Pytorch_RNN執行個體代碼（進階篇）

pytorch_RNN代碼實作（進階篇）——名字國家分類

本文旨在代碼實作，具體内容講解請參考劉老師的視訊：(https://www.bilibili.com/video/BV1Y7411d7Ys?p=13)

訓練集與測試集下載下傳連結: https://pan.baidu.com/s/1Z9bz-nkrb1i68frPBNA0Vw 提取碼: xxqe

代碼如下：

import torch
import gzip
import csv
from torch.utils.data import DataLoader
import torch.optim 
import time
import math
from torch.utils.data import Dataset
HIDDEN_SIZE=100
BATCH_SIZE=256
N_LAYER=2
N_EPOCHS=100
N_CHARS=128
USE_GPU=False
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)))
        self.country_dict=self.getCountryDict()
        self.country_num=len(self.country_list)
    def __getitem__(self,index):
        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
    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_direction=2 if bidirectional else 1
        self.embedding=torch.nn.Embedding(input_size,hidden_size)
        self.gru=torch.nn.GRU(hidden_size,hidden_size,n_layers,
                              bidirectional=bidirectional)
        self.fc=torch.nn.Linear(hidden_size*self.n_direction,output_size)
    def __init__hidden(self,batch_size):
        hidden=torch.zeros(self.n_layers*self.n_direction,
                           batch_size,self.hidden_size)
        return create_tensor(hidden)
    def forward(self,input,seq_lengths):
        input=input.t()
        batch_size=input.size(1)
        hidden=self.__init__hidden(batch_size)
        embedding=self.embedding(input)
        gru_input=torch.nn.utils.rnn.pack_padded_sequence(embedding,seq_lengths)
        output,hidden=self.gru(gru_input,hidden)
        if self.n_direction==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 create_tensor(tensor):
    if USE_GPU:
        device=torch.device('cuda:0')
        tensor=tensor.to(device)
    return tensor
def name2list(name):
    arr=[ord(c) for c in name]
    return arr,len(arr)
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):
        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:
            print(f'[{time_since(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
def time_since(since):
    s=time.time()-since
    m=math.floor(s/60)
    s-=m*60
    return '%dm %ds'%(m,s)
if __name__=='__main__':
    classifier=RNNClassifier(N_CHARS,HIDDEN_SIZE,N_COUNTRY,N_LAYER)
    if USE_GPU:
        device=torch.device('cuda:0')
        classifier.to(device)
    criterion=torch.nn.CrossEntropyLoss()
    optimizer=torch.optim.Adam(classifier.parameters(),lr=0.01)
    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)
import matplotlib.pyplot as plt
import numpy as np
x=np.arange(1,len(acc_list)+1,1)
y=np.array(acc_list)
plt.plot(x,y)
plt.xlabel('Epoch')
plt.ylabel('Test Accuracy')
plt.grid()
plt.show()