[bug] keras.fit_generator(Sequence)

gistfile1.txt

import numpy as np
from keras.models import Model, Sequential
from keras.layers import Input, Dense, Activation
from keras.utils.data_utils import Sequence
from keras.losses import categorical_crossentropy
from keras.optimizers import SGD

class CustomSequence(Sequence):
    def __init__(self, x_set, y_set, batch_size):
        self.X,self.y = x_set,y_set
        self.batch_size = batch_size
        self.counter = 0
        self.epoch_counter = 0

    def __len__(self):
        return len(self.X) // self.batch_size

    def __getitem__(self,idx):
        print '\nBatch #: ' + str(idx)
        print 'From ' + str(idx*self.batch_size) + ' to ' + str((idx+1)*self.batch_size)
        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]
        print 'Returned batch size: ' + str(len(batch_x))
        self.counter += 1
        
        return np.array(batch_x), np.array(batch_y)
        
    def on_epoch_end(self):
        """Method called at the end of every epoch.
        """
        print('\nEpoch end: ' + str(self.epoch_counter) + ' counter: ' + str(self.counter))
        self.epoch_counter +=1
        
model = Sequential()
model.add(Dense(units=5, input_dim=3))
model.add(Activation('relu'))
model.add(Dense(units=2))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])
              
x_train = np.random.rand(20,3)
y_train = np.random.randint(2, size=20)
y_train = np.vstack((y_train, 1-y_train)).T

print x_train.shape
print y_train.shape

batch_size = 4
epochs = 3

train_generator = CustomSequence(x_train, y_train, batch_size)
model.fit_generator(
    train_generator,
    steps_per_epoch = 5,
    epochs = epochs,
    use_multiprocessing=False,
    workers=1,
    verbose=0
)