目录

fit函数的定义在哪里？

fit函数的参数

x

y

epochs

evaluate函数的定义在哪里？

evaluate函数的参数

fit函数源码

evaluate函数源码

---

TensorFlow 2.9的零零碎碎（二）-TensorFlow 2.9的零零碎碎（六）都是围绕使用TensorFlow 2.9在MNIST数据集上训练和评价模型来展开。

Python环境3.8。

代码调试都用的PyCharm。

在构建好数据、模型，完成模型编译之后，接下来的就是模型训练，使用fit函数，以及模型评价，使用evaluate函数。

import tensorflow as tf
 
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
 
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Input(shape=(28, 28)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
 
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['acc'])
 
model.fit(x_train, y_train, epochs=5)
 
model.evaluate(x_test, y_test, verbose=2)

 其实代码写到这，也就写完了，看看代码的运行结果

fit函数的定义在哪里？

通过model = tf.keras.models.Sequential()，我们知道model是Sequential类的一个对象，所以我们去Sequential类里看看

层层跳转，在keras.engine.sequential找到Sequential类的定义

@keras_export('keras.Sequential', 'keras.models.Sequential')
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.

可以看出，Sequential类里并没有fit函数

但是我们发现Sequential类继承了functional模块的Functional类，继续跳转

# pylint: disable=g-classes-have-attributes
class Functional(training_lib.Model):
  """A `Functional` model is a `Model` defined as a directed graph of layers.
  Three types of `Model` exist: subclassed `Model`, `Functional` model,
  and `Sequential` (a special case of `Functional`).
  In general, more Keras features are supported with `Functional`
  than with subclassed `Model`s, specifically:
  - Model cloning (`keras.models.clone`)
  - Serialization (`model.get_config()/from_config`, `model.to_json()`
  - Whole-model saving (`model.save()`)
  A `Functional` model can be instantiated by passing two arguments to
  `__init__`. The first argument is the `keras.Input` Tensors that represent
  the inputs to the model. The second argument specifies the output
  tensors that represent the outputs of this model. Both arguments can be a
  nested structure of tensors.

同样，Functional类里并没有fit函数

但是我们发现Functional类继承了training模块的Model类

注意不是training_lib模块，因为training_lib只是一个别名（from keras.engine import training as training_lib）

继续跳转

@keras_export('keras.Model', 'keras.models.Model')
class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  """`Model` groups layers into an object with training and inference features.

在training模块的Model类中定义了fit函数。源码太长了，这里给一部分，全部的版本贴在最后。

  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).

fit函数的参数

fit函数的参数比较多，但在上面我们训练模型的代码里其实就用了3个参数，分别是x（输入的数据）、y（输入数据的标签）、epochs（训练的轮数）。

x

x也就是输入的数据，可以是多种类型，比如Numpy数组、TensorFlow张量。

        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`.

TensorFlow前面的版本中，还有一个函数叫fit_generator。传进去的不是数组或者张量，而是生成器，生成器里会同时生成输入数据和输入数据的标签，主要用于资源有限（如GPU资源）、数据量比较大的情况，将大规模的数据切片，分别传入进行模型训练。

  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.

但在TensorFlow 2.9中，fit_generator函数已经被弃用了，这是因为fit函数将fit_generator函数的功能集成了进去。

现在的fit函数的x参数一样接受生成器作为参数。

y

y是输入数据的标签，数据类型和x类似，比如可以是Numpy数组或者TensorFlow张量，但y的数据类型需要和x一致

比如x是Numpy数组，则y也必须是Numpy数组

如果x是一个生成器，由于生成器里会同时生成输入数据和输入数据的标签，所以这里的y应该留空（不指定）

        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`).

epochs

epochs是训练的轮数，也有用“迭代数”这个词的

如果不设置steps_per_epoch参数，则一个epoch（轮）指完整的在x和y的所有数据上训练一次

        epochs: Integer. Number of epochs to train the model.
            An epoch is an iteration over the entire `x` and `y`
            data provided
            (unless the `steps_per_epoch` flag is set to
            something other than None).
            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.

evaluate函数的定义在哪里？

和fit函数一样的原理，在training模块下可以找到。

evaluate函数的参数

evaluate函数的参数和fit函数的参数类似

  @traceback_utils.filter_traceback
  def evaluate(self,
               x=None,
               y=None,
               batch_size=None,
               verbose='auto',
               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.)

在上面我们评价模型的代码里 ，有一个verbose=2

这个参数在fit函数中也有，用于控制打印过程信息

verbose参数有4个取值，auto、0、1、2，默认值是auto

verbose=2指的是逐行打印

这个自己试试看看效果就很清楚了

        verbose: `"auto"`, 0, 1, or 2. Verbosity mode.
            0 = silent, 1 = progress bar, 2 = single line.
            `"auto"` defaults to 1 for most cases, and to 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 (e.g. in a production
            environment).

fit函数源码

  @traceback_utils.filter_traceback
  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
            (unless the `steps_per_epoch` flag is set to
            something other than None).
            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.
            If both `validation_data` and `validation_split` are provided,
            `validation_data` will override `validation_split`.
            `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')
 
    verbose = _get_verbosity(verbose, self.distribute_strategy)
 
    if validation_split and validation_data is None:
      # 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():
          data_handler._initial_step = self._maybe_load_initial_step_from_ckpt()  # pylint: disable=protected-access
          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('Unexpected result of `train_function` '
                           '(Empty logs). Please use '
                           '`Model.compile(..., run_eagerly=True)`, or '
                           '`tf.config.run_functions_eagerly(True)` for more '
                           'information of where went wrong, or file a '
                           'issue/bug to `tf.keras`.')
        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 isinstance(self.optimizer, optimizer_experimental.Optimizer):
        self.optimizer.finalize_variable_values(self.trainable_variables)
 
      # If eval data_handler 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

evaluate函数源码

  @traceback_utils.filter_traceback
  def evaluate(self,
               x=None,
               y=None,
               batch_size=None,
               verbose='auto',
               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: `"auto"`, 0, 1, or 2. Verbosity mode.
            0 = silent, 1 = progress bar, 2 = single line.
            `"auto"` defaults to 1 for most cases, and to 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 (e.g. in a production
            environment).
        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`.
    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 a `tf.function`.
    """
    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')
    self._check_sample_weight_warning(x, sample_weight)
    _disallow_inside_tf_function('evaluate')
    use_cached_eval_dataset = kwargs.pop('_use_cached_eval_dataset', False)
    if kwargs:
      raise TypeError(f'Invalid keyword arguments: {list(kwargs.keys())}')
 
    if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
      self._cluster_coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
          self.distribute_strategy)
 
    verbose = _get_verbosity(verbose, 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)