategy '
                       'with steps_per_run greater than 1.')
  if skip_mismatch and not by_name:
    raise ValueError(
        'When calling model.load_weights, skip_mismatch can only be set to '
        'True when by_name is True.')

  filepath, save_format = _detect_save_format(filepath)
  if save_format == 'tf':
    status = self._trackable_saver.restore(filepath, options)
    if by_name:
      raise NotImplementedError(
          'Weights may only be loaded based on topology into Models when '
          'loading TensorFlow-formatted weights (got by_name=True to '
          'load_weights).')
    if not tf.executing_eagerly():
      session = backend.get_session()
      # Restore existing variables (if any) immediately, and set up a
      # streaming restore for any variables created in the future.
      tf.__internal__.tracking.streaming_restore(status=status, session=session)
    status.assert_nontrivial_match()
  else:
    status = None
    if h5py is None:
      raise ImportError(
          '`load_weights` requires h5py when loading weights from HDF5.')
    if not self._is_graph_network and not self.built:
      raise ValueError(
          'Unable to load weights saved in HDF5 format into a subclassed '
          'Model which has not created its variables yet. Call the Model '
          'first, then load the weights.')
    self._assert_weights_created()
    with h5py.File(filepath, 'r') as f:
      if 'layer_names' not in f.attrs and 'model_weights' in f:
        f = f['model_weights']
      if by_name:
        hdf5_format.load_weights_from_hdf5_group_by_name(
            f, self.layers, skip_mismatch=skip_mismatch)
      else:
        hdf5_format.load_weights_from_hdf5_group(f, self.layers)

  # Perform any layer defined finalization of the layer state.
  for layer in self.layers:
    layer.finalize_state()
  return status
def make_predict_function(self, force=False)

Creates a function that executes one step of inference.

This method can be overridden to support custom inference logic. This method is called by Model.predict() and Model.predict_on_batch().

Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual evaluation logic to Model.predict_step().

This function is cached the first time Model.predict() or Model.predict_on_batch() is called. The cache is cleared whenever Model.compile() is called. You can skip the cache and generate again the function with force=True.

Args

force
Whether to regenerate the predict function and skip the cached function if available.

Returns

Function. The function created by this method should accept a tf.data.Iterator, and return the outputs of the Model.

Expand source code
def make_predict_function(self, force=False):
  """Creates a function that executes one step of inference.

  This method can be overridden to support custom inference logic.
  This method is called by `Model.predict` and `Model.predict_on_batch`.

  Typically, this method directly controls `tf.function` and
  `tf.distribute.Strategy` settings, and delegates the actual evaluation
  logic to `Model.predict_step`.

  This function is cached the first time `Model.predict` or
  `Model.predict_on_batch` is called. The cache is cleared whenever
  `Model.compile` is called. You can skip the cache and generate again the
  function with `force=True`.

  Args:
    force: Whether to regenerate the predict function and skip the cached
      function if available.

  Returns:
    Function. The function created by this method should accept a
    `tf.data.Iterator`, and return the outputs of the `Model`.
  """
  if self.predict_function is not None and not force:
    return self.predict_function

  def step_function(model, iterator):
    """Runs a single evaluation step."""

    def run_step(data):
      outputs = model.predict_step(data)
      # Ensure counter is updated only if `test_step` succeeds.
      with tf.control_dependencies(_minimum_control_deps(outputs)):
        model._predict_counter.assign_add(1)  # pylint: disable=protected-access
      return outputs

    data = next(iterator)
    outputs = model.distribute_strategy.run(run_step, args=(data,))
    outputs = reduce_per_replica(
        outputs, self.distribute_strategy, reduction='concat')
    return outputs

  if (self._steps_per_execution is None or
      self._steps_per_execution.numpy().item() == 1):

    def predict_function(iterator):
      """Runs an evaluation execution with one step."""
      return step_function(self, iterator)

  else:

    def predict_function(iterator):
      """Runs an evaluation execution with multiple steps."""
      outputs = step_function(self, iterator)
      for _ in tf.range(self._steps_per_execution - 1):
        tf.autograph.experimental.set_loop_options(
            shape_invariants=[(
                t, tf_utils.get_tensor_spec(t, dynamic_batch=True).shape)
                              for t in tf.nest.flatten(outputs)])
        step_outputs = step_function(self, iterator)
        outputs = tf.nest.map_structure(lambda t1, t2: concat([t1, t2]), outputs,
                                     step_outputs)
      return outputs

  if not self.run_eagerly:
    predict_function = tf.function(
        predict_function, experimental_relax_shapes=True)

  self.predict_function = predict_function
  return self.predict_function
def make_test_function(self, force=False)

Creates a function that executes one step of evaluation.

This method can be overridden to support custom evaluation logic. This method is called by Model.evaluate() and Model.test_on_batch().

Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual evaluation logic to Model.test_step().

This function is cached the first time Model.evaluate() or Model.test_on_batch() is called. The cache is cleared whenever Model.compile() is called. You can skip the cache and generate again the function with force=True.

Args

force
Whether to regenerate the test function and skip the cached function if available.

Returns

Function. The function created by this method should accept a tf.data.Iterator, and return a dict containing values that will be passed to tf.keras.Callbacks.on_test_batch_end.

Expand source code
def make_test_function(self, force=False):
  """Creates a function that executes one step of evaluation.

  This method can be overridden to support custom evaluation logic.
  This method is called by `Model.evaluate` and `Model.test_on_batch`.

  Typically, this method directly controls `tf.function` and
  `tf.distribute.Strategy` settings, and delegates the actual evaluation
  logic to `Model.test_step`.

  This function is cached the first time `Model.evaluate` or
  `Model.test_on_batch` is called. The cache is cleared whenever
  `Model.compile` is called. You can skip the cache and generate again the
  function with `force=True`.

  Args:
    force: Whether to regenerate the test function and skip the cached
      function if available.

  Returns:
    Function. The function created by this method should accept a
    `tf.data.Iterator`, and return a `dict` containing values that will
    be passed to `tf.keras.Callbacks.on_test_batch_end`.
  """
  if self.test_function is not None and not force:
    return self.test_function

  def step_function(model, iterator):
    """Runs a single evaluation step."""

    def run_step(data):
      outputs = model.test_step(data)
      # Ensure counter is updated only if `test_step` succeeds.
      with tf.control_dependencies(_minimum_control_deps(outputs)):
        model._test_counter.assign_add(1)  # pylint: disable=protected-access
      return outputs

    data = next(iterator)
    outputs = model.distribute_strategy.run(run_step, args=(data,))
    outputs = reduce_per_replica(
        outputs, self.distribute_strategy, reduction='first')
    return outputs

  if (self._steps_per_execution is None or
      self._steps_per_execution.numpy().item() == 1):

    def test_function(iterator):
      """Runs an evaluation execution with one step."""
      return step_function(self, iterator)

  else:

    def test_function(iterator):
      """Runs an evaluation execution with multiple steps."""
      for _ in tf.range(self._steps_per_execution):
        outputs = step_function(self, iterator)
      return outputs

  if not self.run_eagerly:
    test_function = tf.function(
        test_function, experimental_relax_shapes=True)

  self.test_function = test_function

  if self._cluster_coordinator:
    self.test_function = lambda iterator: self._cluster_coordinator.schedule(  # pylint: disable=g-long-lambda
        test_function, args=(iterator,))

  return self.test_function
def make_train_function(self, force=False)

Creates a function that executes one step of training.

This method can be overridden to support custom training logic. This method is called by Model.fit() and Model.train_on_batch().

Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual training logic to Model.train_step().

This function is cached the first time Model.fit() or Model.train_on_batch() is called. The cache is cleared whenever Model.compile() is called. You can skip the cache and generate again the function with force=True.

Args

force
Whether to regenerate the train function and skip the cached function if available.

Returns

Function. The function created by this method should accept a tf.data.Iterator, and return a dict containing values that will be passed to tf.keras.Callbacks.on_train_batch_end, such as {'loss': 0.2, 'accuracy': 0.7}.

Expand source code
def make_train_function(self, force=False):
  """Creates a function that executes one step of training.

  This method can be overridden to support custom training logic.
  This method is called by `Model.fit` and `Model.train_on_batch`.

  Typically, this method directly controls `tf.function` and
  `tf.distribute.Strategy` settings, and delegates the actual training
  logic to `Model.train_step`.

  This function is cached the first time `Model.fit` or
  `Model.train_on_batch` is called. The cache is cleared whenever
  `Model.compile` is called. You can skip the cache and generate again the
  function with `force=True`.

  Args:
    force: Whether to regenerate the train function and skip the cached
      function if available.

