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| import time
import numpy as np
import torch
import torch.nn as nn
import pandas as pd
from torch.nn.utils.rnn import pack_padded_sequence
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
start_time = time.time()
HIDDEN_SIZE = 100
BATCH_SIZE = 256
N_LAYER = 2
N_EPOCH = 5
N_CHAR = 128
USE_GPU = False
class NameDataset(Dataset):
def __init__(self, filename):
df = pd.read_csv(filename, header=None)
self.len = df.shape[0]
self.names = df[0]
self.countries = df[1]
self.country_list = list(sorted(set(self.countries)))
self.country_dict = self.get_country_dict()
self.country_num = len(self.country_list)
def __getitem__(self, idx):
return self.names[idx], self.country_dict[self.countries[idx]]
def __len__(self):
return self.len
def get_country_dict(self):
country_dict = dict()
for idx, country_name in enumerate(self.country_list):
country_dict[country_name] = idx
return country_dict
def idx2country(self, idx):
return self.country_list[idx]
def get_countries_num(self):
return self.country_num
train_dataset = NameDataset('./dataset/name/names_train.csv')
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_dataset = NameDataset('./dataset/name/names_test.csv')
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)
N_COUNTRY = train_dataset.get_countries_num()
class RNNClassifier(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 = nn.Embedding(input_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size, n_layers, bidirectional=bidirectional)
self.fc = 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 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 = 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
classifier = RNNClassifier(N_CHAR, HIDDEN_SIZE, N_COUNTRY, N_LAYER)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(classifier.parameters(), lr=0.001)
def time_since(since):
s = time.time() - since
return '%dm %ds' % (s // 60, s % 60)
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 = [sl[0] for sl in sequences_and_lengths]
seq_lengths = torch.LongTensor([sl[1] for sl 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)):
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 seq_tensor, seq_lengths, countries
def train():
total_loss = 0
for i, (names, countries) in enumerate(train_loader, 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_time)}] Epoch {epoch} ', end='')
print(f'[{i * len(inputs)}/{len(train_dataset)}] ', end='')
print(f'loss={total_loss / (i * len(inputs))}')
def test():
correct = 0
total = len(test_dataset)
print('evaluating trained model...')
with torch.no_grad():
for i, (names, countries) in enumerate(test_loader, 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__":
import os
max_acc = 0
filename = './model/name_max_acc.txt'
if not os.path.isfile(filename):
with open(filename, 'w') as f:
f.write(str(max_acc))
else:
with open(filename, 'r') as f:
for line in f:
max_acc = float(line)
print('max_acc', max_acc)
if os.path.exists('./model/name.pkl'):
classifier.load_state_dict(torch.load('./model/name.pkl'))
print('Training for %d epochs...' % N_EPOCH)
acc_list = []
for epoch in range(1, N_EPOCH + 1):
train()
acc = test()
acc_list.append(acc)
if acc > max_acc:
max_acc = acc
with open(filename, 'w') as f:
f.write(str(max_acc))
print("update model")
torch.save(classifier.state_dict(), './model/name.pkl')
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()
|
