def train(net_D,
net_G,
data_iter,
num_epochs,
lr,
latent_dim,
device=d2l.try_gpu()):
loss = torch.nn.BCEWithLogitsLoss()
# net_D.initialize(init=init.Normal(0.02), force_reinit=True, ctx=device)
# net_G.initialize(init=init.Normal(0.02), force_reinit=True, ctx=device)
trainer_hp = {'lr': lr, 'betas': [0.5, 0.999]}
trainer_D = torch.optim.Adam(net_D.parameters(), **trainer_hp)
trainer_G = torch.optim.Adam(net_G.parameters(), **trainer_hp)
for epoch in range(1, num_epochs + 1):
print('Epoch', epoch)
# Train one epoch
timer = d2l.Timer()
metric = d2l.Accumulator(3) # loss_D, loss_G, num_examples
for X, _ in data_iter:
print('Processing batch')
batch_size = X.shape[0]
Z = torch.normal(0, 1, size=(batch_size, latent_dim, 1, 1))
# X, Z = X.as_in_ctx(device), Z.as_in_ctx(device),
metric.add(update_D(X, Z, net_D, net_G, loss, trainer_D),
update_G(Z, net_D, net_G, loss, trainer_G), batch_size)
# Show the losses
loss_D, loss_G = metric[0] / metric[2], metric[1] / metric[2]
print(f'loss_D {loss_D:.3f}, loss_G {loss_G:.3f}')
print(f'loss_D {loss_D:.3f}, loss_G {loss_G:.3f}, '
f'{metric[2] / timer.stop():.1f} examples/sec on {str(device)}')
def train_epoch_ch8(net, train_iter, loss, updater, device, use_random_iter):
state, timer = None, d2l.Timer()
metric = d2l.Accumulator(2) # 训练损失之和, 标记数量
for X, Y in train_iter:
if state is None or use_random_iter:
# 在第一次迭代或使用随机抽样时初始化`state`
state = net.begin_state(batch_size=X.shape[0],
device=device) # 初始化其实隐藏状态H0
else:
if isinstance(net, nn.Module) and not isinstance(state, tuple):
# `state`对于`nn.GRU`是个张量
state.detach_()
else:
# `state`对于`nn.LSTM`或对于我们从零开始实现的模型是个张量
for s in state:
s.detach_()
y = Y.T.reshape(-1)
X, y = X.to(device), y.to(device)
y_hat, state = net(X, state) # __call__
l = loss(y_hat, y.long()).mean()
if isinstance(updater, torch.optim.Optimizer):
updater.zero_grad()
l.backward()
grad_clipping(net, 1)
updater.step()
else:
l.backward()
grad_clipping(net, 1)
# 因为已经调用了`mean`函数
updater(batch_size=1)
metric.add(l * y.numel(), y.numel())
return math.exp(metric[0] / metric[1]), metric[1] / timer.stop()
def train_epoch_ch8(net, train_iter, loss, updater, device, use_random_iter):
state, timer = None, d2l.Timer()
metric = d2l.Accumulator(2)
for X, Y in train_iter:
if state is None or use_random_iter:
state = net.begin_state(batch_size=X.shape[0], device=device)
else:
if isinstance(net, nn.Module) and not isinstance(state, tuple):
state.detach_()
else:
for s in state:
s.detach_()
y = Y.T.reshape(-1)
X, y = X.to(device), y.to(device)
y_hat, state = net(X, state)
l = loss(y_hat, y.long()).mean()
if isinstance(updater, torch.optim.Optimizer):
updater.zero_grad()
l.backward()
grad_clipping(net, 1)
updater.step()
else:
l.backward()
grad_clipping(net, 1)
updater(batch_size=1)
metric.add(l * y.numel(), y.numel())
return math.exp(metric[0] / metric[1]), metric[1] / timer.stop()
def train_epoch_ch8(model, train_iter, loss, updater, device, #@save
use_random_iter):
"""Train a model within one epoch (defined in Chapter 8)."""
