是否可以使用 set_weights 和 get_weights 方案將權(quán)重從經(jīng)過(guò)訓(xùn)練的網(wǎng)絡(luò)加載到我的新模型的最后一層？關(guān)鍵是，我將每一層的權(quán)重保存為一個(gè) mat 文件（訓(xùn)練后），以便在 Matlab 中進(jìn)行一些計(jì)算，我只想將最后一層的修改權(quán)重加載到我的新模型和其他層的最后一層獲得與訓(xùn)練模型相同的權(quán)重。這有點(diǎn)棘手，因?yàn)楸４娴母袷绞?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(quán)重加載到新模型（沒有最后一層）并將最后一層的修改權(quán)重加載到新模型的最后一層？
1 回答
飲歌長(zhǎng)嘯
TA貢獻(xiàn)1951條經(jīng)驗(yàn) 獲得超3個(gè)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(jiǎn)單來(lái)說(shuō)，您只需調(diào)用get_weights所需的層，類似地，set_weights調(diào)用另一個(gè)模型的相應(yīng)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(qǐng)點(diǎn)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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