如何在模型之前添加几层，使用张量流进行迁移学习

Question

我正在尝试在tensorflow中使用迁移学习。我知道高级范式

base_model=MobileNet(weights='imagenet',include_top=False) #imports the 
mobilenet model and discards the last 1000 neuron layer.
x=base_model.output
x=GlobalAveragePooling2D()(x)
x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.
x=Dense(1024,activation='relu')(x) #dense layer 2
x=Dense(512,activation='relu')(x) #dense layer 3
preds=Dense(120,activation='softmax')(x) #final layer with softmax activation

然后通过编译它

model=Model(inputs=base_model.input,outputs=preds)

但是，我希望在 base_model.input 之前还有其他一些层。我想为进来的图像和其他一些东西添加对抗性噪音。如此有效地我想知道如何：

base_model=MobileNet(weights='imagenet',include_top=False) #imports the 
mobilenet model and discards the last 1000 neuron layer
x = somerandomelayers(x_in)
base_model.input = x_in
x=base_model.output
x=GlobalAveragePooling2D()(x)
x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.
x=Dense(1024,activation='relu')(x) #dense layer 2
x=Dense(512,activation='relu')(x) #dense layer 3
preds=Dense(120,activation='softmax')(x) #final layer with softmax activation
model=Model(inputs=x_in,outputs=preds)

但是行base_model.input = x_in显然不是这样做的方法，因为它会抛出can't set attribute错误。我该如何实现所需的行为？

Answer 1

您需要定义输入层。这很简单，只要确保设置正确的形状即可。例如，您可以使用 Keras 中的任何预定义模型。

base_model = keras.applications.any_model(...)
input_layer = keras.layers.Input(shape)
x = keras.layers.Layer(...)(input_layer)
...
x = base_model(x)
...
output = layers.Dense(num_classes, activation)(x)
model = keras.Model(inputs=input_layer, outputs=output)