刘二大人pytorch教程RNN高级篇代码

数据集链接：https://pan.baidu.com/s/1vZ27gKp8Pl-qICn_p2PaSw
提取码：cxe4

import torch
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pack_padded_sequence
import time
import matplotlib.pyplot as plt
import numpy as np
import gzip
import csv
import math

HIDDEN_SIZE=100
BATCH_SIZE=256
N_LAYER=2
N_EPOCHS=100
N_CHARS=128#名字长度，英文名字采用ASCLL码最大128
class NameDataset(Dataset):
    def __init__(self,is_train_set=True):
        filename='names_train.csv.gz' if is_train_set else 'names_test.csv.gz'
        with gzip.open(filename,'rt')as f:
            reader=csv.reader(f)
            rows=list(reader)
        self.names=[row[0] for row in rows]
        self.len=len(self.names)
        self.countries=[row[1] for row in rows]
        self.country_list=list(sorted(set(self.countries)))#set消除本列中相同城市
        self.country_dict=self.getCountryDict()
        self.country_num=len(self.country_list)
    def __getitem__(self, index):#用名字找国家索引
        return self.names[index],self.country_dict[self.countries[index]]
    def __len__(self):
        return self.len
    def getCountryDict(self):
        country_dict=dict()
        for idx,country_name in enumerate(self.country_list,0):
            country_dict[country_name]=idx
        return country_dict###################对齐对齐对齐########################################################################################################
    def idx2country(self,index):
        return self.country_list[index]
    def getCountriesNum(self):
        return self.country_num


trainset=NameDataset(is_train_set=True)
trainloader=DataLoader(trainset,batch_size=BATCH_SIZE,shuffle=True)
testset=NameDataset(is_train_set=False)
testloader=DataLoader(testset,batch_size=BATCH_SIZE,shuffle=False)

N_COUNTRY=trainset.getCountriesNum()

class RNNClassifier(torch.nn.Module):
    def __init__(self,input_size,hidden_size,output_size,n_layers=1,bidirectional=True):
        super(RNNClassifier, self).__init__()
        self.hidden_size=hidden_size
        self.n_layers=n_layers
        self.n_directions=2 if bidirectional else 1

        self.embedding=torch.nn.Embedding(input_size,hidden_size)#inputsize构建嵌入层
        self.gru=torch.nn.GRU(hidden_size,hidden_size,n_layers,bidirectional=bidirectional)
        self.fc=torch.nn.Linear(hidden_size*self.n_directions,output_size)

    def _init_hidden(self,batch_size):
        hidden=torch.zeros(self.n_layers*self.n_directions,batch_size,self.hidden_size)
        return create_tensor(hidden)

    def forward(self,input,seq_lengths):
        input=input.t()#转置将BxS的矩阵变成SxB
        batch_size=input.size(1)

        hidden=self._init_hidden(batch_size)
        embedding=self.embedding(input)
        #pack them up加快gru速度
        gru_input=torch.nn.utils.rnn.pack_padded_sequence(embedding,seq_lengths)

        output,hidden=self.gru(gru_input,hidden)
        if self.n_directions==2:
            hidden_cat=torch.cat([hidden[-1],hidden[-2]],dim=1)
        else:
            hidden_cat=hidden[-1]
        fc_output=self.fc(hidden_cat)
        return fc_output

def name2list(name):
    arr=[ord(c) for c in name]
    return  arr,len(arr)
def create_tensor(tensor):
    return tensor

def make_tensors(names,countries):
    sequences_and_lengths=[name2list(name) for name in names]
    name_sequences=[s1[0] for s1 in sequences_and_lengths]
    seq_lengths=torch.LongTensor([s1[1] for s1 in sequences_and_lengths])
    # countries=countries.long()

    seq_tensor=torch.zeros(len(name_sequences),seq_lengths.max()).long()
    for idx,(seq, seq_len) in enumerate(zip(name_sequences,seq_lengths),0):#对输入数据进行补0操作
        seq_tensor[idx, :seq_len]= torch.LongTensor(seq)

    seq_lengths,perm_idx=seq_lengths.sort(dim=0,descending=True)
    seq_tensor=seq_tensor[perm_idx]
    countries=countries[perm_idx]
    return create_tensor(seq_tensor),create_tensor(seq_lengths),create_tensor(countries)

def trainModel():
    total_loss=0
    for i, (names, countries) in enumerate(trainloader, 1):
        inputs, seq_lengths, target=make_tensors(names, countries)
        output=classifier(inputs, seq_lengths)
        loss=criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_loss+=loss.item()
        if i%10==0:
            end = time.time()
            print(f'[{end-start}]Epoch{epoch}',end='')
            print(f'[{i*len(inputs)}/{len(trainset)}]',end='')
            print(f'loss={total_loss/(i*len(inputs))}')
    return total_loss

def testModel():
    correct=0
    total=len(testset)
    print("evaluating trained model...")
    with torch.no_grad():
        for i,(names,countries)in enumerate(testloader,1):
            inputs,seq_lengths,target=make_tensors(names,countries)
            output=classifier(inputs,seq_lengths)
            pred=output.max(dim=1,keepdim=True)[1]
            correct+=pred.eq(target.view_as(pred)).sum().item()

        percent='%.2f'%(100*correct/total)
        print(f'Test set:Accuracy {correct}/{total}{" "}{percent}%')
    return correct/total


if __name__ == '__main__':
    classifier=RNNClassifier(N_CHARS,HIDDEN_SIZE,N_COUNTRY,N_LAYER)
    criterion=torch.nn.CrossEntropyLoss()
    optimizer=torch.optim.Adam(classifier.parameters(),lr=0.001)
    start=time.time()
    print("Training for %d epochs..."%N_EPOCHS)
    acc_list=[]
    for epoch in range(1,N_EPOCHS+1):
        trainModel()
        acc=testModel()
        acc_list.append(acc)
epoch1=np.arange(1,len(acc_list)+1,1)
acc_list1=np.array(acc_list)
plt.plot(epoch1,acc_list1)
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.grid()
plt.show()