  Returns:
    Function. The function created by this method should accept a
    `tf.data.Iterator`, and return a `dict` containing values that will
    be passed to `tf.keras.Callbacks.on_train_batch_end`, such as
    `{'loss': 0.2, 'accuracy': 0.7}`.
  """
  if self.train_function is not None and not force:
    return self.train_function

  def step_function(model, iterator):
    """Runs a single training step."""

    def run_step(data):
      outputs = model.train_step(data)
      # Ensure counter is updated only if `train_step` succeeds.
      with tf.control_dependencies(_minimum_control_deps(outputs)):
        model._train_counter.assign_add(1)  # pylint: disable=protected-access
      return outputs

    data = next(iterator)
    outputs = model.distribute_strategy.run(run_step, args=(data,))
    outputs = reduce_per_replica(
        outputs, self.distribute_strategy, reduction='first')
    write_scalar_summaries(outputs, step=model._train_counter)  # pylint: disable=protected-access
    return outputs

  if (self._steps_per_execution is None or
      self._steps_per_execution.numpy().item() == 1):

    def train_function(iterator):
      """Runs a training execution with one step."""
      return step_function(self, iterator)

  else:

    def train_function(iterator):
      """Runs a training execution with multiple steps."""
      for _ in tf.range(self._steps_per_execution):
        outputs = step_function(self, iterator)
      return outputs

  if not self.run_eagerly:
    train_function = tf.function(
        train_function, experimental_relax_shapes=True)
    self.train_tf_function = train_function

  self.train_function = train_function

  if self._cluster_coordinator:
    self.train_function = lambda iterator: self._cluster_coordinator.schedule(  # pylint: disable=g-long-lambda
        train_function, args=(iterator,))

  return self.train_function
def predict(self, x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)

Generates output predictions for the input samples.

Computation is done in batches. This method is designed for performance in large scale inputs. For small amount of inputs that fit in one batch, directly using __call__ is recommended for faster execution, e.g., model(x), or model(x, training=False) if you have layers such as tf.keras.layers.BatchNormalization that behaves differently during inference. Also, note the fact that test loss is not affected by regularization layers like noise and dropout.

Args

x
Input samples. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A tf.data dataset. - A generator or keras.utils.Sequence instance. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the Unpacking behavior for iterator-like inputs<code> section of </code>Model.fit.
batch_size
Integer or None. Number of samples per batch. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of dataset, generators, or keras.utils.Sequence instances (since they generate batches).
verbose
Verbosity mode, 0 or 1.
steps
Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of None. If x is a tf.data dataset and steps is None, predict will run until the input dataset is exhausted.
callbacks
List of Callback instances. List of callbacks to apply during prediction. See callbacks.
max_queue_size
Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
workers
Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.
use_multiprocessing
Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.

See the discussion of Unpacking behavior for iterator-like inputs for Model.fit(). Note that Model.predict uses the same interpretation rules as Model.fit() and Model.evaluate(), so inputs must be unambiguous for all three methods.

Returns

Numpy array(s) of predictions.

Raises

RuntimeError
If model.predict is wrapped in tf.function.
ValueError
In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size.
Expand source code
def predict(self,
            x,
            batch_size=None,
            verbose=0,
            steps=None,
            callbacks=None,
            max_queue_size=10,
            workers=1,
            use_multiprocessing=False):
  """Generates output predictions for the input samples.

  Computation is done in batches. This method is designed for performance in
  large scale inputs. For small amount of inputs that fit in one batch,
  directly using `__call__` is recommended for faster execution, e.g.,
  `model(x)`, or `model(x, training=False)` if you have layers such as
  `tf.keras.layers.BatchNormalization` that behaves differently during
  inference. Also, note the fact that test loss is not affected by
  regularization layers like noise and dropout.

  Args:
      x: Input samples. It could be:
        - A Numpy array (or array-like), or a list of arrays
          (in case the model has multiple inputs).
        - A TensorFlow tensor, or a list of tensors
          (in case the model has multiple inputs).
        - A `tf.data` dataset.
        - A generator or `keras.utils.Sequence` instance.
        A more detailed description of unpacking behavior for iterator types
        (Dataset, generator, Sequence) is given in the `Unpacking behavior
        for iterator-like inputs` section of `Model.fit`.
      batch_size: Integer or `None`.
          Number of samples per batch.
          If unspecified, `batch_size` will default to 32.
          Do not specify the `batch_size` if your data is in the
          form of dataset, generators, or `keras.utils.Sequence` instances
          (since they generate batches).
      verbose: Verbosity mode, 0 or 1.
      steps: Total number of steps (batches of samples)
          before declaring the prediction round finished.
          Ignored with the default value of `None`. If x is a `tf.data`
          dataset and `steps` is None, `predict` will
          run until the input dataset is exhausted.
      callbacks: List of `keras.callbacks.Callback` instances.
          List of callbacks to apply during prediction.
          See [callbacks](/api_docs/python/tf/keras/callbacks).
      max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
          input only. Maximum size for the generator queue.
          If unspecified, `max_queue_size` will default to 10.
      workers: Integer. Used for generator or `keras.utils.Sequence` input
          only. Maximum number of processes to spin up when using
          process-based threading. If unspecified, `workers` will default
          to 1.
      use_multiprocessing: Boolean. Used for generator or
          `keras.utils.Sequence` input only. If `True`, use process-based
          threading. If unspecified, `use_multiprocessing` will default to
          `False`. Note that because this implementation relies on
          multiprocessing, you should not pass non-picklable arguments to
          the generator as they can't be passed easily to children processes.

  See the discussion of `Unpacking behavior for iterator-like inputs` for
  `Model.fit`. Note that Model.predict uses the same interpretation rules as
  `Model.fit` and `Model.evaluate`, so inputs must be unambiguous for all
  three methods.

  Returns:
      Numpy array(s) of predictions.

  Raises:
      RuntimeError: If `model.predict` is wrapped in `tf.function`.
      ValueError: In case of mismatch between the provided
          input data and the model's expectations,
          or in case a stateful model receives a number of samples
          that is not a multiple of the batch size.
  """
  base_layer.keras_api_gauge.get_cell('predict').set(True)
  version_utils.disallow_legacy_graph('Model', 'predict')
  self._check_call_args('predict')
  _disallow_inside_tf_function('predict')

  # TODO(yashkatariya): Cache model on the coordinator for faster prediction.
  # If running under PSS, then swap it with OneDeviceStrategy so that
  # execution will run on the coordinator.
  original_pss_strategy = None
  if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
    original_pss_strategy = self.distribute_strategy
    self._distribution_strategy = None

  # Cluster coordinator is set by `.fit()` and `.evaluate()` which is not
  # needed in `.predict()` because all the predictions happen on the
  # coordinator/locally.
  if self._cluster_coordinator:
    self._cluster_coordinator = None

  outputs = None
  with self.distribute_strategy.scope():
    # Creates a `tf.data.Dataset` and handles batch and epoch iteration.
    dataset_types = (tf.compat.v1.data.Dataset, tf.data.Dataset)
    if (self._in_multi_worker_mode() or _is_tpu_multi_host(
        self.distribute_strategy)) and isinstance(x, dataset_types):
      try:
        options = tf.data.Options()
        data_option = tf.data.experimental.AutoShardPolicy.DATA
        options.experimental_distribute.auto_shard_policy = data_option
        x = x.with_options(options)
      except ValueError:
        warnings.warn('Using Model.predict with '
                      'MultiWorkerDistributionStrategy or TPUStrategy and '
                      'AutoShardPolicy.FILE might lead to out-of-order result'
                      '. Consider setting it to AutoShardPolicy.DATA.')

    data_handler = data_adapter.get_data_handler(
        x=x,
        batch_size=batch_size,
        steps_per_epoch=steps,
        initial_epoch=0,
        epochs=1,
        max_queue_size=max_queue_size,
        workers=workers,
        use_multiprocessing=use_multiprocessing,
        model=self,
        steps_per_execution=self._steps_per_execution)

    # Container that configures and calls `tf.keras.Callback`s.
    if not isinstance(callbacks, callbacks_module.CallbackList):
      callbacks = callbacks_module.CallbackList(
          callbacks,
          add_history=True,
          add_progbar=verbose != 0,
          model=self,
          verbose=verbose,
          epochs=1,
          steps=data_handler.inferred_steps)

    self.predict_function = self.make_predict_function()
    self._predict_counter.assign(0)
    callbacks.on_predict_begin()
    batch_outputs = None
    for _, iterator in data_handler.enumerate_epochs():  # Single epoch.
      with data_handler.catch_stop_iteration():
        for step in data_handler.steps():
          callbacks.on_predict_batch_begin(step)
          tmp_batch_outputs = self.predict_function(iterator)
          if data_handler.should_sync:
            context.async_wait()
          batch_outputs = tmp_batch_outputs  # No error, now safe to assign.
          if outputs is None:
            outputs = tf.nest.map_structure(lambda batch_output: [batch_output],
                                         batch_outputs)
          else:
            tf.__internal__.nest.map_structure_up_to(
                batch_outputs,
                lambda output, batch_output: output.append(batch_output),
                outputs, batch_outputs)
          end_step = step + data_handler.step_increment
          callbacks.on_predict_batch_end(end_step, {'outputs': batch_outputs})
    if batch_outputs is None:
      raise ValueError('Expect x to be a non-empty array or dataset.')
    callbacks.on_predict_end()
  all_outputs = tf.__internal__.nest.map_structure_up_to(batch_outputs, concat, outputs)

  # If originally PSS strategy was used, then replace it back since predict
  # is running under `OneDeviceStrategy` after the swap and once its done
  # we need to replace it back to PSS again.
  if original_pss_strategy is not None:
    self._distribution_strategy = original_pss_strategy

  return tf_utils.sync_to_numpy_or_python_type(all_outputs)
def predict_generator(self, generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)

Generates predictions for the input samples from a data generator.

