model.add_output(name='output1', input='layer5') print 'Training....' model.compile(loss={'output1': 'categorical_crossentropy'}, optimizer='adam', metrics=['accuracy']) model.fit({ 'n00': x_train, 'output1': y_train }, nb_epoch=nb_epoch, batch_size=batch_size, validation_split=0.3, shuffle=True, verbose=1) #Model result: loss_and_metrics = model.evaluate(x_train, y_train, batch_size=batch_size, verbose=1) print 'Done!' print 'Loss: ', loss_and_metrics[0] print ' Acc: ', loss_and_metrics[1] #Saving Model json_string = model.to_json() model.save_weights('emotion_weights_googleNet.h5') open('emotion_model_googleNet.json', 'w').write(json_string) model.save_weights('emotion_weights_googleNet.h5')