tf.keras.utils.Sequence

Base object for fitting to a sequence of data, such as a dataset.

Every Sequence must implement the __getitem__ and the __len__ methods. If you want to modify your dataset between epochs you may implement on_epoch_end. The method __getitem__ should return a complete batch.

Notes:

Sequence are a safer way to do multiprocessing. This structure guarantees that the network will only train once on each sample per epoch which is not the case with generators.

Examples:

from skimage.io import imread
from skimage.transform import resize
import numpy as np
import math

# Here, `x_set` is list of path to the images
# and `y_set` are the associated classes.

class CIFAR10Sequence(Sequence):

    def __init__(self, x_set, y_set, batch_size):
        self.x, self.y = x_set, y_