是否可以使用 set_weights 和 get_weights 方案將權重從經過訓練的網絡加載到我的新模型的最后一層？關鍵是，我將每一層的權重保存為一個 mat 文件（訓練后），以便在 Matlab 中進行一些計算，我只想將最后一層的修改權重加載到我的新模型和其他層的最后一層獲得與訓練模型相同的權重。這有點棘手，因為保存的格式是 mat。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}) 我如何才能將其他層的舊權重加載到新模型（沒有最后一層）并將最后一層的修改權重加載到新模型的最后一層？
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飲歌長嘯
TA貢獻1951條經驗 獲得超3個贊
如果將其另存為 .h5 文件格式，則可以正常工作。但是，我不確定 .mat：
簡單來說，您只需調用get_weights所需的層，類似地，set_weights調用另一個模型的相應層：
last_layer_weights = old_model.layers[-1].get_weights()
new_model.layers[-1].set_weights(last_layer_weights)
如需更完整的代碼示例，請點擊此處：
# Create an arbitrary model with some weights, for example
model = Sequential(layers = [
Dense(70, input_shape = (100,)),
Dense(60),
Dense(50),
Dense(5)])
# Save the weights of the model
model.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]))
>> True
print (np.all(new_model.layers[-2].get_weights()[0] == old_model.layers[-2].get_weights()[0]))
>> False
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