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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.
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.
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)
on_epoch_end
on_epoch_end()
Method called at the end of every epoch.
__getitem__
__getitem__( index )
Gets batch at position index
.
Arguments | |
---|---|
index | position of the batch in the Sequence. |
Returns | |
---|---|
A batch |
__iter__
__iter__()
Create a generator that iterate over the Sequence.
__len__
__len__()
Number of batch in the Sequence.
Returns | |
---|---|
The number of batches in the Sequence. |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/utils/Sequence