SRResNet_v1.py 文件源码

def create_model(img_height,img_width,img_channel):
    ip = Input(shape=(img_height, img_width,img_channel))
    x = Conv2D(64, (9, 9), padding='same', activation='linear',  kernel_initializer='glorot_uniform')(ip)
    x = BatchNormalization(axis= -1)(x)
    x = LeakyReLU(alpha=0.25)(x)
    for i in range(5):
        x = residual_block(x, 64,3)
    x = Conv2D(64, (3, 3), padding='same',kernel_initializer='glorot_uniform')(x)
    x = BatchNormalization(axis=-1)(x)
    x=Conv2D(64,(3, 3),padding='same',activation='relu')(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


#plot_model(deblocking, to_file='model.png', show_shapes=True, show_layer_names=True)