pytorch builds SSAE to implement prediction classification code template complete code

 

 

# pip install openpyxl -i https://pypi.tuna.tsinghua.edu.cn/simple/
# pip install optuna -i https://pypi.tuna.tsinghua.edu.cn/simple/
import numpy as np
import pandas as pd
from tqdm import tqdm
import optuna
import torch
from torch import nn
import torch.nn.functional as F
from torch import tensor
import torch.utils.data as Data
import math
from matplotlib import pyplot
from datetime import datetime, timedelta
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
import torch
import torch.nn as nn
import math
import warnings

warnings.filterwarnings("ignore")
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

# 设置随机参数：保证实验结果可以重复
SEED = 1234
import random

random.seed(