用神经网络做运动时序序列。

标签：plt torch feature label test 神经网络 序列 时序 out

代码

import matplotlib.pyplot as plt import numpy as np import pandas as pd df = pd.read_csv('train.csv') df=df.drop(['ID'],axis=1) nmp=df.to_numpy() feature=nmp[:-20,:-1] label=nmp[:-20,-1]#（210，240）
test_feature=nmp[-20:,:-1] test_label=nmp[-20:,-1]#（210，240）
from torch import nn import torch loss=nn.MSELoss() feature=torch.Tensor(feature) label=torch.Tensor(label) label=label.reshape(-1,1)
test_feature=torch.Tensor(test_feature) test_label=torch.Tensor(test_label) test_label=test_label.reshape(-1,1)
network=nn.Sequential(nn.Linear(240,1),nn.Sigmoid()) import torch.optim as optim optimizer = optim.Adam(network.parameters(), lr=0.004) for epoch in range(5000):     optimizer.zero_grad()     out=network(feature)     l=loss(out,label)     l.backward()     optimizer.step()     Y = torch.ge(out, 0.5).float()     acc=Y.eq(label).float().sum()/len(label)     out=network(test_feature)     Y = torch.ge(out, 0.5).float()     test_acc=Y.eq(test_label).float().sum()/len(test_label)     print(epoch,l,acc,test_acc)     if test_acc==0.50 and acc>0.93:         break ax1 = plt.subplot(2,1,1) ax2 = plt.subplot(2,1,2) out=network(feature) out=out.detach().numpy() plt.sca(ax1) for i in range(out.shape[0]):     if label[i]==1:         plt.scatter(out[i],0,color='red')     if label[i]==0:         plt.scatter(out[i],0,color='blue')         out=network(test_feature) out=out.detach().numpy() plt.sca(ax2) for i in range(out.shape[0]):     if test_label[i]==1:         plt.scatter(out[i],0,color='red')     if test_label[i]==0:         plt.scatter(out[i],0,color='blue')  

plt.show()

 

 

标签：plt,torch,feature,label,test,神经网络,序列,时序,out
From： https://www.cnblogs.com/hahaah/p/16938090.html