brain_seg_DCNN.py 文件源码

def two_blocks_dcnn(self):
        """
        Method to model and compile the first CNN and the whole two blocks DCNN.
        Also initialize the field cnn1
        :return: Model, Two blocks DeepCNN compiled
        """
        # input layers
        input65 = Input(shape=(65, 65, 4))
        input33 = Input(shape=(33, 33, 4))
        # 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)
        # 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 'DCNN compiled!'
        return dcnn