state, timer = None, d2l.Timer()
metric = d2l.Accumulator(2) # Sum of training loss, no. of tokens
for X, Y in train_iter:
if state is None or use_random_iter:
# Initialize `state` when either it is the first iteration or
# using random sampling
state = model.begin_state(batch_size=X.shape[0], device=device)
else:
if isinstance(model, nn.Module) and not isinstance(state, tuple):
# `state` is a tensor for `nn.GRU`
state.detach_()
else:
# `state` is a tuple of tensors for `nn.LSTM` and
# for our custom scratch implementation
for s in state:
s.detach_()
y = Y.T.reshape(-1)
X, y = X.to(device), y.to(device)
y_hat, state = model(X, state)
l = loss(y_hat, y.long()).mean()
if isinstance(updater, torch.optim.Optimizer):
updater.zero_grad()
l.backward()
grad_clipping(model, 1)
updater.step()
else:
l.backward()
grad_clipping(model, 1)
# Since the `mean` function has been invoked
updater(batch_size=1)
metric.add(l * d2l.size(y), d2l.size(y))
return math.exp(metric[0] / metric[1]), metric[1] / timer.stop()
def train(net, data_iter, lr, num_epochs, device=d2l.try_gpu()):
def init_weights(m):
if type(m) == nn.Embedding:
nn.init.xavier_uniform_(m.weight)
net.apply(init_weights)
net = net.to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
animator = d2l.Animator(xlabel='epoch',
ylabel='loss',
xlim=[1, num_epochs])
metric = d2l.Accumulator(2) # sum of losses, no. of tokens
for epoch in range(num_epochs):
timer, num_batches = d2l.Timer(), len(data_iter)
for i, batch in enumerate(data_iter):
optimizer.zero_grad()
center, context_negative, mask, label = [
data.to(device) for data in batch
]
pred = skip_gram(center, context_negative, net[0], net[1])
l = (loss(pred.reshape(label.shape).float(), label.float(), mask) /
mask.sum(axis=1) * mask.shape[1])
l.sum().backward()
optimizer.step()
metric.add(l.sum(), l.numel())
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[1], ))
print(f'loss {metric[0] / metric[1]:.3f}, '
f'{metric[1] / timer.stop():.1f} tokens/sec on {str(device)}')
def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay):
# Only train the small custom output network
net = nn.DataParallel(net, device_ids=devices).to(devices[0])
trainer = torch.optim.SGD(
(param for param in net.parameters() if param.requires_grad),
lr=lr,
momentum=0.9,
weight_decay=wd)
scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)
num_batches, timer = len(train_iter), d2l.Timer()
animator = d2l.Animator(xlabel='epoch',
xlim=[1, num_epochs],
legend=['train loss', 'valid loss'])
for epoch in range(num_epochs):
metric = d2l.Accumulator(2)
for i, (img1s, img2s, labels) in enumerate(train_iter):
timer.start()
# push onto gpu
img1s = img1s.to(devices[0])
img2s = img2s.to(devices[0])
labels = labels.to(devices[0])
trainer.zero_grad()
import ipdb
ipdb.set_trace() # TODO BREAKPOINT
# TODO YOU ARE HERE - choose how to cat the imgs
# Then prepare the network to accept...
# ipdb> torch.cat((img1s, img2s), 1).shape
# torch.Size([4, 6, 224, 224])
# ipdb> torch.cat((img1s, img2s), 0).shape
# torch.Size([8, 3, 224, 224])
# ipdb> torch.cat((img1s, img2s), 3).shape
# torch.Size([4, 3, 224, 448])
output1s = net(img1s)
output2s = net(img2s)
l = loss(output, labels).sum()
l.backward()
trainer.step()
metric.add(l, labels.shape[0])
timer.stop()
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[1], None))
if valid_iter is not None:
valid_loss = evaluate_loss(valid_iter, net, devices)
animator.add(epoch + 1, (None, valid_loss))
scheduler.step()
if valid_iter is not None:
print(f'train loss {metric[0] / metric[1]:.3f}, '
f'valid loss {valid_loss:.3f}')
else:
print(f'train loss {metric[0] / metric[1]:.3f}')
print(f'{metric[1] * num_epochs / timer.sum():.1f} examples/sec '
f'on {str(devices)}')
def train_model(net,
train_iter,
test_iter,
num_epochs,
lr,
device=d2l.try_gpu()):
# for idx, (X, y) in enumerate(train_iter):
"""Train and evaluate a model with CPU or GPU."""
