是否可以使用set_weights和get_weights方案，将训练网络的最后一层的权重加载到我的新模型的最后一层？我保存了每个层的权重为mat文件（在训练后），以便在Matlab中进行一些计算，我只想将修改后的最后一层的权重加载到新模型的最后一层，而其他层则保持与训练模型相同的权重。这有点棘手，因为保存的格式是mat文件。

weights1 = lstm_model1.layers[0].get_weights()[0]biases1 = lstm_model1.layers[0].get_weights()[1]weights2 = lstm_model1.layers[2].get_weights()[0]biases2 = lstm_model1.layers[2].get_weights()[1]weights3 = lstm_model1.layers[4].get_weights()[0]biases3 = lstm_model1.layers[4].get_weights()[1]# Save the weights and biases for adaptation algorithm savemat("weights1.mat", mdict={'weights1': weights1})  savemat("biases1.mat", mdict={'biases1': biases1})      savemat("weights2.mat", mdict={'weights2': weights2})   savemat("biases2.mat", mdict={'biases2': biases2})      savemat("weights3.mat", mdict={'weights3': weights3}) savemat("biases3.mat", mdict={'biases3': biases3})  

如何将旧模型中其他层的旧权重加载到新模型（不包括最后一层），并将修改后的最后一层的权重加载到新模型的最后一层？

回答：

如果保存为.h5文件格式，这是可行的。然而，对于.mat文件格式，我不太确定：

简单来说，你只需要在目标层上调用get_weights，并在另一个模型的对应层上调用set_weights：

last_layer_weights = old_model.layers[-1].get_weights()new_model.layers[-1].set_weights(last_layer_weights)

这里有一个更完整的代码示例：

# Create an arbitrary model with some weights, for examplemodel = Sequential(layers = [    Dense(70, input_shape = (100,)),    Dense(60),    Dense(50),    Dense(5)])# Save the weights of the modelmodel.save_weights(“model.h5”)# Later, load in the model (we only really need the layer in question)old_model = Sequential(layers = [    Dense(70, input_shape = (100,)),    Dense(60),    Dense(50),    Dense(5)])old_model.load_weights(“model.h5”)# Create a new model with slightly different architecture (except for the layer in question, at least)new_model = Sequential(layers = [    Dense(80, input_shape = (100,)),    Dense(60),    Dense(50),    Dense(5)])# Set the weights of the final layer of the new model to the weights of the final layer of the old model, but leaving other layers unchanged.new_model.layers[-1].set_weights(old_model.layers[-1].get_weights())# Assert that the weights of the final layer is the same, but other are not.print (np.all(new_model.layers[-1].get_weights()[0] == old_model.layers[-1].get_weights()[0]))>> Trueprint (np.all(new_model.layers[-2].get_weights()[0] == old_model.layers[-2].get_weights()[0]))>> False