inputs = train_set[i]
labels = train_labels[i]
if torch.cuda.is_available():
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if (epoch + 1) % args.save_frequency == 0:
path = os.path.join(args.results_dir, "{0}-{1:04d}.tar".format(args.checkpoint_prefix, epoch + 1))
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'running_loss': running_loss
}, path)
print('\nModel saved to %s' % path)
if (epoch + 1) % args.display_frequency == 0:
print('\nEpoch %d:\n\tLoss = %.3f' % (epoch + 1, running_loss / args.display_frequency))
running_loss = 0.0
model = model.eval()
correct = 0.0
total = 0.0
for k in range(len(train_set)):
inputs = train_set[k]
labels = train_labels[k]
if torch.cuda.is_available():
inputs = inputs.cuda()
ax1.set_xticks(range(len(index_list)))
ax1.set_xticklabels(range(len(index_list)))
df2.plot(ax=ax2, kind='line', rot=360, grid='on')
ax2.set_xticks(range(Epoch))
ax2.set_xticklabels(range(Epoch))
plt.show()
"""
USE_Bi=True
"""
w2v = EmbeddingModel(vocab_size, embedding_dim)
checkpoint = torch.load('Model/checkpoint.pth2.tar')
w2v.load_state_dict(checkpoint['state_dict']) # 模型参数
print(w2v.state_dict()["in_embed.weight"])
"""
if USE_Bi:
print("Using BiLSTM")
model = BiLSTM_Match(w2v,embedding_dim, hidden_dim, vocab_size, target, Batchsize, stringlen)
model_path = "./Model/BiLSTMmodel.pth"
else:
print("Using LSTM")
model = LSTM_Match(embedding_dim, hidden_dim, vocab_size,target,Batchsize,stringlen)
model_path = "./Model/LSTMmodel.pth"
print(w2v.in_embed==model.word_embeddings)
print(model)
if USE_CUDA: