在keras实现rcf网络时，需要将几个中间层输出计算loss，
在这个链接https://blog.csdn.net/u012938704/article/details/79904173提供到了一种方法方法，
这里只摘取关键代码：
# create the base pre-trained model
input_tensor = Input(shape=(299, 299, 3))
base_model = Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
plot_model(base_model, to_file='xception_model.png')
base_model.layers.pop()
base_model.outputs = [base_model.layers[-1].output]
base_model.layers[-1].outbound_nodes = []
base_model.output_layers = [base_model.layers[-1]]
feature = base_model
img1 = Input(shape=(299, 299, 3), name='img_1')
img2 = Input(shape=(299, 299, 3), name='img_2')
feature1 = feature(img1)
feature2 = feature(img2)
# Three loss functions
category_predict1 = Dense(100, activation='softmax', name='ctg_out_1')(
Dropout(0.5)(feature1)
)
category_predict2 = Dense(100, activation='softmax', name='ctg_out_2')(
Dropout(0.5)(feature2)
)
dis = Lambda(eucl_dist, name='square')([feature1, feature2])
model = Model(inputs=[img1, img2], outputs=[category_predict1, category_predict2, judge])
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
loss={
'ctg_out_1': 'categorical_crossentropy',
'ctg_out_2': 'categorical_crossentropy',
'bin_out': 'categorical_crossentropy'},
loss_weights={
'ctg_out_1': 1.,
'ctg_out_2': 1.,
'bin_out': 0.5
},
metrics=['accuracy'])
最终验证这种方法不可行。
在github issue里面，找到了下面这种实现方法，最终验证可行。
def prepare_model():
inputs = Input(shape=(100,100,3))
...
fc = Dense(100)(#previousLayer#)
softmax = Softmax(fc)
def custom_loss(y_true, y_pred):
loss1=softmax(y_true,y_pred)
loss2=center_loss(y_true,fc)
return loss1+lambda*loss2
model = Model(input, output=softmax)
model.compile(optimizer='sgd',
loss=custom_loss,
metrics=['accuracy'])
return model