Deprecated

Model.predict() now supports generators, so there is no longer any need to use this endpoint.

Expand source code
@doc_controls.do_not_generate_docs
def predict_generator(self,
                      generator,
                      steps=None,
                      callbacks=None,
                      max_queue_size=10,
                      workers=1,
                      use_multiprocessing=False,
                      verbose=0):
  """Generates predictions for the input samples from a data generator.

  DEPRECATED:
    `Model.predict` now supports generators, so there is no longer any need
    to use this endpoint.
  """
  warnings.warn('`Model.predict_generator` is deprecated and '
                'will be removed in a future version. '
                'Please use `Model.predict`, which supports generators.')
  return self.predict(
      generator,
      steps=steps,
      max_queue_size=max_queue_size,
      workers=workers,
      use_multiprocessing=use_multiprocessing,
      verbose=verbose,
      callbacks=callbacks)
def predict_on_batch(self, x)

Returns predictions for a single batch of samples.

Args

x
Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).

Returns

Numpy array(s) of predictions.

Raises

RuntimeError
If model.predict_on_batch is wrapped in tf.function.
ValueError
In case of mismatch between given number of inputs and expectations of the model.
Expand source code
def predict_on_batch(self, x):
  """Returns predictions for a single batch of samples.

  Args:
      x: Input data. It could be:
        - A Numpy array (or array-like), or a list of arrays (in case the
            model has multiple inputs).
        - A TensorFlow tensor, or a list of tensors (in case the model has
            multiple inputs).

  Returns:
      Numpy array(s) of predictions.

  Raises:
      RuntimeError: If `model.predict_on_batch` is wrapped in `tf.function`.
      ValueError: In case of mismatch between given number of inputs and
        expectations of the model.
  """
  self._check_call_args('predict_on_batch')
  _disallow_inside_tf_function('predict_on_batch')
  with self.distribute_strategy.scope():
    iterator = data_adapter.single_batch_iterator(self.distribute_strategy, x)
    self.predict_function = self.make_predict_function()
    outputs = self.predict_function(iterator)
  return tf_utils.sync_to_numpy_or_python_type(outputs)
def predict_step(self, data)

The logic for one inference step.

This method can be overridden to support custom inference logic. This method is called by Model.make_predict_function().

This method should contain the mathematical logic for one step of inference. This typically includes the forward pass.

Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_predict_function(), which can also be overridden.

Args

data
A nested structure of Tensors.

Returns

The result of one inference step, typically the output of calling the Model on data.

Expand source code
def predict_step(self, data):
  """The logic for one inference step.

  This method can be overridden to support custom inference logic.
  This method is called by `Model.make_predict_function`.

  This method should contain the mathematical logic for one step of inference.
  This typically includes the forward pass.

  Configuration details for *how* this logic is run (e.g. `tf.function` and
  `tf.distribute.Strategy` settings), should be left to
  `Model.make_predict_function`, which can also be overridden.

  Args:
    data: A nested structure of `Tensor`s.

  Returns:
    The result of one inference step, typically the output of calling the
    `Model` on data.
  """
  data = data_adapter.expand_1d(data)
  x, _, _ = data_adapter.unpack_x_y_sample_weight(data)
  return self(x, training=False)
def reset_metrics(self)

Resets the state of all the metrics in the model.

Examples:

>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> outputs = tf.keras.layers.Dense(2)(inputs)
>>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
>>> x = np.random.random((2, 3))
>>> y = np.random.randint(0, 2, (2, 2))
>>> _ = model.fit(x, y, verbose=0)
>>> assert all(float(m.result()) for m in model.metrics)
>>> model.reset_metrics()
>>> assert all(float(m.result()) == 0 for m in model.metrics)
Expand source code
def reset_metrics(self):
  """Resets the state of all the metrics in the model.

  Examples:

  >>> inputs = tf.keras.layers.Input(shape=(3,))
  >>> outputs = tf.keras.layers.Dense(2)(inputs)
  >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
  >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"])

  >>> x = np.random.random((2, 3))
  >>> y = np.random.randint(0, 2, (2, 2))
  >>> _ = model.fit(x, y, verbose=0)
  >>> assert all(float(m.result()) for m in model.metrics)

  >>> model.reset_metrics()
  >>> assert all(float(m.result()) == 0 for m in model.metrics)

  """
  for m in self.metrics:
    m.reset_state()
def reset_states(self)
Expand source code
def reset_states(self):
  for layer in self.layers:
    if hasattr(layer, 'reset_states') and getattr(layer, 'stateful', False):
      layer.reset_states()
def save(self, filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None, save_traces=True)

Saves the model to Tensorflow SavedModel or a single HDF5 file.

Please see tf.keras.models.save_model or the Serialization and Saving guide for details.

Args

filepath
String, PathLike, path to SavedModel or H5 file to save the model.
overwrite
Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt.
include_optimizer
If True, save optimizer's state together.
save_format
Either 'tf' or 'h5', indicating whether to save the model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X.
signatures
Signatures to save with the SavedModel. Applicable to the 'tf' format only. Please see the signatures argument in tf.saved_model.save for details.
options
(only applies to SavedModel format) tf.saved_model.SaveOptions object that specifies options for saving to SavedModel.
save_traces
(only applies to SavedModel format) When enabled, the SavedModel will store the function traces for each layer. This can be disabled, so that only the configs of each layer are stored. Defaults to True. Disabling this will decrease serialization time and reduce file size, but it requires that all custom layers/models implement a get_config() method.

Example:

from keras.models import load_model

model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
del model  # deletes the existing model

# returns a compiled model
# identical to the previous one
model = load_model('my_model.h5')
Expand source code
def save(self,
         filepath,
         overwrite=True,
         include_optimizer=True,
         save_format=None,
         signatures=None,
         options=None,
         save_traces=True):
  # pylint: disable=line-too-long
  """Saves the model to Tensorflow SavedModel or a single HDF5 file.

  Please see `tf.keras.models.save_model` or the
  [Serialization and Saving guide](https://keras.io/guides/serialization_and_saving/)
  for details.

  Args:
      filepath: String, PathLike, path to SavedModel or H5 file to save the
          model.
      overwrite: Whether to silently overwrite any existing file at the
          target location, or provide the user with a manual prompt.
      include_optimizer: If True, save optimizer's state together.
      save_format: Either `'tf'` or `'h5'`, indicating whether to save the
          model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X,
          and 'h5' in TF 1.X.
      signatures: Signatures to save with the SavedModel. Applicable to the
          'tf' format only. Please see the `signatures` argument in
          `tf.saved_model.save` for details.
      options: (only applies to SavedModel format)
          `tf.saved_model.SaveOptions` object that specifies options for
          saving to SavedModel.
      save_traces: (only applies to SavedModel format) When enabled, the
          SavedModel will store the function traces for each layer. This
          can be disabled, so that only the configs of each layer are stored.
          Defaults to `True`. Disabling this will decrease serialization time
          and reduce file size, but it requires that all custom layers/models
          implement a `get_config()` method.

  Example:

  ```python
  from keras.models import load_model

  model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
  del model  # deletes the existing model

  # returns a compiled model
  # identical to the previous one
  model = load_model('my_model.h5')
  ```
  """
  # pylint: enable=line-too-long
  save.save_model(self, filepath, overwrite, include_optimizer, save_format,
                  signatures, options, save_traces)
def save_spec(self, dynamic_batch=True)

Returns the tf.TensorSpec of call inputs as a tuple (args, kwargs).

This value is automatically defined after calling the model for the first time. Afterwards, you can use it when exporting the model for serving:

model = tf.keras.Model(...)

