Model training APIs

Model training APIs

compile method

 model. roll up  (
     optimizer = `` rmsprop '' ,
     loss = none ,
     metrics = none ,
     loss_weights = none ,
     weighted_metrics = none ,
     run_eagerly = none ,
     steps_per_execution = none ,
     jit_compile = none ,
     ** kwargs
 )

Configures the exemplar for train .
Example

 model. roll up ( optimizer = tf. keras. optimizers. adam ( learning_rate = 1e-3 ) ,
               loss = tf. keras. losses. BinaryCrossentropy ( ) ,
               metrics = [ tf. keras. metrics. BinaryAccuracy ( ) ,
                        tf. keras. metrics. FalseNegatives ( ) ] )

Arguments

  • optimizer: String (name of optimizer) or optimizer instance. See
    tf.keras.optimizers.
  • loss: Loss function. May 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).
  • jit_compile: If True, compile the model training step with XLA.
    XLA is an optimizing compiler for
    machine learning.
    jit_compile is not enabled for by default.
    This option cannot be enabled with run_eagerly=True.
    Note that jit_compile=True is
    may not necessarily work for all models.
    For more information on supported operations please refer to the
    XLA documentation.
    Also refer to
    known XLA issues for
    more details.
  • **kwargs: Arguments supported for backwards compatibility only.

fit method

 exemplary. match (
     x = none ,
     yttrium = none ,
     batch_size = none ,
     era = 1 ,
     long-winded = `` car '' ,
     callbacks = none ,
     validation_split = 0.0 ,
     validation_data = none ,
     shamble = 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 era ( iterations on a dataset ) .
Arguments

  • 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 park convention is to pass a tf.data.Dataset, generator, or tf.keras.utils.Sequence to the x argument of meet, which will in fact give way not lone features ( x ) but optionally targets ( yttrium ) and sample weights. Keras requires that the output signal of such iterator-likes be unequivocal. The iterator should return a tuple of duration 1, 2, or 3, where the optional second and one-third elements will be used for y and sample_weight respectively. Any early character provided will be wrapped in a distance one tuple, efficaciously treating everything as ‘x ‘. When yielding dicts, they should inactive adhere to the top-level tuple social organization. e.g. ({"x0": x0, "x1": x1}, y). Keras will not attempt to separate features, targets, and weights from the keys of a one dict. A celebrated unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype ( tuple ) and a map 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 resultant role the data serve code will merely raise a ValueError if it encounters a namedtuple. ( Along with instructions to remedy the issue. )
Returns
A History object. Its History.history impute is a criminal record of training loss values and metrics values at consecutive epoch, a well as establishment loss values and establishment 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.

evaluate method

 model. measure (
     x =  none ,
     yttrium = none ,
     batch_size = none ,
     long-winded = `` car '' ,
     sample_weight = none ,
     steps = none ,
     callbacks = none ,
     max_queue_size = 10 ,
     workers = 1 ,
     use_multiprocessing = faithlessly ,
     return_dict = false ,
     ** kwargs
 )

Returns the loss measure & metrics values for the model in quiz modality .
calculation is done in batches ( see the batch_size arg. )
Arguments

  • 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.
  • 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 screen loss ( if the model has a individual output signal and no metrics ) or tilt of scalars ( if the mannequin has multiple outputs and/or metrics ). The impute model.metrics_names will give you the display labels for the scalar outputs .
Raises

  • RuntimeError: If model.evaluate is wrapped in a tf.function.

predict method

 exemplar. predict (
     adam ,
     batch_size = none ,
     long-winded = `` car '' ,
     steps = none ,
     callbacks = none ,
     max_queue_size = 10 ,
     workers = 1 ,
     use_multiprocessing = false ,
 )

Generates end product predictions for the input samples .
calculation is done in batches. This method is designed for batch work of big numbers of inputs. It is not intended for use inside of loops that iterate over your data and serve small numbers of inputs at a time .
For small numbers of inputs that fit in one batch, immediately use __call__() for faster murder, for example, model(x), or model(x, training=False) if you have layers such as tf.keras.layers.BatchNormalization that behave differently during inference. You may pair the individual model call with a tf.function for extra performance inside your inner loop. If you need access to numpy array values alternatively of tensors after your model call, you can use tensor.numpy() to get the numpy array respect of an tidal bore tensor .
besides, note the fact that examination personnel casualty is not affected by regularization layers like noise and dropout .
note : See this FAQ entry for more details about the deviation between Model methods predict() and __call__() .
Arguments

  • 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: "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).
  • 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.
  • 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 lapp rendition rules as Model.fit and Model.evaluate, then stimulation must be unambiguous for all three methods .
Returns
Numpy array ( mho ) of predictions .
Raises

  • RuntimeError: If model.predict is wrapped in a 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.

train_on_batch method

 model. train_on_batch (
     ten ,
     y = none ,
     sample_weight = none ,
     class_weight = none ,
     reset_metrics = true ,
     return_dict = fake ,
 )

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

  • 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).
  • 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 educate passing ( if the model has a single output signal 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 a tf.function.

test_on_batch method

 model. test_on_batch (
     x,  yttrium = none,  sample_weight = none,  reset_metrics = true,  return_dict = fake
 )

Test the model on a single batch of samples .
Arguments

  • 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 individual 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 a tf.function.

predict_on_batch method

 model. predict_on_batch ( x )

Returns predictions for a one batch of samples .
Arguments

  • 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 ( south ) of predictions .
Raises

  • RuntimeError: If model.predict_on_batch is wrapped in a tf.function.

run_eagerly property

 tf. keras. model. run_eagerly

Settable property indicating whether the model should run eagerly .
Running eagerly means that your model will be run step by step, like Python code. Your exemplary 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 exemplar to a static graph to deliver the best performance performance .
Returns
Boolean, whether the model should run eagerly .

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