在这个实验中，数据的预处理过程以及网络的初始化及模型的训练等过程同前文《利用Pytorch+LSTM实现中文新闻分类》，具体这里就不再重复解释了。如果有读者在对数据集的预处理过程中有疑问，请参考我的其他博客，里面对这些方法均有我的一些个人体会，这里直接贴上源码。
## 导入本章所需要的模块
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
import time
import copy
import torch
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms
from torchtext import data
from torchtext.vocab import Vectors
## 使用torchtext库进行数据准备
# 定义文件中对文本和标签所要做的操作
## 定义文本切分方法，直接使用空格切分即可
mytokenize = lambda x: x.split()
TEXT = data.Field(sequential=True, tokenize=mytokenize,
include_lengths=True, use_vocab=True,
batch_first=True, fix_length=200)
LABEL = data.Field(sequential=False, use_vocab=False,
pad_token=None, unk_token=None)
## 对所要读取的数据集的列进行处理
train_test_fields = [
("label", LABEL), # 对标签的操作
("text", TEXT) # 对文本的操作
]
## 读取数据
traindata,testdata = data.TabularDataset.splits(
path="./data/chap6", format="csv",
train="imdb_train.csv", fields=train_test_fields,
test = "imdb_test.csv", skip_header=True
)
# ## 加载预训练的词向量和构建词汇表
## Vectors导入预训练好的词向量文件
vec = Vectors("glove.6B.100d.txt", "./data")
# ## 使用训练集构建单词表，导入预先训练的词嵌入
TEXT.build_vocab(traindata,max_size=20000,vectors = vec)
# TEXT.build_vocab(traindata,max_size=20000)
LABEL.build_vocab(traindata)
## 训练集、验证集和测试集定义为迭代器
BATCH_SIZE = 32
train_iter = data.BucketIterator(traindata,batch_size = BATCH_SIZE)
test_iter = data.BucketIterator(testdata,batch_size = BATCH_SIZE)
## 获得一个batch的数据，对数据进行内容进行介绍
for step, batch in enumerate(train_iter):
textdata,target = batch.text[0],batch.label
if step > 0:
break
class GRUNet(nn.Module):
def __init__(self, vocab_size,embedding_dim, hidden_dim, layer_dim, output_dim):
"""
vocab_size:词典长度
embedding_dim:词向量的维度
hidden_dim: GRU神经元个数
layer_dim: GRU的层数
output_dim:隐藏层输出的维度(分类的数量)
"""
super(GRUNet, self).__init__()
self.hidden_dim = hidden_dim ## GRU神经元个数
self.layer_dim = layer_dim ## GRU的层数
## 对文本进行词项量处理
self.embedding = nn.Embedding(vocab_size, embedding_dim)
# LSTM ＋ 全连接层
self.gru = nn.GRU(embedding_dim, hidden_dim, layer_dim,
batch_first=True)
self.fc1 = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
torch.nn.Dropout(0.5),
torch.nn.ReLU(),
nn.Linear(hidden_dim, output_dim)
)
def forward(self, x):
embeds = self.embedding(x)
# r_out shape (batch, time_step, output_size)
# h_n shape (n_layers, batch, hidden_size)
r_out, h_n = self.gru(embeds, None) # None 表示初始的 hidden state 为0
# 选取最后一个时间点的out输出
out = self.fc1(r_out[:, -1, :])
return out
## 初始化网络
vocab_size = len(TEXT.vocab)
embedding_dim = vec.dim # 词向量的维度
# embedding_dim = 128 # 词向量的维度
hidden_dim = 128
layer_dim = 1
output_dim = 2
grumodel = GRUNet(vocab_size, embedding_dim, hidden_dim, layer_dim, output_dim)
## 将导入的词项量作为embedding.weight的初始值
grumodel.embedding.weight.data.copy_(TEXT.vocab.vectors)
## 将无法识别的词'<unk>', '<pad>'的向量初始化为0
UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]
PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]
grumodel.embedding.weight.data[UNK_IDX] = torch.zeros(vec.dim)
grumodel.embedding.weight.data[PAD_IDX] = torch.zeros(vec.dim)
## 定义网络的训练过程函数
def train_model(model,traindataloader, testdataloader,criterion,
optimizer,num_epochs=25):
"""
model:网络模型；traindataloader:训练数据集;valdataloader:验证数据集;
criterion:损失函数;optimizer:优化方法;
num_epochs:训练的轮数,scheduler:学习率变化器
"""
train_loss_all = []
train_acc_all = []
test_loss_all = []
test_acc_all = []
learn_rate = []
since = time.time()
