pytorch 搭建lstm模型

import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error as mse
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False  # 用来正常显示负号
import seaborn as sns
import pyecharts.options as opts
from pyecharts.charts import Line

data = pd.read_csv('黄金价格.csv')

data = data.fillna(0)
print(data.head(5))

# 设置时间为索引
data['Date'] = pd.to_datetime(data['Date'])
# 重置时间顺序
data.set_index('Date', inplace=True)
data.sort_values('Date', ascending=True, inplace=True)

plt.figure(figsize=(12, 6))
data['Close/Last'].plot()
plt.title("黄金价格走势图")
plt.show()


# 计算相关性系数
corr = data.corr()
corr['Close/La