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')