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
torch.nn.init.xavier_uniform_(m.weight) # Part 2.2
net.apply(init_weights)
print('training on', device)
net.to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
loss = nn.BCELoss()
animator = d2l.Animator(xlabel='epoch',
xlim=[0, num_epochs],
legend=['train loss', 'train acc', 'test acc'])
timer = d2l.Timer()
for epoch in range(num_epochs):
metric = d2l.Accumulator(3) # train_loss, train_acc, num_examples
for i, (X, y) in enumerate(train_iter):
timer.start()
net.train()
optimizer.zero_grad()
X = X.float()
X, y = X.to(device), y.to(device)
output = net(X)
# y_hat = torch.round(torch.exp(output)/(1+torch.exp(output)))
y_hat = torch.sigmoid(output)
y = y.to(torch.float)
y = torch.unsqueeze(y, 1)
l = loss(y_hat, y.type(torch.float32)) #.type(torch.float32)
l.backward()
optimizer.step()
with torch.no_grad():
metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0])
timer.stop()
train_loss, train_acc = metric[0] / metric[2], metric[1] / metric[2]
if (i + 1) % 50 == 0:
animator.add(epoch + i / len(train_iter),
(train_loss, train_acc, None))
print(
"BatchNo.=%3i, Epoch No.=%3i, loss=%.3f, train acc=%.3f" %
(i + 1, epoch + 1, train_loss, train_acc))
test_acc = evaluate_accuracy_gpu(net, test_iter)
print("test_acc=", test_acc)
animator.add(epoch + 1, (None, None, test_acc))
print('loss %.3f, train acc %.3f, test acc %.3f' %
(train_loss, train_acc, test_acc))
print('%.1f examples/sec on %s' %
(metric[2] * num_epochs / timer.sum(), device))
def train_ch6(net, train_iter, test_iter, num_epochs, lr, device):
"""用GPU训练模型(在第六章定义)
Defined in :numref:`sec_lenet`"""
print('training on', device)
net.to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss()
animator = d2l.Animator(xlabel='epoch',
xlim=[1, num_epochs],
legend=['train loss', 'train acc', 'test acc'])
timer = d2l.Timer()
num_batches = len(train_iter)
multiplier_anim = max(1, num_batches // 5)
multiplier_save = max(1, num_epochs // 10)
for epoch in tqdm(range(num_epochs)):
# 训练损失之和，训练准确率之和，样本数
metric = d2l.Accumulator(3)
net.train()
for i, (X, y) in enumerate(train_iter):
timer.start()
optimizer.zero_grad()
X, y = X.to(device), y.to(device)
y_hat = net(X)
l = loss(y_hat, y)
l.backward()
optimizer.step()
with torch.no_grad():
metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0])
timer.stop()
train_l = metric[0] / metric[2]
train_acc = metric[1] / metric[2]
if (i + 1) % multiplier_anim == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(train_l, train_acc, None))
if test_iter is not None:
test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)
else:
test_acc = 0
animator.add(epoch + 1, (None, None, test_acc))
# save net state_dict
if epoch == epochs - 1 or (epoch + 1) % multiplier_save == 0:
net_param_file = 'net_param_{:d}.pth'.format(epoch)
path_net = os.path.join(net_save_dir, net_param_file)
torch.save(net.state_dict(), path_net)
print('net param saved to \n\t{}'.format(path_net))
print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, '
f'test acc {test_acc:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec '
f'on {str(device)}')
def train_seq2seq(net, data_iter, lr, num_epochs, tgt_vocab, device):
"""Train a model for sequence to sequence."""
