准确度和验证准确度保持不变，而两种损失均有所减少。尝试了我能找到的所有方法，仍然不起作用

Question

所以，我试图为时间序列预测编写一个多变量LSTM，在我的模型中，损失减少了，但精度指标根本没有改变。我尝试改变神经元的数量、层数、学习率、提前停止、输出层的激活函数和l2正则化，但都不起作用。我是机器学习的初学者，所以任何帮助都会很感激。我的大部分努力就像在黑暗中扔石头。我附加了一个GitHub链接到我的代码，以及一些训练时代。

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.regularizers import l2
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
model = Sequential()
model.add(LSTM(64,activation='sigmoid',return_sequences=True,input_shape = (trainX.shape[1],trainX.shape[2])))
model.add(LSTM(32,activation='sigmoid',return_sequences=False))
model.add(Dropout(0.3))
model.add(Dense(trainY.shape[1]))
opt = Adam(learning_rate= 1e-3)
model.compile(optimizer='adam',loss = 'mse', metrics=['accuracy'])
model.summary()

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_6 (LSTM)                (None, 200, 64)           19200     
_________________________________________________________________
lstm_7 (LSTM)                (None, 32)                12416     
_________________________________________________________________
dropout_3 (Dropout)          (None, 32)                0         
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 33        
=================================================================
Total params: 31,649
Trainable params: 31,649
Non-trainable params: 0

es_callback = EarlyStopping(monitor='val_loss', patience=3)
history = model.fit(trainX,trainY,epochs=40,batch_size= 32,verbose=1,validation_split=0.2, callbacks= [es_callback])

Epoch 1/40
214/214 [==============================] - 58s 169ms/step - loss: 0.1663 - accuracy: 0.0000e+00 - val_loss: 0.0483 - val_accuracy: 5.8617e-04
Epoch 2/40
214/214 [==============================] - 35s 164ms/step - loss: 0.0497 - accuracy: 0.0000e+00 - val_loss: 0.0446 - val_accuracy: 5.8617e-04
Epoch 3/40
214/214 [==============================] - 35s 164ms/step - loss: 0.0309 - accuracy: 0.0000e+00 - val_loss: 0.0092 - val_accuracy: 5.8617e-04
Epoch 4/40
214/214 [==============================] - 35s 163ms/step - loss: 0.0143 - accuracy: 0.0000e+00 - val_loss: 0.0230 - val_accuracy: 5.8617e-04
Epoch 5/40
214/214 [==============================] - 35s 163ms/step - loss: 0.0115 - accuracy: 0.0000e+00 - val_loss: 0.0160 - val_accuracy: 5.8617e-04
Epoch 6/40
214/214 [==============================] - 35s 163ms/step - loss: 0.0099 - accuracy: 0.0000e+00 - val_loss: 0.0172 - val_accuracy: 5.8617e-04

我的代码:https://github.com/RiddhimanRaut/Deep-Learning-based-CPR-estimation/blob/main/CPR_prediction_multivariate_LSTM_tobetrialled_1.ipynb

谢谢!

Answer 1

准确率是分类任务的度量标准。为了衡量回归模型的好坏，可以应用诸如MSE之类的度量。我想这里的讨论可以提供更多的信息。