models.py 文件源码

def create_model(self, height=32, width=32, channels=3, load_weights=False, batch_size=128):
        # Perform check that model input shape is divisible by 4
        init = super(DeepDenoiseSR, self).create_model(height, width, channels, load_weights, batch_size)

        c1 = Convolution2D(self.n1, 3, 3, activation='relu', border_mode='same')(init)
        c1 = Convolution2D(self.n1, 3, 3, activation='relu', border_mode='same')(c1)

        x = MaxPooling2D((2, 2))(c1)

        c2 = Convolution2D(self.n2, 3, 3, activation='relu', border_mode='same')(x)
        c2 = Convolution2D(self.n2, 3, 3, activation='relu', border_mode='same')(c2)

        x = MaxPooling2D((2, 2))(c2)

        c3 = Convolution2D(self.n3, 3, 3, activation='relu', border_mode='same')(x)

        x = UpSampling2D()(c3)

        c2_2 = Convolution2D(self.n2, 3, 3, activation='relu', border_mode='same')(x)
        c2_2 = Convolution2D(self.n2, 3, 3, activation='relu', border_mode='same')(c2_2)

        m1 = merge([c2, c2_2], mode='sum')
        m1 = UpSampling2D()(m1)

        c1_2 = Convolution2D(self.n1, 3, 3, activation='relu', border_mode='same')(m1)
        c1_2 = Convolution2D(self.n1, 3, 3, activation='relu', border_mode='same')(c1_2)

        m2 = merge([c1, c1_2], mode='sum')

        decoded = Convolution2D(channels, 5, 5, activation='linear', border_mode='same')(m2)

        model = Model(init, decoded)
        adam = optimizers.Adam(lr=1e-3)
        model.compile(optimizer=adam, loss='mse', metrics=[PSNRLoss])
        if load_weights: model.load_weights(self.weight_path)

        self.model = model
        return model