@tf.function
def serve(*args, **kwargs):
  outputs = model(*args, **kwargs)
  # Apply postprocessing steps, or add additional outputs.
  ...
  return outputs

# arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this example, is
# an empty dict since functional models do not use keyword arguments.
arg_specs, kwarg_specs = model.save_spec()

model.save(path, signatures={
  'serving_default': serve.get_concrete_function(*arg_specs, **kwarg_specs)
})

Args

dynamic_batch
Whether to set the batch sizes of all the returned tf.TensorSpec to None. (Note that when defining functional or Sequential models with tf.keras.Input([...], batch_size=X), the batch size will always be preserved). Defaults to True.

Returns

If the model inputs are defined, returns a tuple (args, kwargs). All elements in args and kwargs are tf.TensorSpec. If the model inputs are not defined, returns None. The model inputs are automatically set when calling the model, model.fit, model.evaluate or model.predict.

Expand source code
def save_spec(self, dynamic_batch=True):
  """Returns the `tf.TensorSpec` of call inputs as a tuple `(args, kwargs)`.

  This value is automatically defined after calling the model for the first
  time. Afterwards, you can use it when exporting the model for serving:

  ```python
  model = tf.keras.Model(...)

  @tf.function
  def serve(*args, **kwargs):
    outputs = model(*args, **kwargs)
    # Apply postprocessing steps, or add additional outputs.
    ...
    return outputs

  # arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this example, is
  # an empty dict since functional models do not use keyword arguments.
  arg_specs, kwarg_specs = model.save_spec()

  model.save(path, signatures={
    'serving_default': serve.get_concrete_function(*arg_specs, **kwarg_specs)
  })
  ```

  Args:
    dynamic_batch: Whether to set the batch sizes of all the returned
      `tf.TensorSpec` to `None`. (Note that when defining functional or
      Sequential models with `tf.keras.Input([...], batch_size=X)`, the
      batch size will always be preserved). Defaults to `True`.
  Returns:
    If the model inputs are defined, returns a tuple `(args, kwargs)`. All
    elements in `args` and `kwargs` are `tf.TensorSpec`.
    If the model inputs are not defined, returns `None`.
    The model inputs are automatically set when calling the model,
    `model.fit`, `model.evaluate` or `model.predict`.
  """
  return self._get_save_spec(dynamic_batch, inputs_only=False)
def save_weights(self, filepath, overwrite=True, save_format=None, options=None)

Saves all layer weights.

Either saves in HDF5 or in TensorFlow format based on the save_format argument.

When saving in HDF5 format, the weight file has: - layer_names (attribute), a list of strings (ordered names of model layers). - For every layer, a group named layer.name - For every such layer group, a group attribute weight_names, a list of strings (ordered names of weights tensor of the layer). - For every weight in the layer, a dataset storing the weight value, named after the weight tensor.

When saving in TensorFlow format, all objects referenced by the network are saved in the same format as tf.train.Checkpoint, including any Layer instances or Optimizer instances assigned to object attributes. For networks constructed from inputs and outputs using tf.keras.Model(inputs, outputs)<code>, </code>Layer instances used by the network are tracked/saved automatically. For user-defined classes which inherit from tf.keras.Model, Layer instances must be assigned to object attributes, typically in the constructor. See the documentation of tf.train.Checkpoint and tf.keras.Model for details.

While the formats are the same, do not mix save_weights and tf.train.Checkpoint. Checkpoints saved by Model.save_weights() should be loaded using Model.load_weights(). Checkpoints saved using tf.train.Checkpoint.save should be restored using the corresponding tf.train.Checkpoint.restore. Prefer tf.train.Checkpoint over save_weights for training checkpoints.

The TensorFlow format matches objects and variables by starting at a root object, self for save_weights, and greedily matching attribute names. For Model.save() this is the Model, and for Checkpoint.save this is the Checkpoint even if the Checkpoint has a model attached. This means saving a tf.keras.Model using save_weights and loading into a tf.train.Checkpoint with a Model attached (or vice versa) will not match the Model's variables. See the guide to training checkpoints for details on the TensorFlow format.

Args

filepath
String or PathLike, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format.
overwrite
Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt.
save_format
Either 'tf' or 'h5'. A filepath ending in '.h5' or '.keras' will default to HDF5 if save_format is None. Otherwise None defaults to 'tf'.
options
Optional tf.train.CheckpointOptions object that specifies options for saving weights.

Raises

ImportError
If h5py is not available when attempting to save in HDF5 format.
ValueError
For invalid/unknown format arguments.
Expand source code
def save_weights(self,
                 filepath,
                 overwrite=True,
                 save_format=None,
                 options=None):
  """Saves all layer weights.

  Either saves in HDF5 or in TensorFlow format based on the `save_format`
  argument.

  When saving in HDF5 format, the weight file has:
    - `layer_names` (attribute), a list of strings
        (ordered names of model layers).
    - For every layer, a `group` named `layer.name`
        - For every such layer group, a group attribute `weight_names`,
            a list of strings
            (ordered names of weights tensor of the layer).
        - For every weight in the layer, a dataset
            storing the weight value, named after the weight tensor.

  When saving in TensorFlow format, all objects referenced by the network are
  saved in the same format as `tf.train.Checkpoint`, including any `Layer`
  instances or `Optimizer` instances assigned to object attributes. For
  networks constructed from inputs and outputs using `tf.keras.Model(inputs,
  outputs)`, `Layer` instances used by the network are tracked/saved
  automatically. For user-defined classes which inherit from `tf.keras.Model`,
  `Layer` instances must be assigned to object attributes, typically in the
  constructor. See the documentation of `tf.train.Checkpoint` and
  `tf.keras.Model` for details.

  While the formats are the same, do not mix `save_weights` and
  `tf.train.Checkpoint`. Checkpoints saved by `Model.save_weights` should be
  loaded using `Model.load_weights`. Checkpoints saved using
  `tf.train.Checkpoint.save` should be restored using the corresponding
  `tf.train.Checkpoint.restore`. Prefer `tf.train.Checkpoint` over
  `save_weights` for training checkpoints.

  The TensorFlow format matches objects and variables by starting at a root
  object, `self` for `save_weights`, and greedily matching attribute
  names. For `Model.save` this is the `Model`, and for `Checkpoint.save` this
  is the `Checkpoint` even if the `Checkpoint` has a model attached. This
  means saving a `tf.keras.Model` using `save_weights` and loading into a
  `tf.train.Checkpoint` with a `Model` attached (or vice versa) will not match
  the `Model`'s variables. See the [guide to training
  checkpoints](https://www.tensorflow.org/guide/checkpoint) for details
  on the TensorFlow format.

  Args:
      filepath: String or PathLike, path to the file to save the weights to.
          When saving in TensorFlow format, this is the prefix used for
          checkpoint files (multiple files are generated). Note that the '.h5'
          suffix causes weights to be saved in HDF5 format.
      overwrite: Whether to silently overwrite any existing file at the
          target location, or provide the user with a manual prompt.
      save_format: Either 'tf' or 'h5'. A `filepath` ending in '.h5' or
          '.keras' will default to HDF5 if `save_format` is `None`. Otherwise
          `None` defaults to 'tf'.
      options: Optional `tf.train.CheckpointOptions` object that specifies
          options for saving weights.

  Raises:
      ImportError: If h5py is not available when attempting to save in HDF5
          format.
      ValueError: For invalid/unknown format arguments.
  """
  self._assert_weights_created()
  filepath = path_to_string(filepath)
  filepath_is_h5 = saving_utils.is_hdf5_filepath(filepath)
  if save_format is None:
    if filepath_is_h5:
      save_format = 'h5'
    else:
      save_format = 'tf'
  else:
    user_format = save_format.lower().strip()
    if user_format in ('tensorflow', 'tf'):
      save_format = 'tf'
    elif user_format in ('hdf5', 'h5', 'keras'):
      save_format = 'h5'
    else:
      raise ValueError(
          'Unknown format "%s". Was expecting one of {"tf", "h5"}.' % (
              save_format,))
  if save_format == 'tf' and filepath_is_h5:
    raise ValueError(
        ('save_weights got save_format="tf"/"tensorflow", but the '
         'filepath ("%s") looks like an HDF5 file. Omit the ".h5"/".keras" '
         'when saving in TensorFlow format.')
        % filepath)

  if save_format == 'h5' and h5py is None:
    raise ImportError(
        '`save_weights` requires h5py when saving in hdf5.')
  if save_format == 'tf':
    check_filepath = filepath + '.index'
  else:
    check_filepath = filepath
  # If file exists and should not be overwritten:
  if not overwrite and os.path.isfile(check_filepath):
    proceed = ask_to_proceed_with_overwrite(check_filepath)
    if not proceed:
      return
  if save_format == 'h5':
    with h5py.File(filepath, 'w') as f:
      hdf5_format.save_weights_to_hdf5_group(f, self.layers)
  else:
    if tf.executing_eagerly():
      session = None
    else:
      session = backend.get_session()
    self._trackable_saver.save(filepath, session=session, options=options)
    # Record this checkpoint so it's visible from tf.train.latest_checkpoint.
    tf.__internal__.train.update_checkpoint_state(
        save_dir=os.path.dirname(filepath),
        model_checkpoint_path=filepath,
        save_relative_paths=True,
        all_model_checkpoint_paths=[filepath])
def summary(self, line_length=None, positions=None, print_fn=None)

Prints a string summary of the network.

