我目前正在使用LSTM神经网络来分析股票数据的收盘价。我收到一条错误消息：
TypeError: Error converting shape to a TensorShape: only size-1 arrays can be converted to Python scalars.
执行此代码时：
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
#take user input to replace stock to analyze
userInput = input('Stocks: \n')
days = int(input('how many time series to analyze? \n'))
#read data
data = pd.read_csv('Stocks/%s.us.txt' % userInput, usecols=[5], engine = 'python', skipfooter = 3)
#convert it into numpy array
dataSet = data.values
#scale the data into a range of 0, 1
scaling = MinMaxScaler(feature_range = (0,1))
#transform testing data to have 0 mean
dataSet = scaling.fit_transform(dataSet)
#splitting to testing and training data
trainData = int(len(dataSet) * 0.80)
testData = len(dataSet) - trainData
#time series data shaping
features = []
labels = []
for i in range(days, len(dataSet)):
features.append(dataSet[i-days:i, 0])
labels.append(dataSet[i, 0])
#convert features and labels to numpy arrays
features, labels = np.array(features), np.array(labels)
#building LSTM model
model = Sequential()
model.add(LSTM(1, input_shape=(1, dataSet)))
model.add(Dense(1))
model.compile(optimizer = 'adam', loss = 'mse')
model.fit(trainData, testData, batch_size = 32, epochs = 10)
在本文here中，它解释了此问题可能是由于索引不同维度的行而不是长度而引起的，但是我看不到它如何适用于我的代码，因为我从未索引过任何内容分类。我曾尝试过寻找其他职位，但没有一种解决方案适用于我的问题。 同样值得注意的是，错误可以追溯到Anaconda3 lib文件的第143行，就像这样
File "C:\Users\me\Anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 143, in make_shape
为什么会出现此错误，我该如何解决？