我调了下模型，
nn.Linear(36,2),nn.Sigmoid(),nn.Linear(2,2),nn.Sigmoid(),nn.Linear(2,1),nn.Sigmoid().
原先是(36,3)(3,1)的全连接神经网络。但是容易过拟合，但是我随便调了下调成了。(36,2)(2,2)(2,1)模型准确率达到了0.85，且更能找到2模型准确率。
由此我得出结论深层是对前一层的微调，而宽的可能表示提取的特征。宽度大了也容易过拟合。
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.fft as fft
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）
feature=torch.fft.fft(torch.Tensor(feature))
feature=torch.abs(feature)/240*2
feature=feature[:,[0, 1, 60, 180, 239, 58, 59, 61, 179, 181, 182, 120, 62, 178, 119, 121, 117, 123, 2, 238, 55, 65, 175, 185, 63, 116, 124, 177, 118, 122, 56, 64, 176, 184, 57, 183]]
test_feature=nmp[-20:,:-1]
test_label=nmp[-20:,-1]#（210，240）
test_feature=torch.fft.fft(torch.Tensor(test_feature))
test_feature=torch.abs(test_feature)/240*2
test_feature=test_feature[:,[0, 1, 60, 180, 239, 58, 59, 61, 179, 181, 182, 120, 62, 178, 119, 121, 117, 123, 2, 238, 55, 65, 175, 185, 63, 116, 124, 177, 118, 122, 56, 64, 176, 184, 57, 183]]
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(36,2),nn.Sigmoid(),nn.Linear(2,2),nn.Sigmoid(),nn.Linear(2,1),nn.Sigmoid())
import torch.optim as optim
optimizer = optim.Adam(network.parameters(), lr=0.004)
for epoch in range(100000):
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:
if acc>=0.8 and test_acc>=0.90:
break
df = pd.read_csv('test.csv')
df=df.drop(['ID'],axis=1)
nmp=df.to_numpy()
feature=nmp[:,:]
feature=torch.fft.fft(torch.Tensor(feature))
feature=torch.abs(feature)/240*2
feature=torch.Tensor(feature[:,[0, 1, 60, 180, 239, 58, 59, 61, 179, 181, 182, 120, 62, 178, 119, 121, 117, 123, 2, 238, 55, 65, 175, 185, 63, 116, 124, 177, 118, 122, 56, 64, 176, 184, 57, 183]])
out=network(feature)
out=out.detach().numpy()
out=out>0.5
out=out.astype(np.int)
out=pd.DataFrame(out)
out.columns = ['CLASS']
w=[]
for k in range(out.shape[0]):
w.append(k+210)
out['ID']=np.reshape(w,(-1,1))
out[['ID','CLASS']].to_csv('out.csv',index=False)