tf.keras.utils.Sequence

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

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.utils.Sequence, `tf.compat.v2.keras.utils.Sequence`

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

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on_epoch_end()

Method called at the end of every epoch.

__getitem__

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__getitem__(
    index
)

Gets batch at position index.

Arguments
index	position of the batch in the Sequence.

Returns
A batch

__iter__

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__iter__()

Create a generator that iterate over the Sequence.

__len__

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__len__()

Number of batch in the Sequence.

Returns
The number of batches in the Sequence.