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 subdirectories 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).
• subset: Subset of data ("training" or "validation") if validation_split is set in ImageDataGenerator.
• interpolation: Interpolation method used to resample the image if the target size is different from that of the loaded image. Supported methods are "nearest", "bilinear", and "bicubic". If PIL version 1.1.3 or newer is installed, "lanczos" is also supported. If PIL version 3.4.0 or newer is installed, "box" and "hamming" are also supported. By default, "nearest" is used.

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,
    subset=None,
    interpolation='nearest'
)

Initialize self. See help(type(self)) for accurate signature.

__getitem__

__getitem__(idx)

Gets batch at position index.

Arguments:

• index: position of the batch in the Sequence.

Returns:

A batch

__iter__

__iter__()

Creates an infinite generator that iterate over the Sequence.

Yields:

Sequence items.

__len__

__len__()

Number of batch in the Sequence.

Returns:

The number of batches in the Sequence.

__next__

__next__(
    *args,
    **kwargs
)

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()