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tf.keras.preprocessing.image.NumpyArrayIterator

TensorFlow 2.0 version View source on GitHub

Class NumpyArrayIterator

Iterator yielding data from a Numpy array.

Inherits From: Iterator

Aliases:

  • Class tf.compat.v1.keras.preprocessing.image.NumpyArrayIterator
  • Class tf.compat.v2.keras.preprocessing.image.NumpyArrayIterator

Arguments:

  • x: Numpy array of input data or tuple. If tuple, the second elements is either another numpy array or a list of numpy arrays, each of which gets passed through as an output without any modifications.
  • y: Numpy array of targets data.
  • image_data_generator: Instance of ImageDataGenerator to use for random transformations and normalization.
  • batch_size: Integer, size of a batch.
  • shuffle: Boolean, whether to shuffle the data between epochs.
  • sample_weight: Numpy array of sample weights.
  • seed: Random seed for data shuffling.
  • data_format: String, one of channels_first, channels_last.
  • save_to_dir: Optional directory where to save the pictures being yielded, in a viewable format. This is useful for visualizing the random transformations being applied, for debugging purposes.
  • save_prefix: String prefix to use for saving sample images (if save_to_dir is set).
  • save_format: Format to use for saving sample images (if save_to_dir is set).
  • subset: Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.
  • dtype: Dtype to use for the generated arrays.

__init__

View source

__init__(
    x,
    y,
    image_data_generator,
    batch_size=32,
    shuffle=False,
    sample_weight=None,
    seed=None,
    data_format=None,
    save_to_dir=None,
    save_prefix='',
    save_format='png',
    subset=None,
    dtype=None
)

Methods

__getitem__

__getitem__(idx)

__iter__

__iter__()

__len__

__len__()

next

next()

For python 2.x.

Returns

The next batch.

on_epoch_end

on_epoch_end()

reset

reset()

Class Members

  • white_list_formats