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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.
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(tf.keras.utils.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
on_epoch_end()
Method called at the end of every epoch.
__getitem__
__getitem__(
index
)
Gets batch at position index.
| Args | |
|---|---|
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. |