Args

line_length
Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes).
positions
Relative or absolute positions of log elements in each line. If not provided, defaults to [.33, .55, .67, 1.].
print_fn
Print function to use. Defaults to print. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary.

Raises

ValueError
if summary() is called before the model is built.
Expand source code
def summary(self, line_length=None, positions=None, print_fn=None):
  """Prints a string summary of the network.

  Args:
      line_length: Total length of printed lines
          (e.g. set this to adapt the display to different
          terminal window sizes).
      positions: Relative or absolute positions of log elements
          in each line. If not provided,
          defaults to `[.33, .55, .67, 1.]`.
      print_fn: Print function to use. Defaults to `print`.
          It will be called on each line of the summary.
          You can set it to a custom function
          in order to capture the string summary.

  Raises:
      ValueError: if `summary()` is called before the model is built.
  """
  if not self.built:
    raise ValueError('This model has not yet been built. '
                     'Build the model first by calling `build()` or calling '
                     '`fit()` with some data, or specify '
                     'an `input_shape` argument in the first layer(s) for '
                     'automatic build.')
  layer_utils.print_summary(self,
                            line_length=line_length,
                            positions=positions,
                            print_fn=print_fn)
def test_on_batch(self, x, y=None, sample_weight=None, reset_metrics=True, return_dict=False)

Test the model on a single batch of samples.

Args

x
Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
y
Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely).
sample_weight
Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
reset_metrics
If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.
return_dict
If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

Returns

Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

Raises

RuntimeError
If model.test_on_batch is wrapped in tf.function.
ValueError
In case of invalid user-provided arguments.
Expand source code
def test_on_batch(self,
                  x,
                  y=None,
                  sample_weight=None,
                  reset_metrics=True,
                  return_dict=False):
  """Test the model on a single batch of samples.

  Args:
      x: Input data. It could be:
        - A Numpy array (or array-like), or a list of arrays (in case the
            model has multiple inputs).
        - A TensorFlow tensor, or a list of tensors (in case the model has
            multiple inputs).
        - A dict mapping input names to the corresponding array/tensors, if
            the model has named inputs.
      y: Target data. Like the input data `x`, it could be either Numpy
        array(s) or TensorFlow tensor(s). It should be consistent with `x`
        (you cannot have Numpy inputs and tensor targets, or inversely).
      sample_weight: Optional array of the same length as x, containing
        weights to apply to the model's loss for each sample. In the case of
        temporal data, you can pass a 2D array with shape (samples,
        sequence_length), to apply a different weight to every timestep of
        every sample.
      reset_metrics: If `True`, the metrics returned will be only for this
        batch. If `False`, the metrics will be statefully accumulated across
        batches.
      return_dict: If `True`, loss and metric results are returned as a dict,
        with each key being the name of the metric. If `False`, they are
        returned as a list.

  Returns:
      Scalar test loss (if the model has a single output and no metrics)
      or list of scalars (if the model has multiple outputs
      and/or metrics). The attribute `model.metrics_names` will give you
      the display labels for the scalar outputs.

  Raises:
      RuntimeError: If `model.test_on_batch` is wrapped in `tf.function`.
      ValueError: In case of invalid user-provided arguments.
  """
  self._assert_compile_was_called()
  self._check_call_args('test_on_batch')
  _disallow_inside_tf_function('test_on_batch')
  with self.distribute_strategy.scope():
    iterator = data_adapter.single_batch_iterator(self.distribute_strategy, x,
                                                  y, sample_weight)
    self.test_function = self.make_test_function()
    logs = self.test_function(iterator)

  if reset_metrics:
    self.reset_metrics()
  logs = tf_utils.sync_to_numpy_or_python_type(logs)
  if return_dict:
    return logs
  else:
    return flatten_metrics_in_order(logs, self.metrics_names)
def test_step(self, data)

The logic for one evaluation step.

This method can be overridden to support custom evaluation logic. This method is called by Model.make_test_function().

This function should contain the mathematical logic for one step of evaluation. This typically includes the forward pass, loss calculation, and metrics updates.

Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_test_function(), which can also be overridden.

Args

data
A nested structure of Tensors.

Returns

A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the values of the Model's metrics are returned.

Expand source code
def test_step(self, data):
  """The logic for one evaluation step.

  This method can be overridden to support custom evaluation logic.
  This method is called by `Model.make_test_function`.

  This function should contain the mathematical logic for one step of
  evaluation.
  This typically includes the forward pass, loss calculation, and metrics
  updates.

  Configuration details for *how* this logic is run (e.g. `tf.function` and
  `tf.distribute.Strategy` settings), should be left to
  `Model.make_test_function`, which can also be overridden.

  Args:
    data: A nested structure of `Tensor`s.

  Returns:
    A `dict` containing values that will be passed to
    `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
    values of the `Model`'s metrics are returned.
  """
  data = data_adapter.expand_1d(data)
  x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)

  y_pred = self(x, training=False)
  # Updates stateful loss metrics.
  self.compiled_loss(
      y, y_pred, sample_weight, regularization_losses=self.losses)
  self.compiled_metrics.update_state(y, y_pred, sample_weight)
  # Collect metrics to return
  return_metrics = {}
  for metric in self.metrics:
    result = metric.result()
    if isinstance(result, dict):
      return_metrics.update(result)
    else:
      return_metrics[metric.name] = result
  return return_metrics
def to_json(self, **kwargs)

Returns a JSON string containing the network configuration.

To load a network from a JSON save file, use keras.models.model_from_json(json_string, custom_objects={}).

Args

**kwargs
Additional keyword arguments to be passed to json.dumps().

Returns

A JSON string.

Expand source code
def to_json(self, **kwargs):
  """Returns a JSON string containing the network configuration.

  To load a network from a JSON save file, use
  `keras.models.model_from_json(json_string, custom_objects={})`.

  Args:
      **kwargs: Additional keyword arguments
          to be passed to `json.dumps()`.

  Returns:
      A JSON string.
  """
  model_config = self._updated_config()
  return json.dumps(
      model_config, default=json_utils.get_json_type, **kwargs)
def to_yaml(self, **kwargs)

Returns a yaml string containing the network configuration.

Note: Since TF 2.6, this method is no longer supported and will raise a RuntimeError.

To load a network from a yaml save file, use keras.models.model_from_yaml(yaml_string, custom_objects={}).

custom_objects should be a dictionary mapping the names of custom losses / layers / etc to the corresponding functions / classes.

Args

**kwargs
Additional keyword arguments to be passed to yaml.dump().

Returns

A YAML string.

Raises

RuntimeError
announces that the method poses a security risk (Use the safer safe_load function instead of unsafe_load when possible)
Expand source code
def to_yaml(self, **kwargs):
  """Returns a yaml string containing the network configuration.

  Note: Since TF 2.6, this method is no longer supported and will raise a
  RuntimeError.

  To load a network from a yaml save file, use
  `keras.models.model_from_yaml(yaml_string, custom_objects={})`.

  `custom_objects` should be a dictionary mapping
  the names of custom losses / layers / etc to the corresponding
  functions / classes.

  Args:
      **kwargs: Additional keyword arguments
          to be passed to `yaml.dump()`.

  Returns:
      A YAML string.

  Raises:
      RuntimeError: announces that the method poses a security risk
        (Use the safer `safe_load` function instead of `unsafe_load` when possible)
  """
  raise RuntimeError(
      'Method `model.to_yaml()` has been removed due to security risk of '
      'arbitrary code execution. Please use `model.to_json()` instead.'
  )
def train_on_batch(self, x, y=None, sample_weight=None, class_weight=None, reset_metrics=True, return_dict=False)

Runs a single gradient update on a single batch of data.

Args

x
Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
y
Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely).
sample_weight
Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
class_weight
Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
reset_metrics
If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.
return_dict
If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

Returns

Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

Raises

RuntimeError
If model.train_on_batch is wrapped in tf.function.
ValueError
In case of invalid user-provided arguments.
Expand source code
def train_on_batch(self,
                   x,
                   y=None,
                   sample_weight=None,
                   class_weight=None,
                   reset_metrics=True,
                   return_dict=False):
  """Runs a single gradient update on a single batch of data.

