tf.keras.preprocessing.image.NumpyArrayIterator

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Iterator yielding data from a Numpy array.

Inherits From: Iterator

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

__getitem__

__getitem__(
    idx
)

Gets batch at position index.

Arguments:

• index: position of the batch in the Sequence.

Returns:

A batch

__iter__

__iter__()

Create a generator that iterate over the Sequence.

__len__

__len__()

Number of batch in the Sequence.

Returns:

The number of batches in the Sequence.

next

next()

For python 2.x.

Returns

The next batch.

on_epoch_end

on_epoch_end()

Method called at the end of every epoch.

reset

reset()

Class Variables

• white_list_formats