ifresnel.py 文件源码

def modeling(self):
        Lx, Ly = self.Lx, self.Ly
        input_img = Input(shape=(1, Lx, Ly))
        ks = 8

        x = Convolution2D(16, ks*2, ks*2, activation='relu', border_mode='same')(input_img)
        x = MaxPooling2D((2, 2), border_mode='same')(x)  # 160 --> 80
        x = Convolution2D(8, ks*2, ks*2, activation='relu', border_mode='same')(x)
        x = MaxPooling2D((2, 2), border_mode='same')(x)  # 80 --> 40
        x = Convolution2D(8, ks*2, ks*2, activation='relu', border_mode='same')(x)
        encoded = MaxPooling2D((2, 2), border_mode='same')(x) # 40 --> 20

        # at this point the representation is (8, 20, 20) 

        x = Convolution2D(8, ks, ks, activation='relu', border_mode='same')(encoded)
        x = UpSampling2D((2, 2))(x) # 20 --> 40
        x = Convolution2D(8, ks, ks, activation='relu', border_mode='same')(x)
        x = UpSampling2D((2, 2))(x) # 20 --> 80
        x = Convolution2D(16, ks, ks, activation='relu', border_mode='same')(x)
        x = UpSampling2D((2, 2))(x) # 80 --> 160
        decoded = Convolution2D(1, ks, ks, activation='sigmoid', border_mode='same')(x)

        autoencoder = Model(input_img, decoded)
        autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

        self.autoencoder = autoencoder