def xavier_init_weights(m):
if type(m) == nn.Linear:
nn.init.xavier_uniform_(m.weight)
if type(m) == nn.GRU:
for param in m._flat_weights_names:
if "weight" in param:
nn.init.xavier_uniform_(m._parameters[param])
net.apply(xavier_init_weights)
net.to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
loss = MaskedSoftmaxCELoss()
net.train()
animator = d2l.Animator(xlabel='epoch',
ylabel='loss',
xlim=[10, num_epochs])
for epoch in range(num_epochs):
timer = d2l.Timer()
metric = d2l.Accumulator(2) # Sum of training loss, no. of tokens
first = True
for i, batch in enumerate(data_iter):
X, X_valid_len, Y, Y_valid_len = [x.to(device) for x in batch]
bos = torch.tensor([tgt_vocab['<bos>']] * Y.shape[0],
device=device).reshape(-1, 1)
# 4. In training, replace teacher forcing with feeding the prediction at the previous time step into the
# decoder. How does this influence the performance?
if first:
dec_input = d2l.concat([bos, Y[:, :-1]], 1) # Teacher forcing
first = False
else:
dec_input = Y_hat.argmax(dim=2)
dec_input = dec_input[:X.shape[0]]
Y_hat, _ = net(X, dec_input, X_valid_len)
l = loss(Y_hat, Y, Y_valid_len)
l.sum().backward() # Make the loss scalar for `backward`
d2l.grad_clipping(net, 1)
num_tokens = Y_valid_len.sum()
optimizer.step()
with torch.no_grad():
metric.add(l.sum(), num_tokens)
if (epoch + 1) % 10 == 0:
animator.add(epoch + 1, (metric[0] / metric[1], ))
print(f'loss {metric[0] / metric[1]:.3f}, {metric[1] / timer.stop():.1f} '
f'tokens/sec on {str(device)}')
def train_bert(train_iter, net, loss, vocab_size, devices, num_steps):
net = nn.DataParallel(net, device_ids=devices).to(devices[0])
trainer = torch.optim.Adam(net.parameters(), lr=1e-3)
step, timer = 0, d2l.Timer()
animator = d2l.Animator(xlabel='step',
ylabel='loss',
xlim=[1, num_steps],
legend=['mlm', 'nsp'])
# Sum of masked language modeling losses, sum of next sentence prediction
# losses, no. of sentence pairs, count
metric = d2l.Accumulator(4)
num_steps_reached = False
while step < num_steps and not num_steps_reached:
for tokens_X, segments_X, valid_lens_x, pred_positions_X,\
mlm_weights_X, mlm_Y, nsp_y in train_iter:
tokens_X = tokens_X.to(devices[0])
segments_X = segments_X.to(devices[0])
valid_lens_x = valid_lens_x.to(devices[0])
pred_positions_X = pred_positions_X.to(devices[0])
mlm_weights_X = mlm_weights_X.to(devices[0])
mlm_Y, nsp_y = mlm_Y.to(devices[0]), nsp_y.to(devices[0])
trainer.zero_grad()
timer.start()
mlm_l, nsp_l, l = _get_batch_loss_bert(net, loss, vocab_size,
tokens_X, segments_X,
valid_lens_x,
pred_positions_X,
mlm_weights_X, mlm_Y, nsp_y)
l.backward()
trainer.step()
metric.add(mlm_l, nsp_l, tokens_X.shape[0], 1)
timer.stop()
animator.add(step + 1,
(metric[0] / metric[3], metric[1] / metric[3]))
step += 1
if step == num_steps:
num_steps_reached = True
break
print(f'MLM loss {metric[0] / metric[3]:.3f}, '
f'NSP loss {metric[1] / metric[3]:.3f}')
print(f'{metric[2] / timer.sum():.1f} sentences pairs/sec on '
f'{str(devices)}')
def train_ch6(net, train_iter, test_iter, num_epochs, lr,
device=d2l.try_gpu()):
"""Train a model with a GPU (defined in Chapter 6)."""
