TensorFlow 1 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
)
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
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. |