Iterator yielding data from a Numpy array.
Inherits From: Iterator, Sequence
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
View source
next()
For python 2.x.
on_epoch_end
View source
on_epoch_end()
reset
View source
reset()
__getitem__
View source
__getitem__(
idx
)
__iter__
View source
__iter__()
__len__
View source
__len__()
Class Variables |
|---|
|
white_list_formats
|
|
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TensorFlow 1 version
View source on GitHub