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
nn.init.xavier_uniform_(m.weight)
net.apply(init_weights)
print('training on', device)
net.to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss()
animator = d2l.Animator(xlabel='epoch',
xlim=[1, num_epochs],
legend=['train loss', 'train acc', 'test acc'])
timer, num_batches = d2l.Timer(), len(train_iter)
for epoch in range(num_epochs):
# Sum of training loss, sum of training accuracy, no. of examples
metric = d2l.Accumulator(3)
net.train()
for i, (X, y) in enumerate(train_iter):
timer.start()
optimizer.zero_grad()
X, y = X.to(device), y.to(device)
y_hat = net(X)
l = loss(y_hat, y)
l.backward()
optimizer.step()
with torch.no_grad():
metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0])
timer.stop()
train_l = metric[0] / metric[2]
train_acc = metric[1] / metric[2]
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(train_l, train_acc, None))
test_acc = evaluate_accuracy_gpu(net, test_iter)
animator.add(epoch + 1, (None, None, test_acc))
print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, '
f'test acc {test_acc:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec '
f'on {str(device)}')
plt.show()
def train_func(net,
train_iter,
test_iter,
num_epochs,
lr,
device=d2l.try_gpu()):
def init_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
torch.nn.init.xavier_uniform_(m.weight)
net.apply(init_weights)
print('training on', device)
net.to(device)
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
loss = nn.CrossEntropyLoss()
timer = d2l.Timer()
for epoch in range(num_epochs):
metric = d2l.Accumulator(3)
for i, (X, y) in enumerate(train_iter):
timer.start()
net.train()
optimizer.zero_grad()
X, y = X.to(device), y.to(device)
y_hat = net(X)
l = loss(y_hat, y)
l.backward()
optimizer.step()
with torch.no_grad():
metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0])
timer.stop()
train_loss = metric[0] / metric[2]
train_acc = metric[1] / metric[2]
if (i + 1) % 50 == 0:
print(f"epoch: {epoch} --- iter: {i} --- of {len(train_iter)}")
print(f"train loss: {train_loss} --- train acc: {train_acc}")
test_acc = evaluate_accuracy_gpu(net, test_iter)
print(f'loss {train_loss:.3f}, train acc {train_acc:.3f}, '
f'test acc {test_acc:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec '
f'on {str(device)}')
def train_bert(train_iter, net, loss, vocab_size, devices, num_steps):
def xavier_init_weights(m):
if type(m) is nn.Linear and\
len(m.weight.shape)>1:
torch.nn.init.xavier_uniform_(m.weight)
net.apply(xavier_init_weights)
optimizer = torch.optim.Adam(net.parameters())
step, timer = 0, d2l.Timer()
animator = d2l.Animator(xlabel='step',
ylabel='loss',
xlim=[1, num_steps],
legend=['mlm', 'nsp'])
# Sum of masked language modeling losses, sum of next sentence prediction
# losses, no. of sentence pairs, count
metric = d2l.Accumulator(4)
num_steps_reached = False
while step < num_steps and not num_steps_reached:
for batch in train_iter:
# temp = list((batch[i][j] for j in range(len(batch[0])) for i in range(len(batch))))
# (tokens_X_shards,segments_X_shards, valid_lens_x_shards,\
# pred_positions_X_shards, mlm_weights_X_shards,\
# mlm_Y_shards, nsp_y_shards)= tuple(temp[i:i+batch_size] for i in range(0,len(temp),batch_size))
timer.start()
optimizer.zero_grad()
mlm_ls, nsp_ls, ls = _get_batch_loss_bert(batch)
for l in ls:
l.backward()
optimizer.step()
timer.stop()
animator.add(step + 1, (metric[0] / metric[3], metric[1] / metric[3]))
step += 1
if step == num_steps:
num_steps_reached = True
break
print(f'MLM loss {metric[0] / metric[3]:.3f}, '
f'NSP loss {metric[1] / metric[3]:.3f}')
print(f'{metric[2] / timer.sum():.1f} sentence pairs/sec on '
f'{str(devices)}')
def train_epoch(self, net, train_iter, loss, updater, device,
use_random_iter):
"""Train a net within one epoch (defined in Chapter 8)."""
state, timer = None, d2l.Timer()
metric = d2l.Accumulator(2) # Sum of training loss, no. of tokens
i = 0
for X, Y in train_iter:
X = X.to(torch.float32) # TODO is this necessary?
