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# create the base pre-trained modelinput_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_modelimg1 = 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 functionscategory_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'])

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