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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)
__getitem____getitem__(
index
)
Gets batch at position index.
index: position of the batch in the Sequence.A batch
__iter____iter__()
Create a generator that iterate over the Sequence.
__len____len__()
Number of batch in the Sequence.
The number of batches in the Sequence.
on_epoch_endon_epoch_end()
Method called at the end of every epoch.