tf.keras.utils.Sequence

Class Sequence

Defined in tensorflow/python/keras/_impl/keras/utils/data_utils.py.

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

Every Sequence must implements 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

__getitem__

__getitem__(index)

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.

on_epoch_end

on_epoch_end()

Method called at the end of every epoch.