如何在Keras上仅加载特定的砝码 [英] How to load only specific weights on Keras

问题描述

我有一个训练有素的模型，我已经导出了权重，并希望部分加载到另一个模型中. 我的模型是使用TensorFlow作为后端在Keras中构建的.

I have a trained model that I've exported the weights and want to partially load into another model. My model is built in Keras using TensorFlow as backend.

现在我正在执行以下操作:

Right now I'm doing as follows:

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape, trainable=False))
model.add(Activation('relu', trainable=False))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3), trainable=False))
model.add(Activation('relu', trainable=False))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), trainable=True))
model.add(Activation('relu', trainable=True))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])


model.load_weights("image_500.h5")
model.pop()
model.pop()
model.pop()
model.pop()
model.pop()
model.pop()


model.add(Conv2D(1, (6, 6),strides=(1, 1), trainable=True))
model.add(Activation('relu', trainable=True))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

我相信这是一种糟糕的方法，尽管它可以工作.

I'm sure it's a terrible way to do it, although it works.

如何仅加载前9层?

推荐答案

如果在最初训练的模型和新模型之间一致地命名了前9层，则可以将model.load_weights()与by_name=True结合使用.这样只会在新模型的图层中更新权重，这些图层在原始训练模型中具有相同名称的图层.

If your first 9 layers are consistently named between your original trained model and the new model, then you can use model.load_weights() with by_name=True. This will update weights only in the layers of your new model that have an identically named layer found in the original trained model.

可以使用name关键字指定图层名称，例如:

The name of the layer can be specified with the name keyword, for example:

model.add(Dense(8, activation='relu',name='dens_1'))

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