def test(model, val_iter, vocab, reg_lambda, recon_lambda):
model.eval()
loss_checker = LossChecker(4)
PAD_idx = vocab.word2idx['<PAD>']
for b, batch in enumerate(val_iter, 1):
_, feats, captions = parse_batch(batch)
output, feats_recon = model(feats)
cross_entropy_loss = F.nll_loss(output[1:].view(-1, vocab.n_vocabs),
captions[1:].contiguous().view(-1),
ignore_index=PAD_idx)
entropy_loss = losses.entropy_loss(
output[1:], ignore_mask=(captions[1:] == PAD_idx))
if model.reconstructor is None:
reconstruction_loss = torch.zeros(1)
elif model.reconstructor._type == 'global':
reconstruction_loss = losses.global_reconstruction_loss(
feats, feats_recon, keep_mask=(captions != PAD_idx))
else:
reconstruction_loss = losses.local_reconstruction_loss(
feats, feats_recon)
loss = cross_entropy_loss + reg_lambda * entropy_loss + recon_lambda * reconstruction_loss
loss_checker.update(loss.item(), cross_entropy_loss.item(),
entropy_loss.item(), reconstruction_loss.item())
total_loss, cross_entropy_loss, entropy_loss, reconstruction_loss = loss_checker.mean(
)
loss = {
'total': total_loss,
'cross_entropy': cross_entropy_loss,
'entropy': entropy_loss,
'reconstruction': reconstruction_loss,
}
return loss
def train(e, model, optimizer, train_iter, vocab, teacher_forcing_ratio, reg_lambda, gradient_clip):
model.train()
loss_checker = LossChecker(3)
PAD_idx = vocab.word2idx['<PAD>']
for b, batch in enumerate(train_iter, 1):
_, feats, captions = parse_batch(batch)
optimizer.zero_grad()
output = model(feats, captions, teacher_forcing_ratio)
cross_entropy_loss = F.nll_loss(output[1:].view(-1, vocab.n_vocabs),
captions[1:].contiguous().view(-1),
ignore_index=PAD_idx)
entropy_loss = reg_lambda * losses.entropy_loss(output[1:], ignore_mask=(captions[1:] == PAD_idx))
loss = cross_entropy_loss + entropy_loss
loss.backward()
if gradient_clip is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), gradient_clip)
optimizer.step()
loss_checker.update(loss.item(), cross_entropy_loss.item(), entropy_loss.item())
if len(train_iter) < 10 or b % (len(train_iter) // 10) == 0:
inter_loss, inter_cross_entropy_loss, inter_entropy_loss = loss_checker.mean(last=10)
print("\t[{:d}/{:d}] loss: {:.4f} (CE {:.4f} + E {:.4f})".format(
b, len(train_iter), inter_loss, inter_cross_entropy_loss, inter_entropy_loss))
total_loss, cross_entropy_loss, entropy_loss = loss_checker.mean()
loss = {
'total': total_loss,
'cross_entropy': cross_entropy_loss,
'entropy': entropy_loss,
}
return loss
def train(e, model, optimizer, train_iter, vocab, teacher_forcing_ratio,
reg_lambda, gradient_clip):
model.train()
loss_checker = LossChecker(3)
PAD_idx = vocab.word2idx['<PAD>']
t = tqdm(train_iter)
for batch in t:
_, feats, captions = parse_batch(batch)
optimizer.zero_grad()
output = model(feats, captions, teacher_forcing_ratio)
cross_entropy_loss = F.nll_loss(output[1:].view(-1, vocab.n_vocabs),
captions[1:].contiguous().view(-1),
ignore_index=PAD_idx)
entropy_loss = losses.entropy_loss(
output[1:], ignore_mask=(captions[1:] == PAD_idx))
loss = cross_entropy_loss + reg_lambda * entropy_loss
loss.backward()
if gradient_clip is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), gradient_clip)
optimizer.step()
loss_checker.update(loss.item(), cross_entropy_loss.item(),
entropy_loss.item())
t.set_description(
"[Epoch #{0}] loss: {2:.3f} = (CE: {3:.3f}) + (Ent: {1} * {4:.3f})"
.format(e, reg_lambda, *loss_checker.mean(last=10)))
total_loss, cross_entropy_loss, entropy_loss = loss_checker.mean()
loss = {
'total': total_loss,
'cross_entropy': cross_entropy_loss,
'entropy': entropy_loss,
}
return loss
def train(e, model, optimizer, train_iter, vocab, teacher_forcing_ratio,
reg_lambda, recon_lambda, gradient_clip):
model.train()
loss_checker = LossChecker(4)
PAD_idx = vocab.word2idx['<PAD>']
#Python进度条
t = tqdm(train_iter)
for batch in t:
#变量放GPU上
_, feats, captions = parse_batch(batch)
optimizer.zero_grad()
#解码器输出、重构器
output, feats_recon = model(feats, captions, teacher_forcing_ratio)
#最大似然 / log似然代价函数NLLLoss的输入是一个对数概率向量和一个目标标签. 它不会为我们计算对数概率. 适合网络的最后一层是log_softmax. 交叉熵损失
#
cross_entropy_loss = F.nll_loss(output[1:].view(-1, vocab.n_vocabs),
captions[1:].contiguous().view(-1),
ignore_index=PAD_idx)
#???????????????????????????????????????
entropy_loss = losses.entropy_loss(
output[1:], ignore_mask=(captions[1:] == PAD_idx))
#reg_lambda=0
loss = cross_entropy_loss + reg_lambda * entropy_loss
if model.reconstructor is None:
reconstruction_loss = torch.zeros(1)
else:
if model.reconstructor._type == 'global':
reconstruction_loss = losses.global_reconstruction_loss(
feats, feats_recon, keep_mask=(captions != PAD_idx))
else:
reconstruction_loss = losses.local_reconstruction_loss(
feats, feats_recon)
loss += recon_lambda * reconstruction_loss
loss.backward()
if gradient_clip is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), gradient_clip)
optimizer.step()
loss_checker.update(loss.item(), cross_entropy_loss.item(),
entropy_loss.item(), reconstruction_loss.item())
#.3f 保留三位小数
t.set_description(
"[Epoch #{0}] loss: {3:.3f} = (CE: {4:.3f}) + (Ent: {1} * {5:.3f}) + (Rec: {2} * {6:.3f})"
.format(e, reg_lambda, recon_lambda, *loss_checker.mean(last=10)))
total_loss, cross_entropy_loss, entropy_loss, reconstruction_loss = loss_checker.mean(
)
loss = {
'total': total_loss,
'cross_entropy': cross_entropy_loss,
'entropy': entropy_loss,
'reconstruction': reconstruction_loss,
}
return loss
def evaluate(model, val_iter, vocab, reg_lambda):
model.eval()
loss_checker = LossChecker(3)
PAD_idx = vocab.word2idx['<PAD>']
for b, batch in enumerate(val_iter, 1):
_, feats, captions = parse_batch(batch)
output = model(feats, captions)
cross_entropy_loss = F.nll_loss(output[1:].view(-1, vocab.n_vocabs),
captions[1:].contiguous().view(-1),
ignore_index=PAD_idx)
entropy_loss = reg_lambda * losses.entropy_loss(output[1:], ignore_mask=(captions[1:] == PAD_idx))
loss = cross_entropy_loss + entropy_loss
loss_checker.update(loss.item(), cross_entropy_loss.item(), entropy_loss.item())
total_loss, cross_entropy_loss, entropy_loss = loss_checker.mean()
loss = {
'total': total_loss,
'cross_entropy': cross_entropy_loss,
'entropy': entropy_loss,
}
return loss
