autoencoder_tensorflow_train.py

def train(self, input_train, input_test, batch_size, epochs):
        self._session.run(self._training)
        
        for epoch in range(epochs):
            epoch_loss = 0
            for i in range(int(input_train.shape[0]/batch_size)):
                epoch_input = input_train[ i * batch_size : (i + 1) * batch_size ]
                _, c = self._session.run([self._optimizer, self._meansq], feed_dict={self._input_layer: epoch_input, self._real_output: epoch_input})
                epoch_loss += c
                print('Epoch', epoch, '/', epochs, 'loss:',epoch_loss)