  Args:
      x: Input data. It could be:
        - A Numpy array (or array-like), or a list of arrays
            (in case the model has multiple inputs).
        - A TensorFlow tensor, or a list of tensors
            (in case the model has multiple inputs).
        - A dict mapping input names to the corresponding array/tensors,
            if the model has named inputs.
      y: Target data. Like the input data `x`, it could be either Numpy
        array(s) or TensorFlow tensor(s). It should be consistent with `x`
        (you cannot have Numpy inputs and tensor targets, or inversely).
      sample_weight: Optional array of the same length as x, containing
        weights to apply to the model's loss for each sample. In the case of
        temporal data, you can pass a 2D array with shape (samples,
        sequence_length), to apply a different weight to every timestep of
        every sample.
      class_weight: Optional dictionary mapping class indices (integers) to a
        weight (float) to apply to the model's loss for the samples from this
        class during training. This can be useful to tell the model to "pay
        more attention" to samples from an under-represented class.
      reset_metrics: If `True`, the metrics returned will be only for this
        batch. If `False`, the metrics will be statefully accumulated across
        batches.
      return_dict: If `True`, loss and metric results are returned as a dict,
        with each key being the name of the metric. If `False`, they are
        returned as a list.

  Returns:
      Scalar training loss
      (if the model has a single output and no metrics)
      or list of scalars (if the model has multiple outputs
      and/or metrics). The attribute `model.metrics_names` will give you
      the display labels for the scalar outputs.

  Raises:
    RuntimeError: If `model.train_on_batch` is wrapped in `tf.function`.
    ValueError: In case of invalid user-provided arguments.
  """
  self._assert_compile_was_called()
  self._check_call_args('train_on_batch')
  _disallow_inside_tf_function('train_on_batch')
  with self.distribute_strategy.scope(), \
       training_utils.RespectCompiledTrainableState(self):
    iterator = data_adapter.single_batch_iterator(self.distribute_strategy, x,
                                                  y, sample_weight,
                                                  class_weight)
    self.train_function = self.make_train_function()
    logs = self.train_function(iterator)

  if reset_metrics:
    self.reset_metrics()
  logs = tf_utils.sync_to_numpy_or_python_type(logs)
  if return_dict:
    return logs
  else:
    return flatten_metrics_in_order(logs, self.metrics_names)
def train_step(self, data)

The logic for one training step.

This method can be overridden to support custom training logic. For concrete examples of how to override this method see Customizing what happends in fit. This method is called by Model.make_train_function().

This method should contain the mathematical logic for one step of training. This typically includes the forward pass, loss calculation, backpropagation, and metric updates.

Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_train_function(), which can also be overridden.

Args

data
A nested structure of Tensors.

Returns

A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the values of the Model's metrics are returned. Example: {'loss': 0.2, 'accuracy': 0.7}.

Expand source code
def train_step(self, data):
  """The logic for one training step.

  This method can be overridden to support custom training logic.
  For concrete examples of how to override this method see
  [Customizing what happends in fit](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit).
  This method is called by `Model.make_train_function`.

  This method should contain the mathematical logic for one step of training.
  This typically includes the forward pass, loss calculation, backpropagation,
  and metric updates.

  Configuration details for *how* this logic is run (e.g. `tf.function` and
  `tf.distribute.Strategy` settings), should be left to
  `Model.make_train_function`, which can also be overridden.

  Args:
    data: A nested structure of `Tensor`s.

  Returns:
    A `dict` containing values that will be passed to
    `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
    values of the `Model`'s metrics are returned. Example:
    `{'loss': 0.2, 'accuracy': 0.7}`.

  """
  # These are the only transformations `Model.fit` applies to user-input
  # data when a `tf.data.Dataset` is provided.
  data = data_adapter.expand_1d(data)
  x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data)
  # Run forward pass.
  with tf.GradientTape() as tape:
    y_pred = self(x, training=True)
    loss = self.compiled_loss(
        y, y_pred, sample_weight, regularization_losses=self.losses)
  # Run backwards pass.
  self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
  self.compiled_metrics.update_state(y, y_pred, sample_weight)
  # Collect metrics to return
  return_metrics = {}
  for metric in self.metrics:
    result = metric.result()
    if isinstance(result, dict):
      return_metrics.update(result)
    else:
      return_metrics[metric.name] = result
  return return_metrics

Inherited members

class Sequential (layers=None, name=None)

Sequential groups a linear stack of layers into a tf.keras.Model.

Sequential provides training and inference features on this model.

Examples:

>>> # Optionally, the first layer can receive an <code>input\_shape</code> argument:
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
>>> # Afterwards, we do automatic shape inference:
>>> model.add(tf.keras.layers.Dense(4))
>>> # This is identical to the following:
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.Input(shape=(16,)))
>>> model.add(tf.keras.layers.Dense(8))
>>> # Note that you can also omit the <code>input\_shape</code> argument.
>>> # In that case the model doesn't have any weights until the first call
>>> # to a training/evaluation method (since it isn't yet built):
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8))
>>> model.add(tf.keras.layers.Dense(4))
>>> # model.weights not created yet
>>> # Whereas if you specify the input shape, the model gets built
>>> # continuously as you are adding layers:
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
>>> model.add(tf.keras.layers.Dense(4))
>>> len(model.weights)
4
>>> # When using the delayed-build pattern (no input shape specified), you can
>>> # choose to manually build your model by calling
>>> # <code>build(batch\_input\_shape)</code>:
>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(8))
>>> model.add(tf.keras.layers.Dense(4))
>>> model.build((None, 16))
>>> len(model.weights)
4
# Note that when using the delayed-build pattern (no input shape specified),
# the model gets built the first time you call `fit`, `eval`, or `predict`,
# or the first time you call the model on some input data.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='sgd', loss='mse')
# This builds the model for the first time:
model.fit(x, y, batch_size=32, epochs=10)

Creates a Sequential model instance.

Args

layers
Optional list of layers to add to the model.
name
Optional name for the model.
Expand source code
class Sequential(functional.Functional):
  """`Sequential` groups a linear stack of layers into a `tf.keras.Model`.

  `Sequential` provides training and inference features on this model.

  Examples:

  >>> # Optionally, the first layer can receive an `input_shape` argument:
  >>> model = tf.keras.Sequential()
  >>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
  >>> # Afterwards, we do automatic shape inference:
  >>> model.add(tf.keras.layers.Dense(4))

  >>> # This is identical to the following:
  >>> model = tf.keras.Sequential()
  >>> model.add(tf.keras.Input(shape=(16,)))
  >>> model.add(tf.keras.layers.Dense(8))

  >>> # Note that you can also omit the `input_shape` argument.
  >>> # In that case the model doesn't have any weights until the first call
  >>> # to a training/evaluation method (since it isn't yet built):
  >>> model = tf.keras.Sequential()
  >>> model.add(tf.keras.layers.Dense(8))
  >>> model.add(tf.keras.layers.Dense(4))
  >>> # model.weights not created yet

  >>> # Whereas if you specify the input shape, the model gets built
  >>> # continuously as you are adding layers:
  >>> model = tf.keras.Sequential()
  >>> model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
  >>> model.add(tf.keras.layers.Dense(4))
  >>> len(model.weights)
  4

  >>> # When using the delayed-build pattern (no input shape specified), you can
  >>> # choose to manually build your model by calling
  >>> # `build(batch_input_shape)`:
  >>> model = tf.keras.Sequential()
  >>> model.add(tf.keras.layers.Dense(8))
  >>> model.add(tf.keras.layers.Dense(4))
  >>> model.build((None, 16))
  >>> len(model.weights)
  4

  ```python
  # Note that when using the delayed-build pattern (no input shape specified),
  # the model gets built the first time you call `fit`, `eval`, or `predict`,
  # or the first time you call the model on some input data.
  model = tf.keras.Sequential()
  model.add(tf.keras.layers.Dense(8))
  model.add(tf.keras.layers.Dense(1))
  model.compile(optimizer='sgd', loss='mse')
  # This builds the model for the first time:
  model.fit(x, y, batch_size=32, epochs=10)
  ```
  """

  @tf.__internal__.tracking.no_automatic_dependency_tracking
  def __init__(self, layers=None, name=None):
    """Creates a `Sequential` model instance.

