如何在训练后使用cnn模型的实际权重提取特征?

Question

首先，我用Cifar10训练Alexnet，准确率达到80%。但是，我想从最后一个dropout层提取特征，使用给予80%精度的权重。这是模型

Alexnet=keras.Sequential([
keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding="same", input_shape=(32,32,3)),
keras.layers.BatchNormalization(),
keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'),
keras.layers.MaxPool2D(pool_size=(2,2)),
keras.layers.Dropout(0.2),
keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding="same"),
keras.layers.BatchNormalization(),
keras.layers.Conv2D(filters=64, kernel_size=(1,1), activation='relu'),
keras.layers.BatchNormalization(),
keras.layers.MaxPool2D(pool_size=(2,2)),
keras.layers.Dropout(0.2),
keras.layers.Flatten(),
keras.layers.Dense(1024,activation='relu'),
keras.layers.Dropout(0.2),
keras.layers.Dense(10, activation='softmax')    
])

，下面是我想如何从最后一个dropout层

提取特征(输出)

feature_extractor = keras.Model(
inputs=Alexnet.inputs,
outputs=Alexnet.get_layer(name="dropout_2").output,
)

我想在训练后使用模型的权重来做这个。有人能帮帮我吗?

提前感谢，

Answer 1

您可以按照以下方式构建您的模型:

inputs = keras.layers.Input(shape = (32,32,3))
x = keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu', 
padding="same")(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu')(x)
x = keras.layers.MaxPool2D(pool_size=(2,2))(x)
x = keras.layers.Dropout(0.2)(x)
x = keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', 
padding="same")(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Conv2D(filters=64, kernel_size=(1,1), activation='relu')(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.MaxPool2D(pool_size=(2,2))(x)
x = keras.layers.Dropout(0.2)(x)
x = keras.layers.Flatten()(x)
x = keras.layers.Dense(1024,activation='relu')(x)
x = keras.layers.Dropout(0.2)(x)
intermediary_model = keras.Model(inputs, x)
x = keras.layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs,x)

那么你只训练最终模型，intermediary_model将自动学习与最终模型相同的权重。你可以通过intermediary_model.predict(some_input)

访问你想要的特征图