Y = Y.to(torch.float32)
X = X.reshape(-1, *X.shape) # [direction, batch_size, seq_len]
if state is None or use_random_iter:
# Initialize `state` when either it is the first iteration or
# using random sampling
state = net.begin_state(batch_size=X.shape[1], device=device)
else:
if isinstance(net, nn.Module) and not isinstance(state, tuple):
# `state` is a tensor for `nn.GRU`
state.detach_()
else:
# `state` is a tuple of tensors for `nn.LSTM` and
# for our custom scratch implementation
for s in state:
s.detach_()
# y = Y.T.reshape(-1)
X, Y = X.to(device), Y.to(device)
y_hat, state = net(X, state)
l = loss(y_hat, Y).mean()
if isinstance(updater, torch.optim.Optimizer):
updater.zero_grad()
l.backward()
self.grad_clipping(net, 1)
updater.step()
else:
l.backward()
self.grad_clipping(net, 1)
# Since the `mean` function has been invoked
updater(batch_size=1)
metric.add(l * d2l.size(Y), d2l.size(Y))
return math.exp(metric[0] / metric[1]), metric[1] / timer.stop()
def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay):
# Only train the small custom output network
net = nn.DataParallel(net, device_ids=devices).to(devices[0])
trainer = torch.optim.SGD((param for param in net.parameters()
if param.requires_grad), lr=lr,
momentum=0.9, weight_decay=wd)
scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)
num_batches, timer = len(train_iter), d2l.Timer()
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],
legend=['train loss', 'valid loss'])
for epoch in range(num_epochs):
metric = d2l.Accumulator(2)
for i, (features, labels) in enumerate(train_iter):
timer.start()
features, labels = features.to(devices[0]), labels.to(devices[0])
trainer.zero_grad()
output = net(features)
l = loss(output, labels).sum()
l.backward()
trainer.step()
metric.add(l, labels.shape[0])
timer.stop()
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[1], None))
if valid_iter is not None:
valid_loss = evaluate_loss(valid_iter, net, devices)
animator.add(epoch + 1, (None, valid_loss))
scheduler.step()
if valid_iter is not None:
print(f'train loss {metric[0] / metric[1]:.3f}, '
f'valid loss {valid_loss:.3f}')
else:
print(f'train loss {metric[0] / metric[1]:.3f}')
print(f'{metric[1] * num_epochs / timer.sum():.1f} examples/sec '
f'on {str(devices)}')
def train_s2s_ch9(model, data_iter, lr, num_epochs, tgt_vocab, device):
"""Train a model for sequence to sequence (defined in Chapter 9)."""
def xavier_init_weights(m):
if type(m) == nn.Linear:
torch.nn.init.xavier_uniform_(m.weight)
if type(m) == nn.GRU:
for param in m._flat_weights_names:
if "weight" in param:
torch.nn.init.xavier_uniform_(m._parameters[param])
model.apply(xavier_init_weights)
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
loss = MaskedSoftmaxCELoss()
model.train()
# animator = d2l.Animator(xlabel='epoch', ylabel='loss',
# xlim=[10, num_epochs])
for epoch in range(num_epochs):
timer = d2l.Timer()
metric = d2l.Accumulator(2) # Sum of training loss, no. of tokens
for batch in data_iter:
X, X_valid_len, Y, Y_valid_len = [x.to(device) for x in batch]
bos = torch.tensor([tgt_vocab['<bos>']] * Y.shape[0],
device=device).reshape(-1, 1)
dec_input = torch.cat([bos, Y[:, :-1]], 1) # Teacher forcing
Y_hat, _ = model(X, dec_input, X_valid_len)
l = loss(Y_hat, Y, Y_valid_len)
l.sum().backward() # Make the loss scalar for `backward`
d2l.grad_clipping(model, 1)
num_tokens = Y_valid_len.sum()
optimizer.step()
