L8.py 文件源码

def create_model(img_height,img_width,img_channel):
    ip = Input(shape=(img_height, img_width,img_channel))
    L1 = Conv2D(32, (11, 11), padding='same', activation='relu', kernel_initializer='glorot_uniform')(ip)
    L2 = Conv2D(64, (3, 3), padding='same', activation='relu', kernel_initializer='glorot_uniform')(L1)
    L3 = Conv2D(64, (3, 3), padding='same', activation='relu', kernel_initializer='glorot_uniform')(L2)
    L4 = Conv2D(64, (3, 3), padding='same', activation='relu', kernel_initializer='glorot_uniform')(L3)
    L4=concatenate([L4,L1],axis=-1)#Attention!.maybe this connection will influence the result,which means it can be moved.
    L5 = Conv2D(64, (1, 1), padding='same', activation='relu', kernel_initializer='glorot_uniform')(L4)
    L6 = Conv2D(64, (5, 5), padding='same', activation='relu', kernel_initializer='glorot_uniform')(L5)
    L6=concatenate([L6,L1],axis=-1)#Attention!.maybe this connection will influence the result,which means it can be moved.
    L7 = Conv2D(128, (1, 1), padding='same', activation='relu', kernel_initializer='glorot_uniform')(L6)
    L8 = Conv2D(img_channel, (5, 5), padding='same', activation='relu', kernel_initializer='glorot_uniform')(L7)
    deblocking =Model(inputs=ip,outputs= L8)
    optimizer = optimizers.Adam(lr=1e-4)
    deblocking.compile(optimizer=optimizer,loss='mean_squared_error', metrics=[psnr,ssim])
    return deblocking