Pytorch学习记录-torchtext和Pytorch的实例5
0. PyTorch Seq2Seq项目介绍
在完成基本的torchtext之后，找到了这个教程，《基于Pytorch和torchtext来理解和实现seq2seq模型》。
这个项目主要包括了6个子项目
使用神经网络训练Seq2Seq使用RNN encoder-decoder训练短语表示用于统计机器翻译使用共同学习完成NMT的堆砌和翻译打包填充序列、掩码和推理- 卷积Seq2Seq
- Transformer
5. 卷积Seq2Seq
5.1 准备数据
5.2 构建模型
5.3 训练模型
INPUT_DIM = len(SRC.vocab)
OUTPUT_DIM = len(TRG.vocab)
EMB_DIM = 256
HID_DIM = 512
ENC_LAYERS = 10
DEC_LAYERS = 10
ENC_KERNEL_SIZE = 3
DEC_KERNEL_SIZE = 3
ENC_DROPOUT = 0.25
DEC_DROPOUT = 0.25
PAD_IDX = TRG.vocab.stoi['']
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
enc = Encoder(INPUT_DIM, EMB_DIM, HID_DIM, ENC_LAYERS, ENC_KERNEL_SIZE, ENC_DROPOUT, device)
dec = Decoder(OUTPUT_DIM, EMB_DIM, HID_DIM, DEC_LAYERS, DEC_KERNEL_SIZE, DEC_DROPOUT, PAD_IDX, device)
model = Seq2Seq(enc, dec, device).to(device)
model
Seq2Seq(
(encoder): Encoder(
(embedding): Embedding(7853, 256)
(rnn): GRU(256, 512, bidirectional=True)
(fc): Linear(in_features=1024, out_features=512, bias=True)
(dropout): Dropout(p=0.5)
)
(decoder): Decoder(
(attention): Attention(
(attn): Linear(in_features=1536, out_features=512, bias=True)
)
(embedding): Embedding(5893, 256)
(rnn): GRU(1280, 512)
(out): Linear(in_features=1792, out_features=5893, bias=True)
(dropout): Dropout(p=0.5)
)
)
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'The model has {count_parameters(model):,} trainable parameters')
The model has 37,351,685 trainable parameters
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss(ignore_index = PAD_IDX)
def train(model, iterator, optimizer, criterion, clip):
model.train()
epoch_loss = 0
for i, batch in enumerate(iterator):
src = batch.src
trg = batch.trg
optimizer.zero_grad()
output, _ = model(src, trg[:,:-1])
#output = [batch size, trg sent len - 1, output dim]
#trg = [batch size, trg sent len]
output = output.contiguous().view(-1, output.shape[-1])
trg = trg[:,1:].contiguous().view(-1)
#output = [batch size * trg sent len - 1, output dim]
#trg = [batch size * trg sent len - 1]
loss = criterion(output, trg)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()
epoch_loss += loss.item()
return epoch_loss / len(iterator)
def evaluate(model, iterator, criterion):
model.eval()
epoch_loss = 0
with torch.no_grad():
for i, batch in enumerate(iterator):
src = batch.src
trg = batch.trg
output, _ = model(src, trg[:,:-1])
#output = [batch size, trg sent len - 1, output dim]
#trg = [batch size, trg sent len]
output = output.contiguous().view(-1, output.shape[-1])
trg = trg[:,1:].contiguous().view(-1)
#output = [batch size * trg sent len - 1, output dim]
#trg = [batch size * trg sent len - 1]
loss = criterion(output, trg)
epoch_loss += loss.item()
return epoch_loss / len(iterator)
def epoch_time(start_time, end_time):
elapsed_time = end_time - start_time
elapsed_mins = int(elapsed_time / 60)
elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
return elapsed_mins, elapsed_secs
N_EPOCHS = 10
CLIP = 1
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
start_time = time.time()
train_loss = train(model, train_iterator, optimizer, criterion, CLIP)
valid_loss = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), 'tut5-model.pt')
