使用keras图断开误差的VGG16预训练模型的U-Net模型

Question

我正在尝试使用 VGG16 编码器构建 U-Net 模型。这是模型代码。

from keras.applications.vgg16 import VGG16
base_pretrained_model = VGG16(input_shape =  shape, include_top = False, weights = 'imagenet')
base_pretrained_model.trainable = False
base_pretrained_model.summary()
inp = Input(shape=shape,name = 'image_input')
output_vgg16_conv = base_pretrained_model(inp)

# output and start upsampling
conv_1  = Conv2D(512, (3,3), activation='relu', padding='same')(output_vgg16_conv)
up_conv = Conv2DTranspose(256, (3,3), strides=(2,2), activation='relu', padding='same')(conv_1)

# first concatenation block
concat_1 = concatenate([base_pretrained_model.get_layer('block5_conv3').output, up_conv], axis=-1, name='concat_1')
conv_2 = Conv2D(512, (3,3), activation='relu', padding='same')(concat_1)
up_conv_2 = Conv2DTranspose(256, (3,3), strides=(2,2), activation='relu', padding='same')(conv_2)

# second concatenation block
concat_2 = concatenate([up_conv_2, base_pretrained_model.get_layer('block4_conv3').output])
conv_3 = Conv2D(512, (3,3), activation='relu', padding='same')(concat_2)
up_conv_3 = Conv2DTranspose(128, (3,3), strides=(2,2), activation='relu', padding='same')(conv_3)

# third concatenation block
concat_3 = concatenate([up_conv_3, base_pretrained_model.get_layer('block3_conv3').output])
conv_4 = Conv2D(256, (3,3), activation='relu', padding='same')(concat_3)
up_conv_4 = Conv2DTranspose(64, (3,3), strides=(2,2), activation='relu', padding='same')(conv_4)

# fourth concatenation block
concat_4 = concatenate([up_conv_4, base_pretrained_model.get_layer('block2_conv2').output])
conv_5 = Conv2D(128, (3,3), activation='relu', padding='same')(concat_4)
up_conv_5 = Conv2DTranspose(32, (3,3), strides=(2,2), activation='relu', padding='same')(conv_5)

# fifth concatenation block
concat_4 = concatenate([up_conv_5, base_pretrained_model.get_layer('block1_conv2').output])
conv_6 = Conv2D(128, (3,3), activation='sigmoid', padding='same')(concat_4)

finalModel = Model(inp, output = conv_6)

我收到以下错误。

ValueError：图断开连接：无法获取张量张量的值（"input_1： 0"，形状=（无，512,512,3），dtype=float32）在层"input_1"。

注意：input_1是VGG16型号的输入层

Answer 1

您不需要定义额外的< code>Input层，因为< code>VGG16模型中已经有一个< code>Input层，您可以使用

inp = base_pretrained_model.input

您还可以使用以下命令获得VGG16模型的输出

output_vgg16_conv = base_pretrained_model.output

此外，要保持基本模型的权重VGG16不变，可以使每个层不可访问：

for layer in base_pretrained_model.layers:
    layer.trainable = False

希望有帮助。