BrainSegDCNN_2.py 文件源码

def compile_model(self):
        """
        Model and compile the first CNN and the whole two blocks DCNN.
        Also initialize the field cnn1
        :return: Model, Two blocks DeepCNN compiled
        """
        if self.cascade_model:
            # input layers
            input65 = Input(shape=(4, 65, 65))
            input33 = Input(shape=(4, 33, 33))
            # first CNN modeling
            output_cnn1 = self.one_block_model(input65)
            # first cnn compiling
            cnn1 = Model(inputs=input65, outputs=output_cnn1)
            sgd = SGD(lr=self.learning_rate, momentum=self.momentum_rate, decay=self.decay_rate, nesterov=False)
            cnn1.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
            # initialize the field cnn1
            self.cnn1 = cnn1
            print 'First CNN compiled!'
            # concatenation of the output of the first CNN and the input of shape 33x33
            conc_input = Concatenate(axis=1)([input33, output_cnn1])
            # second cnn modeling
            output_dcnn = self.one_block_model(conc_input)
            output_dcnn = Reshape((5,))(output_dcnn)
            # whole dcnn compiling
            dcnn = Model(inputs=[input65, input33], outputs=output_dcnn)
            sgd = SGD(lr=self.learning_rate, momentum=self.momentum_rate, decay=self.decay_rate, nesterov=False)
            dcnn.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
            print 'Cascade DCNN compiled!'
            return dcnn
        else:
            # input layers
            input33 = Input(shape=(4, 33, 33))
            # first CNN modeling
            output_cnn1 = self.one_block_model(input33)
            # first cnn compiling
            cnn1 = Model(inputs=input33, outputs=output_cnn1)
            sgd = SGD(lr=self.learning_rate, momentum=self.momentum_rate, decay=self.decay_rate, nesterov=False)
            cnn1.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
            # initialize the field cnn1
            self.cnn1 = cnn1
            print 'Two pathway CNN compiled!'
            return cnn1