SRResNet.py 文件源码

def create_model(img_height,img_width,img_channel):
    ip = Input(shape=(img_height, img_width,img_channel))
    x_1 = Conv2D(64, (9, 9), padding='same', activation='linear', kernel_initializer='glorot_uniform')(ip)
    x_1 = LeakyReLU(alpha=0.25)(x_1)
    x=x_1
    for i in range(5):#or 15
        x = residual_block(x, 64,3)

    x = Conv2D(64, (3, 3), padding='same',kernel_initializer='glorot_uniform')(x)
    x = BatchNormalization(axis=-1)(x)
    x = add([x_1,x])

    x=upscale(x)
    op = Conv2D(img_channel, (9, 9),padding='same', activation='tanh', kernel_initializer='glorot_uniform')(x)

    deblocking =Model(inputs=ip,outputs= op)
    optimizer = optimizers.Adam(lr=1e-4)
    deblocking.compile(optimizer=optimizer,loss='mean_squared_error', metrics=[psnr,ssim])
    return deblocking