with torch.no_grad():
metric.add(l.sum(), num_tokens)
# if (epoch + 1) % 10 == 0:
# animator.add(epoch + 1, (metric[0] / metric[1],))
print(f'loss {metric[0] / metric[1]:.3f}, {metric[1] / timer.stop():.1f} '
f'tokens/sec on {str(device)}')
def train(resume_training=True):
EMBEDDING_SIZE = 32
num_hiddens, num_layers, dropout, batch_size, num_steps = EMBEDDING_SIZE, 2, 0.1, 64, 10
lr, num_epochs, device = 0.005, 1000, d2lt.try_gpu()
ffn_num_input, ffn_num_hiddens, num_heads = EMBEDDING_SIZE, 64, 4
key_size, query_size, value_size = EMBEDDING_SIZE, EMBEDDING_SIZE, EMBEDDING_SIZE
norm_shape = [EMBEDDING_SIZE]
### Load data
data_iter, src_vocab, tgt_vocab = load_data_nmt(batch_size, num_steps)
encoder = TransformerEncoder(len(src_vocab), key_size, query_size,
value_size, num_hiddens, norm_shape,
ffn_num_input, ffn_num_hiddens, num_heads,
num_layers, dropout)
decoder = TransformerDecoder(len(tgt_vocab), key_size, query_size,
value_size, num_hiddens, norm_shape,
ffn_num_input, ffn_num_hiddens, num_heads,
num_layers, dropout)
### Load model
model = EncoderDecoder(encoder, decoder).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
### Load checkpoint
if resume_training and PATH_MODEL.exists(
) and os.path.getsize(PATH_MODEL) > 0:
model, optimizer, last_epoch = load_checkpoint(model, optimizer)
print("Continue training from last checkpoint...")
else:
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
with open(PATH_MODEL, 'w') as fp:
pass
print(
'No prior checkpoint existed, created new save files for checkpoint.'
)
model.apply(xavier_init_weights)
last_epoch = 0
# model.apply(xavier_init_weights)
# model.to(device)
# optimizer = torch.optim.Adam(model.parameters(), lr=lr)
### Initialize Loss functions
loss = MaskedSoftmaxCELoss()
### Train
model.train()
# animator = d2lt.Animator(xlabel='epoch', ylabel='loss',
# xlim=[10, num_epochs])
for epoch in range(last_epoch, num_epochs):
timer = d2lt.Timer()
metric = d2lt.Accumulator(2) # Sum of training loss, no. of tokens
for batch in data_iter:
X, X_valid_len, Y, Y_valid_len = [x.to(device) for x in batch]
bos = torch.tensor([tgt_vocab['<bos>']] * Y.shape[0],
device=device).reshape(-1, 1)
dec_input = torch.cat([bos, Y[:, :-1]], 1) # Teacher forcing
Y_hat, _ = model(X, dec_input, X_valid_len)
l = loss(Y_hat, Y, Y_valid_len)
l.sum().backward() # Make the loss scalar for `backward`
d2lt.grad_clipping(model, 1)
num_tokens = Y_valid_len.sum()
optimizer.step()
with torch.no_grad():
metric.add(l.sum(), num_tokens)
if (epoch + 1) % 10 == 0:
# animator.add(epoch + 1, (metric[0] / metric[1],))
print(f'epoch {epoch + 1} - ' f'loss {metric[0] / metric[1]:.5f}')
### Save checkpoint
save_checkpoint(epoch, model, optimizer)
print(f'loss {metric[0] / metric[1]:.5f}, {metric[1] / timer.stop():.1f} '
f'tokens/sec on {str(device)}')
batch_size = 256
def get_dataloader_workers(): #@save
"""Use 4 processes to read the data."""
return 4
train_iter = data.DataLoader(mnist_train, batch_size, shuffle=True,
num_workers=get_dataloader_workers())
timer = d2l.Timer()
for X, y in train_iter:
continue
f'{timer.stop():.2f} sec'
def load_data_fashion_mnist(batch_size, resize=None): #@save
"""Download the Fashion-MNIST dataset and then load it into memory."""
trans = [transforms.ToTensor()]
if resize:
trans.insert(0, transforms.Resize(resize))
trans = transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(
