代码复现,
其实我想做自己的模型训练。
直接可以跑
import torch
import matplotlib.pyplot as plt
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
import gzip
import csv
import time
from torch.nn.utils.rnn import pack_padded_sequence
import math
#可不加
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
USE_GPU = False
def time_since(since):
s = time.time() - since
m = math.floor(s / 60)
s -= m*60
return '%dm %ds' % (m, s)
#ord()取ASCII码值
def name2list(name):
arr = [ord(c) for c in name]
return arr, len(arr)
def create_tensor(tensor):
if USE_GPU:
device = torch.device("cuda:0")
tensor = tensor.to(device)
return tensor
def make_tensors(names, countries):
sequences_and_length = [name2list(name) for name in names]
#取出所有的列表中每个姓名的ASCII码序列
name_sequences = [s1[0] for s1 in sequences_and_length]
#将列表车行度转换为LongTensor
seq_lengths = torch.LongTensor([s1[1] for s1 in sequences_and_length])
#将整型变为长整型
countries = countries.long()
#做padding
#新建一个全0张量大小为最大长度-当前长度
seq_tensor = torch.zeros(len(name_sequences), seq_lengths.max()).long()
#取出每个序列及其长度idx固定0
for idx, (seq, seq_len) in enumerate(zip(name_sequences, seq_lengths), 0):
#将序列转化为LongTensor填充至第idx维的0到当前长度的位置
seq_tensor[idx, :seq_len] = torch.LongTensor(seq)
#返回排序后的序列及索引
seq_length, 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_length),create_tensor(countries)
class NameDataset(Dataset):
def __init__(self, is_train_set=True):
#读数据
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)
#数据元组(name,country),将其中的name和country提取出来,并记录数量
self.names = [row[0] for row in rows]
self. len = len(self.names)
self.countries = [row[1] for row in rows]
#将country转换成索引
#列表->集合->排序->列表->字典
self.country_list = list(sorted(set(self.countries)))
self.country_dict = self.getCountryDict()
#获取长度
self.country_num = len(self.country_list)
#获取键值对,country(key)-index(value)
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
#根据索引返回国家名
def idx2country(self, index):
return self.country_list[index]
#返回国家数目
def getCountriesNum(self):
return self.country_num
BATCH_SIZE = 32
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)
#最终的输出维度
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
#Embedding层输入 (SeqLen,BatchSize)
#Embedding层输出 (SeqLen,BatchSize,HiddenSize)
#将原先样本总数为SeqLen,批量数为BatchSize的数据,转换为HiddenSize维的向量
self.embedding = torch.nn.Embedding(input_size, hidden_size)
#bidirection用于表示神经网络是单向还是双向
self.gru = torch.nn.GRU(hidden_size, hidden_size, n_layers, bidirectional = bidirectional)
#线性层需要*direction
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_length):
#对input进行转置
input = input.t()
batch_size = input.size(1)
#(n_Layer * nDirections, BatchSize, HiddenSize)
hidden = self._init_hidden(batch_size)
#(SeqLen, BatchSize, HiddenSize)
embedding = self.embedding(input)
#对数据计算过程提速
#需要得到嵌入层的结果(输入数据)及每条输入数据的长度
gru_input = pack_padded_sequence(embedding, seq_length)
output, hidden = self.gru(gru_input, hidden)
#如果是双向神经网络会有h_N^f以及h_1^b两个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 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
N_CHARS = 128
HIDDEN_SIZE = 1
N_LAYER = 1
N_EPOCHS =100
#迁移至GPU
classifier = RNNClassifier(N_CHARS, HIDDEN_SIZE, N_COUNTRY, N_LAYER)
#迁移至GPU
if USE_GPU:
device = torch.device("cuda:0")
classifier.to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(classifier.parameters(), lr=0.001)
start = time.time()
print("Training for %d epochs ... " % N_EPOCHS)
#记录训练准确率
acc_list = []
for epoch in range(1, N_EPOCHS+1):
#训练模型
trainModel()
#检测模型
acc = testModel()
acc_list.append(acc)
#绘制图像
epoch = np.arange(1, len(acc_list)+1, 1)
acc_list = np.array(acc_list)
plt.plot(epoch, acc_list)
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.grid()
plt.show()
# if __name__ == '__main__':
# '''
# N_CHARS:字符数量,英文字母转变为One-Hot向量
# HIDDEN_SIZE:GRU输出的隐层的维度
# N_COUNTRY:分类的类别总数
# N_LAYER:GRU层数
# '''
# classifier = RNNClassifier(N_CHARS, HIDDEN_SIZE, N_COUNTRY, N_LAYER)
# #迁移至GPU
# if USE_GPU:
# device = torch.device("cuda:0")
# classifier.to(device)
# criterion = torch.nn.CrossEntropyLoss()
# optimizer = torch.optim.Adam(classifier.parameters(), lr=0.001)
# start = time.time()
# print("Training for %d epochs ... " % N_EPOCHS)
# #记录训练准确率
# acc_list = []
# for epoch in range(1, N_EPOCHS+1):
# #训练模型
# trainModel()
# #检测模型
# acc = testModel()
# acc_list.append(acc)
# #绘制图像
# epoch = np.arange(1, len(acc_list)+1, 1)
# acc_list = np.array(acc_list)
# plt.plot(epoch, acc_list)
# plt.xlabel('Epoch')
# plt.ylabel('Accuracy')
# plt.grid()
# plt.show()
结果如下:
我没用GPU训练