tf.keras.preprocessing.image.NumpyArrayIterator

TensorFlow 2 version View source on GitHub

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
)

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

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()

__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.

Class Variables

  • white_list_formats