    Args:
      layers: Optional list of layers to add to the model.
      name: Optional name for the model.
    """
    # Skip the init in FunctionalModel since model doesn't have input/output yet
    super(functional.Functional, self).__init__(  # pylint: disable=bad-super-call
        name=name, autocast=False)
    base_layer.keras_api_gauge.get_cell('Sequential').set(True)
    self.supports_masking = True
    self._compute_output_and_mask_jointly = True
    self._auto_track_sub_layers = False
    self._inferred_input_shape = None
    self._has_explicit_input_shape = False
    self._input_dtype = None
    self._layer_call_argspecs = {}
    self._created_nodes = set()
    # Flag that indicate whether the sequential network topology has been
    # created. It is false when there isn't any layer, or the layers doesn't
    # have input shape.
    self._graph_initialized = False

    # Unfortunately some Sequential models using custom layers or FeatureColumn
    # layers have multiple inputs. This is fundamentally incompatible with
    # most of the Sequential API, and we have to disable a number of features
    # for such models.
    self._use_legacy_deferred_behavior = False

    # Add to the model any layers passed to the constructor.
    if layers:
      if not isinstance(layers, (list, tuple)):
        layers = [layers]
      for layer in layers:
        self.add(layer)

  @property
  def layers(self):
    # Historically, `sequential.layers` only returns layers that were added
    # via `add`, and omits the auto-generated `InputLayer` that comes at the
    # bottom of the stack.
    # `Trackable` manages the `_layers` attributes and does filtering
    # over it.
    layers = super(Sequential, self).layers
    if layers and isinstance(layers[0], input_layer.InputLayer):
      return layers[1:]
    return layers[:]

  @tf.__internal__.tracking.no_automatic_dependency_tracking
  def add(self, layer):
    """Adds a layer instance on top of the layer stack.

    Args:
        layer: layer instance.

    Raises:
        TypeError: If `layer` is not a layer instance.
        ValueError: In case the `layer` argument does not
            know its input shape.
        ValueError: In case the `layer` argument has
            multiple output tensors, or is already connected
            somewhere else (forbidden in `Sequential` models).
    """
    # If we are passed a Keras tensor created by keras.Input(), we can extract
    # the input layer from its keras history and use that without any loss of
    # generality.
    if hasattr(layer, '_keras_history'):
      origin_layer = layer._keras_history[0]
      if isinstance(origin_layer, input_layer.InputLayer):
        layer = origin_layer

    if isinstance(layer, tf.Module):
      if not isinstance(layer, base_layer.Layer):
        layer = functional.ModuleWrapper(layer)
    else:
      raise TypeError('The added layer must be '
                      'an instance of class Layer. '
                      'Found: ' + str(layer))

    tf_utils.assert_no_legacy_layers([layer])
    if not self._is_layer_name_unique(layer):
      raise ValueError('All layers added to a Sequential model '
                       'should have unique names. Name "%s" is already the name'
                       ' of a layer in this model. Update the `name` argument '
                       'to pass a unique name.' % (layer.name,))

    self.built = False
    set_inputs = False
    self._maybe_create_attribute('_self_tracked_trackables', [])
    if not self._self_tracked_trackables:
      if isinstance(layer, input_layer.InputLayer):
        # Case where the user passes an Input or InputLayer layer via `add`.
        set_inputs = True
      else:
        batch_shape, dtype = training_utils.get_input_shape_and_dtype(layer)
        if batch_shape:
          # Instantiate an input layer.
          x = input_layer.Input(
              batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
          # This will build the current layer
          # and create the node connecting the current layer
          # to the input layer we just created.
          layer(x)
          set_inputs = True

      if set_inputs:
        outputs = tf.nest.flatten(layer._inbound_nodes[-1].outputs)
        if len(outputs) != 1:
          raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
        self.outputs = outputs
        self.inputs = layer_utils.get_source_inputs(self.outputs[0])
        self.built = True
        self._has_explicit_input_shape = True

    elif self.outputs:
      # If the model is being built continuously on top of an input layer:
      # refresh its output.
      output_tensor = layer(self.outputs[0])
      if len(tf.nest.flatten(output_tensor)) != 1:
        raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
      self.outputs = [output_tensor]
      self.built = True

    if set_inputs or self._graph_initialized:
      self._init_graph_network(self.inputs, self.outputs)
      self._graph_initialized = True
    else:
      self._self_tracked_trackables.append(layer)
      self._handle_deferred_layer_dependencies([layer])

    self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)

  @tf.__internal__.tracking.no_automatic_dependency_tracking
  def pop(self):
    """Removes the last layer in the model.

    Raises:
        TypeError: if there are no layers in the model.
    """
    if not self.layers:
      raise TypeError('There are no layers in the model.')

    layer = self._self_tracked_trackables.pop()
    self._layer_call_argspecs.pop(layer)
    if not self.layers:
      self.outputs = None
      self.inputs = None
      self.built = False
      self._inferred_input_shape = None
      self._has_explicit_input_shape = False
      self._graph_initialized = False
    elif self._graph_initialized:
      self.layers[-1]._outbound_nodes = []
      self.outputs = [self.layers[-1].output]
      self._init_graph_network(self.inputs, self.outputs)
      self.built = True

  @tf.__internal__.tracking.no_automatic_dependency_tracking
  def _build_graph_network_for_inferred_shape(self,
                                              input_shape,
                                              input_dtype=None):
    if input_shape is None or not self.layers:
      return
    if not tf.__internal__.tf2.enabled() or not tf.compat.v1.executing_eagerly_outside_functions():
      # This behavior is disabled in V1 or when eager execution is disabled.
      return
    if (not self._has_explicit_input_shape and
        not self._use_legacy_deferred_behavior):
      # Determine whether the input shape is novel, i.e. whether the model
      # should be rebuilt.
      input_shape = tuple(input_shape)
      if self._inferred_input_shape is None:
        new_shape = input_shape
      else:
        new_shape = relax_input_shape(self._inferred_input_shape, input_shape)
      if (new_shape is not None and new_shape != self._inferred_input_shape):
        # A novel shape has been received: we need to rebuild the model.
        # In case we are inside a graph function, we step out of it.
        with tf.init_scope():
          inputs = input_layer.Input(
              batch_shape=new_shape,
              dtype=input_dtype,
              name=self.layers[0].name + '_input')
          layer_input = inputs
          created_nodes = set()
          for layer in self.layers:
            # Clear nodes previously created via this method. This prevents
            # node accumulation and ensures that e.g. `layer.output` is
            # always connected to `model.inputs`
            # (this is important e.g. for the feature extraction use case).
            # We don't just do `layer._inbound_nodes = []` in order
            # not to break shared layers added to Sequential models (which is
            # technically illegal as per the `add()` docstring,
            # but wasn't previously disabled).
            clear_previously_created_nodes(layer, self._created_nodes)
            try:
              # Create Functional API connection by calling the current layer
              layer_output = layer(layer_input)
            except:  # pylint:disable=bare-except
              # Functional API calls may fail for a number of reasons:
              # 1) The layer may be buggy. In this case it will be easier for
              # the user to debug if we fail on the first call on concrete data,
              # instead of our own call on a symbolic input.
              # 2) The layer is dynamic (graph-incompatible) and hasn't
              # overridden `compute_output_shape`. In this case, it is
              # impossible to build a graph network.
              # 3) The layer is otherwise incompatible with the Functional API
              # (e.g. this is the case for some probabilistic layers that rely
              # on hacks and that do not return tensors).
              # In all these cases, we should avoid creating a graph network
              # (or we simply can't).
              self._use_legacy_deferred_behavior = True
              return
            if len(tf.nest.flatten(layer_output)) != 1:
              raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
            # Keep track of nodes just created above
            track_nodes_created_by_last_call(layer, created_nodes)
            layer_input = layer_output
            outputs = layer_output
          self._created_nodes = created_nodes
          try:
            # Initialize a graph Network. This call will never fail for
            # a stack of valid Keras layers.
            # However some users have layers that are fundamentally incompatible
            # with the Functional API, which do not return tensors. In this
            # case, we fall back to the legacy deferred behavior.
            # TODO(fchollet): consider raising here, as we should not be
            # supporting such layers.
            self._init_graph_network(inputs, outputs)
            self._graph_initialized = True
          except:  # pylint:disable=bare-except
            self._use_legacy_deferred_behavior = True
        self._inferred_input_shape = new_shape

