LostTech.TensorFlow : API Documentation

Type Sequence

Namespace tensorflow.keras.utils

Parent PythonObjectContainer

Interfaces ISequence

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.

Show Example
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)



Public instance methods

void on_epoch_end()

Method called at the end of every epoch.

object on_epoch_end_dyn()

Method called at the end of every epoch.

Public properties

ValueTuple<ndarray, object> Item get;

object PythonObject get;