standard_sequence_testing.py

import numpy as np

from keras.layers import Input, Lambda
from keras.models import Model
from keras.utils import Sequence


class SSEquence(Sequence):
    def __init__(self, value=1):
        self.value = value

    def __len__(self):
        return 100

    def __getitem__(self, item):
        return [np.zeros((30, 20, 20, 3)), np.zeros((30, 20, 20, 3))], np.ones((30, 20, 20, 3)) * self.value

    def on_epoch_end(self):
        self.value *= 2

# Training loss would be 0 and validation loss would always go higher.
inp = Input([20, 20, 3])
inp2 = Input([20, 20, 3])
o = Lambda(lambda i: i[0] + i[1])([inp, inp2])
mod = Model([inp, inp2], o)
mod.compile('adam', 'mse')
mod.fit_generator(SSEquence(0), 100, epochs=10, use_multiprocessing=True, validation_data=SSEquence(2),
                  validation_steps=100)