tf.keras.preprocessing.image.DirectoryIterator

Class DirectoryIterator

Inherits From: Iterator

Defined in tensorflow/python/keras/_impl/keras/preprocessing/image.py.

Iterator capable of reading images from a directory on disk.

Arguments:

• directory: Path to the directory to read images from. Each subdirectory in this directory will be considered to contain images from one class, or alternatively you could specify class subdirectories via the classes argument.
• image_data_generator: Instance of ImageDataGenerator to use for random transformations and normalization.
• target_size: tuple of integers, dimensions to resize input images to.
• color_mode: One of "rgb", "grayscale". Color mode to read images.
• classes: Optional list of strings, names of sudirectories containing images from each class (e.g. ["dogs", "cats"]). It will be computed automatically if not set.
• class_mode: Mode for yielding the targets: "binary": binary targets (if there are only two classes), "categorical": categorical targets, "sparse": integer targets, "input": targets are images identical to input images (mainly used to work with autoencoders), None: no targets get yielded (only input images are yielded).
• batch_size: Integer, size of a batch.
• shuffle: Boolean, whether to shuffle the data between epochs.
• 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).

Methods

__init__

__init__(
    directory,
    image_data_generator,
    target_size=(256, 256),
    color_mode='rgb',
    classes=None,
    class_mode='categorical',
    batch_size=32,
    shuffle=True,
    seed=None,
    data_format=None,
    save_to_dir=None,
    save_prefix='',
    save_format='png',
    follow_links=False
)

__iter__

__iter__()

__next__

__next__(
    *args,
    **kwargs
)

next

next()

For python 2.x.

Returns:

The next batch.

reset

reset()