## 设置等间隔调整学习率,每隔step_size个epoch,学习率缩小10倍
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
for epoch in range(num_epochs):
learn_rate.append(scheduler.get_lr()[0])
print('-' * 10)
print('Epoch {}/{},Lr:{}'.format(epoch, num_epochs - 1,learn_rate[-1]))
# 每个epoch有两个阶段,训练阶段和验证阶段
train_loss = 0.0
train_corrects = 0
train_num = 0
test_loss = 0.0
test_corrects = 0
test_num = 0
model.train() ## 设置模型为训练模式
for step,batch in enumerate(traindataloader):
textdata,target = batch.text[0],batch.label
out = model(textdata)
pre_lab = torch.argmax(out,1) # 预测的标签
loss = criterion(out, target) # 计算损失函数值
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item() * len(target)
train_corrects += torch.sum(pre_lab == target.data)
train_num += len(target)
## 计算一个epoch在训练集上的损失和精度
train_loss_all.append(train_loss / train_num)
train_acc_all.append(train_corrects.double().item()/train_num)
print('{} Train Loss: {:.4f} Train Acc: {:.4f}'.format(
epoch, train_loss_all[-1], train_acc_all[-1]))
scheduler.step() ## 更新学习率
## 计算一个epoch的训练后在验证集上的损失和精度
model.eval() ## 设置模型为训练模式评估模式
for step,batch in enumerate(testdataloader):
textdata,target = batch.text[0],batch.label
out = model(textdata)
pre_lab = torch.argmax(out,1)
loss = criterion(out, target)
test_loss += loss.item() * len(target)
test_corrects += torch.sum(pre_lab == target.data)
test_num += len(target)
## 计算一个epoch在训练集上的损失和精度
test_loss_all.append(test_loss / test_num)
test_acc_all.append(test_corrects.double().item()/test_num)
print('{} Test Loss: {:.4f} Test Acc: {:.4f}'.format(
epoch, test_loss_all[-1], test_acc_all[-1]))
train_process = pd.DataFrame(
data={"epoch":range(num_epochs),
"train_loss_all":train_loss_all,
"train_acc_all":train_acc_all,
"test_loss_all":test_loss_all,
"test_acc_all":test_acc_all,
"learn_rate":learn_rate})
return model,train_process
# 定义优化器
optimizer = optim.RMSprop(grumodel.parameters(), lr=0.003)
loss_func = nn.CrossEntropyLoss() # 交叉熵作为损失函数
## 对模型进行迭代训练,对所有的数据训练EPOCH轮
grumodel,train_process = train_model(
grumodel,train_iter,test_iter,loss_func,optimizer,num_epochs=10)
## 输出结果保存和数据保存
torch.save(grumodel,"data/chap7/grumodel.pkl")
## 导入保存的模型
grumodel = torch.load("data/chap7/grumodel.pkl")
grumodel
## 保存训练过程
train_process.to_csv("data/chap7/grumodel_process.csv",index=False)
## 可视化模型训练过程中
plt.figure(figsize=(18,6))
plt.subplot(1,2,1)
plt.plot(train_process.epoch,train_process.train_loss_all,
"r.-",label = "Train loss")
plt.plot(train_process.epoch,train_process.test_loss_all,
"bs-",label = "Test loss")
plt.legend()
plt.xlabel("Epoch number",size = 13)
plt.ylabel("Loss value",size = 13)
plt.subplot(1,2,2)
plt.plot(train_process.epoch,train_process.train_acc_all,
"r.-",label = "Train acc")
plt.plot(train_process.epoch,train_process.test_acc_all,
"bs-",label = "Test acc")
plt.xlabel("Epoch number",size = 13)
plt.ylabel("Acc",size = 13)
plt.legend()
plt.show()
## 对测试集进行预测并计算精度
grumodel.eval() ## 设置模型为训练模式评估模式
test_y_all = torch.LongTensor()
pre_lab_all = torch.LongTensor()
for step,batch in enumerate(test_iter):
textdata,target = batch.text[0],batch.label.view(-1)
out = grumodel(textdata)
pre_lab = torch.argmax(out,1)
test_y_all = torch.cat((test_y_all,target)) ##测试集的标签
pre_lab_all = torch.cat((pre_lab_all,pre_lab))##测试集的预测标签
acc = accuracy_score(test_y_all,pre_lab_all)
print("在测试集上的预测精度为:",acc)
