tf.keras.preprocessing.image.NumpyArrayIterator
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
next()
For python 2.x.
| Returns | |
|---|---|
| The next batch. |
on_epoch_end
on_epoch_end()
reset
reset()
__getitem__
__getitem__(
idx
)
__iter__
__iter__()
__len__
__len__()
| Class Variables | |
|---|---|
| white_list_formats | |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/preprocessing/image/NumpyArrayIterator