models.py 文件源码

def create_model(self, height=32, width=32, channels=3, load_weights=False, batch_size=128):
        """
            Creates a model to be used to scale images of specific height and width.
        """
        init = super(ExpantionSuperResolution, self).create_model(height, width, channels, load_weights, batch_size)

        x = Convolution2D(self.n1, self.f1, self.f1, activation='relu', border_mode='same', name='level1')(init)

        x1 = Convolution2D(self.n2, self.f2_1, self.f2_1, activation='relu', border_mode='same', name='lavel1_1')(x)
        x2 = Convolution2D(self.n2, self.f2_2, self.f2_2, activation='relu', border_mode='same', name='lavel1_2')(x)
        x3 = Convolution2D(self.n2, self.f2_3, self.f2_3, activation='relu', border_mode='same', name='lavel1_3')(x)

        x = merge([x1, x2, x3], mode='ave')

        out = Convolution2D(channels, self.f3, self.f3, activation='relu', border_mode='same', name='output')(x)

        model = Model(init, out)
        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