Function model_iteration

tensorflow/python/keras/engine/training_arrays.py:46–467

Loop function for arrays of data with modes TRAIN/TEST/PREDICT. Arguments: model: Keras Model instance. inputs: Either a list or dictionary of arrays, or a dataset instance. targets: List/dictionary of input arrays. sample_weights: Optional list of sample weight arrays.

(model,
                    inputs,
                    targets=None,
                    sample_weights=None,
                    batch_size=None,
                    epochs=1,
                    verbose=1,
                    callbacks=None,
                    val_inputs=None,
                    val_targets=None,
                    val_sample_weights=None,
                    shuffle=True,
                    initial_epoch=0,
                    steps_per_epoch=None,
                    validation_steps=None,
                    validation_freq=1,
                    mode=ModeKeys.TRAIN,
                    validation_in_fit=False,
                    prepared_feed_values_from_dataset=False,
                    steps_name='steps',
                    **kwargs)

def model_iteration(model,
                    inputs,
                    targets=None,
                    sample_weights=None,
                    batch_size=None,
                    epochs=1,
                    verbose=1,
                    callbacks=None,
                    val_inputs=None,
                    val_targets=None,
                    val_sample_weights=None,
                    shuffle=True,
                    initial_epoch=0,
                    steps_per_epoch=None,
                    validation_steps=None,
                    validation_freq=1,
                    mode=ModeKeys.TRAIN,
                    validation_in_fit=False,
                    prepared_feed_values_from_dataset=False,
                    steps_name='steps',
                    **kwargs):
  """Loop function for arrays of data with modes TRAIN/TEST/PREDICT.

  Arguments:
      model: Keras Model instance.
      inputs: Either a list or dictionary of arrays, or a dataset instance.
      targets: List/dictionary of input arrays.
      sample_weights: Optional list of sample weight arrays.
      batch_size: Integer batch size or None if unknown.
      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
      val_inputs: Either a list or dictionary of arrays, or a dataset instance.
      val_targets: List/dictionary of target arrays.
      val_sample_weights: Optional list of sample weight arrays.
      shuffle: Whether to shuffle the data at the beginning of each epoch
        concatenation of list the display names of the outputs of `f` and the
        list of display names of the outputs of `f_val`.
      initial_epoch: Epoch at which to start training (useful for resuming a
        previous training run)
      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`.
      validation_steps: Number of steps to run validation for (only if doing
        validation from data tensors). Ignored with the default value of
        `None`.
      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.
      mode: One of ModeKeys.TRAIN/ModeKeys.TEST/ModeKeys.PREDICT.