def main(config):
if not os.path.exists(config.model_dir):
os.makedirs(config.model_dir)
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
print("\t \t \t the model name is {}".format(config.model_name))
device, n_gpu = get_device()
torch.manual_seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.deterministic = True # cudnn 使用确定性算法，保证每次结果一样
'''数据准备'''
text_field = data.Field(tokenize='spacy',
lower=True,
include_lengths=True,
fix_length=config.sequence_length)
label_field = data.LabelField(dtype=torch.long)
train_iterator, dev_iterator, test_iterator = load_sst2(
config.data_path, text_field, label_field, config.batch_size, device,
config.glove_word_file, config.cache_path)
'''词向量准备'''
pretrained_embeddings = text_field.vocab.vectors
model_file = config.model_dir + 'model1.pt'
'''模型准备'''
if config.model_name == 'TextRNN':
from TextRNN import TextRNN
model = TextRNN.TextRNN(config.glove_word_dim, config.output_dim,
config.hidden_size, config.num_layers,
config.bidirectional, config.dropout,
pretrained_embeddings)
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()
if config.do_train:
train(config.epoch_num, model, train_iterator, dev_iterator, optimizer,
criterion, ['0', '1'], model_file, config.log_dir,
config.print_step, 'word')
model.load_state_dict(torch.load(model_file))
test_loss, test_acc, test_report = evaluate(model, test_iterator,
criterion, ['0', '1'], 'word')
print("-------------- Test -------------")
print("\t Loss: {} | Acc: {} | Macro avg F1: {} | Weighted avg F1: {}".
format(test_loss, test_acc, test_report['macro avg']['f1-score'],
test_report['weighted avg']['f1-score']))
def main(config):
print("\t \t \t the model name is {}".format(config.model_name))
device, n_gpu = get_device()
torch.manual_seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.deterministic = True # cudnn 使用确定性算法，保证每次结果一样
""" sst2 数据准备 """
text_field = data.Field(tokenize='spacy',
lower=True,
include_lengths=True,
fix_length=config.sequence_length)
label_field = data.LabelField(dtype=torch.long)
train_iterator, dev_iterator, test_iterator = load_sst2(
config.data_path, text_field, label_field, config.batch_size, device,
config.glove_word_file)
""" 词向量准备 """
pretrained_embeddings = text_field.vocab.vectors
""" 模型准备 """
if config.model_name == "TextCNN":
filter_sizes = [int(val) for val in config.filter_sizes.split()]
model = TextCNN.TextCNN(config.glove_word_dim, config.filter_num,
filter_sizes, config.output_dim,
config.dropout, pretrained_embeddings)
elif config.model_name == "TextRNN":
model = TextRNN.TextRNN(config.glove_word_dim, config.output_dim,
config.hidden_size, config.num_layers,
config.bidirectional, config.dropout,
pretrained_embeddings)
elif config.model_name == "LSTMATT":
model = LSTMATT.LSTMATT(config.glove_word_dim, config.output_dim,
config.hidden_size, config.num_layers,
config.bidirectional, config.dropout,
pretrained_embeddings)
elif config.model_name == 'TextRCNN':
model = TextRCNN.TextRCNN(config.glove_word_dim, config.output_dim,
config.hidden_size, config.num_layers,
config.bidirectional, config.dropout,
pretrained_embeddings)
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()
model = model.to(device)
criterion = criterion.to(device)
best_dev_loss = float('inf')
for epoch in range(config.epoch_num):
start_time = time.time()
train_loss, train_acc, train_report = train(model, train_iterator,
optimizer, criterion,
config.output_dim)
dev_loss, dev_acc, dev_report = evaluate(model, dev_iterator,
criterion, config.output_dim)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if dev_loss < best_dev_loss:
best_dev_loss = dev_loss
torch.save(model.state_dict(), 'tut2-model.pt')
print(
f'---------------- Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s ----------'
)
print("-------------- Train -------------")
print(f'\t \t Loss: {train_loss:.3f} | Acc: {train_acc*100: .2f} %')
print('{}'.format(train_report))
print("-------------- Dev -------------")
print(f'\t \t Loss: {dev_loss: .3f} | Acc: {dev_acc*100: .2f} %')
print('{}'.format(dev_report))
model.load_state_dict(torch.load('tut2-model.pt'))
test_loss, test_acc, test_report = evaluate(model, test_iterator,
criterion, config.output_dim)
print("-------------- Test -------------")
print(f'\t \t Loss: {test_loss: .3f} | Acc: {test_acc*100: .2f} %')
print('{}'.format(test_report))