Function model_iteration

tensorflow/python/keras/engine/training_generator.py:41–349

Loop function for arrays of data with modes TRAIN/TEST/PREDICT. Arguments: model: Keras Model instance. data: Either a tuple of NumPy/Tensor inputs (i.e. `(x,)` or `(x, y)` or `(x, y, sample_weights)`) or a generator or `keras.utils.data_utils.Sequence` object or Eager

(model,
                    data,
                    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=False,
                    initial_epoch=0,
                    mode=ModeKeys.TRAIN,
                    batch_size=None,
                    steps_name='steps',
                    **kwargs)

def model_iteration(model,
                    data,
                    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=False,
                    initial_epoch=0,
                    mode=ModeKeys.TRAIN,
                    batch_size=None,
                    steps_name='steps',
                    **kwargs):
  """Loop function for arrays of data with modes TRAIN/TEST/PREDICT.

  Arguments:
      model: Keras Model instance.
      data: Either a tuple of NumPy/Tensor inputs (i.e. `(x,)` or `(x, y)` or
        `(x, y, sample_weights)`) or a generator or
        `keras.utils.data_utils.Sequence` object or Eager Iterator or Dataset.
      steps_per_epoch: Total number of steps (batches of samples) before
        declaring one epoch finished and starting the next epoch. Ignored with
        the default value of `None`.
      epochs: Number of times to iterate over the data.
      verbose: 0, 1, or 2. Verbosity mode.
        0 = silent, 1 = progress bar, 2 = one line per epoch.
        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 callbacks to be called during training.
      validation_data: Either a tuple of NumPy/Tensor inputs (i.e. `(x,)` or
        `(x, y)` or `(x, y, sample_weights)`) or a generator or
        `keras.utils.data_utils.Sequence` object or Eager Iterator or Dataset.
      validation_steps: Total number of steps (batches of samples) before
        declaring validation finished.
      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.
      class_weight: Dictionary mapping class indices to a weight for the class.
      max_queue_size: Integer. Maximum size for the generator queue. If
        unspecified, `max_queue_size` will default to 10.
      workers: Integer. Maximum number of processes to spin up when using
        process-based threading. If unspecified, `workers` will default to 1. If
        0, will execute the generator on the main thread.
      use_multiprocessing: Boolean. 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