tf.keras.utils.Sequence

TensorFlow 1 version View source on GitHub

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_set
            self.batch_size = batch_size

        def __len__(self):
            return math.ceil(len(self.x) / self.batch_size)

        def __getitem__(self, idx):
            batch_x = self.x[idx * self.batch_size:(idx + 1) *
            self.batch_size]
            batch_y = self.y[idx * self.batch_size:(idx + 1) *
            self.batch_size]

            return np.array([
                resize(imread(file_name), (200, 200))
                   for file_name in batch_x]), np.array(batch_y)

Methods

on_epoch_end

View source

on_epoch_end()

Method called at the end of every epoch.

__getitem__

View source

__getitem__(
    index
)

Gets batch at position index.

Arguments
index position of the batch in the Sequence.

Returns
A batch

__iter__

View source

__iter__()

Create a generator that iterate over the Sequence.

__len__

View source

__len__()

Number of batch in the Sequence.

Returns
The number of batches in the Sequence.