  @generic_utils.default
  def build(self, input_shape=None):
    if self._graph_initialized:
      self._init_graph_network(self.inputs, self.outputs)
    else:
      if input_shape is None:
        raise ValueError('You must provide an `input_shape` argument.')
      self._build_graph_network_for_inferred_shape(input_shape)
      if not self.built:
        input_shape = tuple(input_shape)
        self._build_input_shape = input_shape
        super(Sequential, self).build(input_shape)
    self.built = True

  def call(self, inputs, training=None, mask=None):  # pylint: disable=redefined-outer-name
    # If applicable, update the static input shape of the model.
    if not self._has_explicit_input_shape:
      if not tf.is_tensor(inputs) and not isinstance(
          inputs, tf.Tensor):
        # This is a Sequential with mutiple inputs. This is technically an
        # invalid use case of Sequential, but we tolerate it for backwards
        # compatibility.
        self._use_legacy_deferred_behavior = True
        self._build_input_shape = tf.nest.map_structure(_get_shape_tuple, inputs)
        if tf.__internal__.tf2.enabled():
          logging.warning('Layers in a Sequential model should only have a '
                          'single input tensor, but we receive a %s input: %s'
                          '\nConsider rewriting this model with the Functional '
                          'API.' % (type(inputs), inputs))
      else:
        self._build_graph_network_for_inferred_shape(inputs.shape, inputs.dtype)

    if self._graph_initialized:
      if not self.built:
        self._init_graph_network(self.inputs, self.outputs)
      return super(Sequential, self).call(inputs, training=training, mask=mask)

    outputs = inputs  # handle the corner case where self.layers is empty
    for layer in self.layers:
      # During each iteration, `inputs` are the inputs to `layer`, and `outputs`
      # are the outputs of `layer` applied to `inputs`. At the end of each
      # iteration `inputs` is set to `outputs` to prepare for the next layer.
      kwargs = {}
      argspec = self._layer_call_argspecs[layer].args
      if 'mask' in argspec:
        kwargs['mask'] = mask
      if 'training' in argspec:
        kwargs['training'] = training

      outputs = layer(inputs, **kwargs)

      if len(tf.nest.flatten(outputs)) != 1:
        raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
      # `outputs` will be the inputs to the next layer.
      inputs = outputs
      mask = getattr(outputs, '_keras_mask', None)
    return outputs

  def compute_output_shape(self, input_shape):
    shape = input_shape
    for layer in self.layers:
      shape = layer.compute_output_shape(shape)
    return shape

  def compute_mask(self, inputs, mask):
    # TODO(omalleyt): b/123540974 This function is not really safe to call
    # by itself because it will duplicate any updates and losses in graph
    # mode by `call`ing the Layers again.
    outputs = self.call(inputs, mask=mask)  # pylint: disable=unexpected-keyword-arg
    return getattr(outputs, '_keras_mask', None)

  def get_config(self):
    layer_configs = []
    for layer in super(Sequential, self).layers:
      # `super().layers` include the InputLayer if available (it is filtered out
      # of `self.layers`). Note that `self._self_tracked_trackables` is managed
      # by the tracking infrastructure and should not be used.
      layer_configs.append(generic_utils.serialize_keras_object(layer))
    config = {
        'name': self.name,
        'layers': copy.deepcopy(layer_configs)
    }
    if not self._is_graph_network and self._build_input_shape is not None:
      config['build_input_shape'] = self._build_input_shape
    return config

  @classmethod
  def from_config(cls, config, custom_objects=None):
    if 'name' in config:
      name = config['name']
      build_input_shape = config.get('build_input_shape')
      layer_configs = config['layers']
    else:
      name = None
      build_input_shape = None
      layer_configs = config
    model = cls(name=name)
    for layer_config in layer_configs:
      layer = layer_module.deserialize(layer_config,
                                       custom_objects=custom_objects)
      model.add(layer)
    if (not model.inputs and build_input_shape and
        isinstance(build_input_shape, (tuple, list))):
      model.build(build_input_shape)
    return model

  @property
  def input_spec(self):
    if hasattr(self, '_manual_input_spec'):
      return self._manual_input_spec
    if self.layers and hasattr(self.layers[0], 'input_spec'):
      return self.layers[0].input_spec
    return None

  @input_spec.setter
  def input_spec(self, value):
    self._manual_input_spec = value

  @property
  def _trackable_saved_model_saver(self):
    return model_serialization.SequentialSavedModelSaver(self)

  def _is_layer_name_unique(self, layer):
    for ref_layer in self.layers:
      if layer.name == ref_layer.name and ref_layer is not layer:
        return False
    return True

  def _assert_weights_created(self):
    if self._graph_initialized:
      return
    # When the graph has not been initialized, use the Model's implementation to
    # to check if the weights has been created.
    super(functional.Functional, self)._assert_weights_created()  # pylint: disable=bad-super-call

Ancestors

Subclasses

Instance variables

var layers
Expand source code
@property
def layers(self):
  # Historically, `sequential.layers` only returns layers that were added
  # via `add`, and omits the auto-generated `InputLayer` that comes at the
  # bottom of the stack.
  # `Trackable` manages the `_layers` attributes and does filtering
  # over it.
  layers = super(Sequential, self).layers
  if layers and isinstance(layers[0], input_layer.InputLayer):
    return layers[1:]
  return layers[:]

Methods

def add(self, layer)

Adds a layer instance on top of the layer stack.

Args

layer
layer instance.

Raises

TypeError
If layer is not a layer instance.
ValueError
In case the layer argument does not know its input shape.
ValueError
In case the layer argument has multiple output tensors, or is already connected somewhere else (forbidden in Sequential models).
Expand source code
@tf.__internal__.tracking.no_automatic_dependency_tracking
def add(self, layer):
  """Adds a layer instance on top of the layer stack.

  Args:
      layer: layer instance.

  Raises:
      TypeError: If `layer` is not a layer instance.
      ValueError: In case the `layer` argument does not
          know its input shape.
      ValueError: In case the `layer` argument has
          multiple output tensors, or is already connected
          somewhere else (forbidden in `Sequential` models).
  """
  # If we are passed a Keras tensor created by keras.Input(), we can extract
  # the input layer from its keras history and use that without any loss of
  # generality.
  if hasattr(layer, '_keras_history'):
    origin_layer = layer._keras_history[0]
    if isinstance(origin_layer, input_layer.InputLayer):
      layer = origin_layer

  if isinstance(layer, tf.Module):
    if not isinstance(layer, base_layer.Layer):
      layer = functional.ModuleWrapper(layer)
  else:
    raise TypeError('The added layer must be '
                    'an instance of class Layer. '
                    'Found: ' + str(layer))

  tf_utils.assert_no_legacy_layers([layer])
  if not self._is_layer_name_unique(layer):
    raise ValueError('All layers added to a Sequential model '
                     'should have unique names. Name "%s" is already the name'
                     ' of a layer in this model. Update the `name` argument '
                     'to pass a unique name.' % (layer.name,))

  self.built = False
  set_inputs = False
  self._maybe_create_attribute('_self_tracked_trackables', [])
  if not self._self_tracked_trackables:
    if isinstance(layer, input_layer.InputLayer):
      # Case where the user passes an Input or InputLayer layer via `add`.
      set_inputs = True
    else:
      batch_shape, dtype = training_utils.get_input_shape_and_dtype(layer)
      if batch_shape:
        # Instantiate an input layer.
        x = input_layer.Input(
            batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
        # This will build the current layer
        # and create the node connecting the current layer
        # to the input layer we just created.
        layer(x)
        set_inputs = True

    if set_inputs:
      outputs = tf.nest.flatten(layer._inbound_nodes[-1].outputs)
      if len(outputs) != 1:
        raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
      self.outputs = outputs
      self.inputs = layer_utils.get_source_inputs(self.outputs[0])
      self.built = True
      self._has_explicit_input_shape = True

  elif self.outputs:
    # If the model is being built continuously on top of an input layer:
    # refresh its output.
    output_tensor = layer(self.outputs[0])
    if len(tf.nest.flatten(output_tensor)) != 1:
      raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
    self.outputs = [output_tensor]
    self.built = True

  if set_inputs or self._graph_initialized:
    self._init_graph_network(self.inputs, self.outputs)
    self._graph_initialized = True
  else:
    self._self_tracked_trackables.append(layer)
    self._handle_deferred_layer_dependencies([layer])

  self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)
def pop(self)

Removes the last layer in the model.

Raises

TypeError
if there are no layers in the model.
Expand source code
@tf.__internal__.tracking.no_automatic_dependency_tracking
def pop(self):
  """Removes the last layer in the model.

  Raises:
      TypeError: if there are no layers in the model.
  """
  if not self.layers:
    raise TypeError('There are no layers in the model.')

  layer = self._self_tracked_trackables.pop()
  self._layer_call_argspecs.pop(layer)
  if not self.layers:
    self.outputs = None
    self.inputs = None
    self.built = False
    self._inferred_input_shape = None
    self._has_explicit_input_shape = False
    self._graph_initialized = False
  elif self._graph_initialized:
    self.layers[-1]._outbound_nodes = []
    self.outputs = [self.layers[-1].output]
    self._init_graph_network(self.inputs, self.outputs)
    self.built = True

Inherited members