GA.py 文件源码

def make_init_model(self):
        models = []

        input_data = Input(shape=self.gl_config.input_shape)
        import random
        init_model_index = random.randint(1, 4)
        init_model_index = 1
        if init_model_index == 1:  # one conv layer with kernel num = 64
            stem_conv_1 = Conv2D(128, 3, padding='same', name='conv2d1' )(input_data)
            stem_conv_1 = PReLU()(stem_conv_1)

        elif init_model_index == 2:  # two conv layers with kernel num = 64
            stem_conv_0 = Conv2D(128, 3, padding='same', name='conv2d1')(input_data)
            stem_conv_0 = PReLU()(stem_conv_0)
            stem_conv_1 = Conv2D(128, 3, padding='same', name='conv2d2')(stem_conv_0)
            stem_conv_1 = PReLU()(stem_conv_1)

        elif init_model_index == 3:  # one conv layer with a wider kernel num = 128
            stem_conv_1 = Conv2D(256, 3, padding='same', name='conv2d1')(input_data)
            stem_conv_1 = PReLU()(stem_conv_1)

        elif init_model_index == 4:  # two conv layers with a wider kernel_num = 128
            stem_conv_0 = Conv2D(256, 3, padding='same', name='conv2d1')(input_data)
            stem_conv_0 = PReLU()(stem_conv_0)
            stem_conv_1 = Conv2D(256, 3, padding='same', name='conv2d2')(stem_conv_0)
            stem_conv_1 = PReLU()(stem_conv_1)
        import keras
        stem_conv_1 = keras.layers.MaxPooling2D(name='maxpooling2d1')(stem_conv_1)
        stem_conv_1 = Conv2D(self.gl_config.nb_class, 3, padding='same', name='conv2d3')(stem_conv_1)
        stem_global_pooling_1 = GlobalMaxPooling2D(name='globalmaxpooling2d1')(stem_conv_1)
        stem_softmax_1 = Activation('softmax', name='activation1')(stem_global_pooling_1)

        model = Model(inputs=input_data, outputs=stem_softmax_1)

        return model