当我训练我的LSTM模型时，我的损失显示为NaN，准确度为0

Question

我正在尝试根据历史数据预测股价。我正在使用LSTM训练我的模型。但是当我训练时，损失为NaN，准确度为0。我使用的数据来自Yahoo Finance。是银行股票的5年数据。我将数据拆分为测试和训练集，并按比例缩放（尽管不是必需的）。添加了2层LSTM进行训练。

我的代码如下：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout


#Get the Data
data = pd.read_csv('YESBANK.NS.csv')
X = data.iloc[:, [5]].values

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test = train_test_split(X, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(X_train)


# Creating a data structure with 60 timesteps and 1 output
X_train1 = []
y_train1 = []
for i in range(60, training_set_scaled.shape[0]):
    X_train1.append(training_set_scaled[i-60:i, 0])
    y_train1.append(training_set_scaled[i, 0])
X_train, y_train = np.array(X_train1), np.array(y_train1)

# Reshaping for LSTM 
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))


#Initialize the RNN
model = Sequential()

#Adding first LSTM layer
model.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
model.add(Dropout(0.2))

# #Adding second LSTM layer
# model.add(LSTM(units=50, return_sequences=True))
# model.add(Dropout(0.2))

# #Adding third LSTM layer
# model.add(LSTM(units= 50, return_sequences=True))
# model.add(Dropout(0.2))

#Adding fourth LSTM layer
model.add(LSTM(units=50, return_sequences=False))
model.add(Dropout(0.2))

#Adding Output layer
model.add(Dense(units=1))

#Compiling the RNN
model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])

#Fitiing the RNN
model.fit(X_train, y_train, epochs = 500, batch_size = 10) ```

Answer 1

[您正在做的是regression回归股票价格（即连续值）。因此，不应将accuracy用作metric功能。您可以使用均方误差：“ mse”或均方根绝对误差：“ mae”作为度量函数。