print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')
print(f'\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')
print(f'\t Val. Loss: {valid_loss:.3f} | Val. PPL: {math.exp(valid_loss):7.3f}')
# 10个epoch
Epoch: 01 | Time: 1m 6s
Train Loss: 4.154 | Train PPL: 63.715
Val. Loss: 2.897 | Val. PPL: 18.116
Epoch: 02 | Time: 1m 6s
Train Loss: 2.952 | Train PPL: 19.140
Val. Loss: 2.368 | Val. PPL: 10.680
Epoch: 03 | Time: 1m 6s
Train Loss: 2.556 | Train PPL: 12.884
Val. Loss: 2.125 | Val. PPL: 8.370
Epoch: 04 | Time: 1m 6s
Train Loss: 2.335 | Train PPL: 10.334
Val. Loss: 1.987 | Val. PPL: 7.291
Epoch: 05 | Time: 1m 6s
Train Loss: 2.193 | Train PPL: 8.966
Val. Loss: 1.926 | Val. PPL: 6.862
Epoch: 06 | Time: 1m 6s
Train Loss: 2.089 | Train PPL: 8.074
Val. Loss: 1.878 | Val. PPL: 6.538
Epoch: 07 | Time: 1m 6s
Train Loss: 2.011 | Train PPL: 7.470
Val. Loss: 1.835 | Val. PPL: 6.264
Epoch: 08 | Time: 1m 6s
Train Loss: 1.946 | Train PPL: 7.001
Val. Loss: 1.818 | Val. PPL: 6.159
Epoch: 09 | Time: 1m 6s
Train Loss: 1.890 | Train PPL: 6.621
Val. Loss: 1.802 | Val. PPL: 6.064
Epoch: 10 | Time: 1m 6s
Train Loss: 1.850 | Train PPL: 6.359
Val. Loss: 1.790 | Val. PPL: 5.988
# 20个epoch
Epoch: 01 | Time: 1m 6s
Train Loss: 1.815 | Train PPL: 6.144
Val. Loss: 1.771 | Val. PPL: 5.880
Epoch: 02 | Time: 1m 6s
Train Loss: 1.779 | Train PPL: 5.926
Val. Loss: 1.753 | Val. PPL: 5.772
Epoch: 03 | Time: 1m 6s
Train Loss: 1.751 | Train PPL: 5.759
Val. Loss: 1.732 | Val. PPL: 5.651
Epoch: 04 | Time: 1m 6s
Train Loss: 1.723 | Train PPL: 5.600
Val. Loss: 1.735 | Val. PPL: 5.671
Epoch: 05 | Time: 1m 6s
Train Loss: 1.700 | Train PPL: 5.472
Val. Loss: 1.736 | Val. PPL: 5.672
Epoch: 06 | Time: 1m 6s
Train Loss: 1.674 | Train PPL: 5.333
Val. Loss: 1.721 | Val. PPL: 5.589
Epoch: 07 | Time: 1m 6s
Train Loss: 1.651 | Train PPL: 5.211
Val. Loss: 1.720 | Val. PPL: 5.587
Epoch: 08 | Time: 1m 6s
Train Loss: 1.631 | Train PPL: 5.108
Val. Loss: 1.720 | Val. PPL: 5.585
Epoch: 09 | Time: 1m 6s
Train Loss: 1.613 | Train PPL: 5.020
Val. Loss: 1.722 | Val. PPL: 5.596
Epoch: 10 | Time: 1m 6s
Train Loss: 1.590 | Train PPL: 4.905
Val. Loss: 1.708 | Val. PPL: 5.520
Epoch: 11 | Time: 1m 6s
Train Loss: 1.579 | Train PPL: 4.848
Val. Loss: 1.719 | Val. PPL: 5.577
Epoch: 12 | Time: 1m 6s
Train Loss: 1.562 | Train PPL: 4.770
Val. Loss: 1.728 | Val. PPL: 5.632
Epoch: 13 | Time: 1m 6s
Train Loss: 1.552 | Train PPL: 4.719
Val. Loss: 1.703 | Val. PPL: 5.493
Epoch: 14 | Time: 1m 6s
Train Loss: 1.539 | Train PPL: 4.660
Val. Loss: 1.723 | Val. PPL: 5.602
Epoch: 15 | Time: 1m 6s
Train Loss: 1.526 | Train PPL: 4.598
Val. Loss: 1.710 | Val. PPL: 5.529
Epoch: 16 | Time: 1m 6s
Train Loss: 1.518 | Train PPL: 4.565
Val. Loss: 1.704 | Val. PPL: 5.494
Epoch: 17 | Time: 1m 6s
Train Loss: 1.517 | Train PPL: 4.560
Val. Loss: 1.726 | Val. PPL: 5.616
Epoch: 18 | Time: 1m 6s
Train Loss: 2.414 | Train PPL: 11.177
Val. Loss: 2.562 | Val. PPL: 12.961
Epoch: 19 | Time: 1m 6s
Train Loss: 2.830 | Train PPL: 16.952
Val. Loss: 2.583 | Val. PPL: 13.240
Epoch: 20 | Time: 1m 6s
Train Loss: 12.083 | Train PPL: 176818.618
Val. Loss: 15.417 | Val. PPL: 4961313.167
感谢Colab，要不这么多计算量我得把笔记本显卡跑废