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| import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch import nn
from os.path import exists
batch_size=100
word_cnt=29
src=pd.read_csv("./data/data.csv")
class mydata(Dataset):
def __init__(self,typ):
self.data=src[src.part==typ]
self.typ=typ
def __getitem__(self, idx):
sen=[int(x) for x in self.data.iloc[idx]['x'].split(',')]
oht=np.zeros((15,word_cnt))
for i in range(min(len(sen),15)):
oht[i,sen[i]-1]=1
return torch.FloatTensor(oht),int(self.data.iloc[idx]['y'])
def __len__(self):
return len(self.data)
train_set=mydata("train")
train_load=DataLoader(dataset=train_set,batch_size=batch_size,shuffle=True)
test_set=mydata("test")
test_load=DataLoader(dataset=test_set,batch_size=batch_size,shuffle=True)
val_set=mydata("val")
val_load=DataLoader(dataset=val_set,batch_size=batch_size,shuffle=True)
class Mol(nn.Module):
def __init__(self):
super().__init__()
self.h=50
self.mol=nn.Sequential(
nn.Conv1d(15,self.h,5,2),nn.ELU(),
nn.Conv1d(self.h,self.h,5,2),nn.ELU(),
nn.Conv1d(self.h,self.h,5,1),nn.ELU(),
)
self.lin=nn.Linear(self.h,18)
def forward(self,x):
y1=self.mol(x).squeeze(dim=2)
return self.lin(y1)
mynn=Mol()
def test_accuracy(data_load):
with torch.no_grad():
siz=0
ac=0
for data in data_load:
sen,tag=data
out=mynn(sen)
for x,y in zip(out,tag):
x=x.argmax(dim=0)
siz+=1
if x==y:
ac+=1
print("准确率为{:f}".format(ac/siz))
def train_model():
epoch=0
train_step=0
loss_fn=nn.CrossEntropyLoss()
optim=torch.optim.Adam(mynn.parameters(), lr=1e-3)
for epoch in range(30):
print("批次:{}".format(epoch))
for data in train_load:
optim.zero_grad()
sen,tag=data
output=mynn(sen)
res_loss=loss_fn(output,tag)
res_loss.backward()
optim.step()
train_step+=1
if train_step%10==0:
print("训练次数:{},loss:{}".format(train_step,res_loss))
test_accuracy(test_load)
torch.save(mynn.state_dict(),"./model/epoch_{}.pth".format(epoch))
torch.save(mynn.state_dict(),"./model/final.pth")
if not exists("./model/final.pth"):
train_model()
else:
mynn.load_state_dict(torch.load("./model/final.pth"))
test_accuracy(val_load)
|