tf.keras.preprocessing.image.NumpyArrayIterator

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Iterator yielding data from a Numpy array.

Inherits From: Iterator, Sequence

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.preprocessing.image.NumpyArrayIterator

tf.keras.preprocessing.image.NumpyArrayIterator(
    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
)

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.

Methods

next

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next()

For python 2.x.

Returns
The next batch.

on_epoch_end

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on_epoch_end()

reset

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reset()

__getitem__

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__getitem__(
    idx
)

__iter__

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__iter__()

__len__

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__len__()

Class Variables
white_list_formats