Training the model.

from keras.callbacks import ModelCheckpoint

# Creating the tensorflow session to train the model and save checkpoint after every epoch.
with tf.Session() as session:
  K.set_session(session)
  session.run(tf.global_variables_initializer())
  session.run(tf.tables_initializer())
  
  filepath="drive/My Drive/Colab Notebooks/Northout/model-{epoch:02d}-{val_acc:.2f}.hdf5"
  checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=True, mode='auto', period=1)
  callbacks_list = [checkpoint]

  history = model.fit([train_q1, train_q2], 
            train_labels,
            validation_data=([test_q1, test_q2], test_labels),
            epochs=10,
            batch_size=512, callbacks=callbacks_list)
#   model.save('drive/My Drive/Colab Notebooks/Northout/final_model.h5')