Problem
d2l中有些包缺失
解决办法:将以下代码加入到d2l.torch中。
#Chapter 9
class RNNModelScratch:
"""A RNN Model implemented from scratch."""
def __init__(self, vocab_size, num_hiddens, device,
get_params, init_state, forward_fn):
"""Defined in :numref:`sec_rnn_scratch`"""
self.vocab_size, self.num_hiddens = vocab_size, num_hiddens
self.params = get_params(vocab_size, num_hiddens, device)
self.init_state, self.forward_fn = init_state, forward_fn
def __call__(self, X, state):
X = F.one_hot(X.T, self.vocab_size).type(torch.float32)
return self.forward_fn(X, state, self.params)
def begin_state(self, batch_size, device):
return self.init_state(batch_size, self.num_hiddens, device)
def predict_ch8(prefix, num_preds, net, vocab, device):
"""Generate new characters following the `prefix`.
Defined in :numref:`sec_rnn_scratch`"""
state = net.begin_state(batch_size=1, device=device)
outputs = [vocab[prefix[0]]]
get_input = lambda: d2l.reshape(d2l.tensor(
[outputs[-1]], device=device), (1, 1))
for y in prefix[1:]: # Warm-up period
_, state = net(get_input(), state)
outputs.append(vocab[y])
for _ in range(num_preds): # Predict `num_preds` steps
y, state = net(get_input(), state)
outputs.append(int(y.argmax(dim=1).reshape(1)))
return ''.join([vocab.idx_to_token[i] for i in outputs])
def grad_clipping(net, theta):
"""Clip the gradient.
Defined in :numref:`sec_rnn_scratch`"""
if isinstance(net, nn.Module):
params = [p for p in net.parameters() if p.requires_grad]
else:
params = net.params
norm = torch.sqrt(sum(torch.sum((p.grad ** 2)) for p in params))
if norm > theta:
for param in params:
param.grad[:] *= theta / norm
def train_epoch_ch8(net, train_iter, loss, updater, device, use_random_iter):
"""Train a net within one epoch (defined in Chapter 8).
Defined in :numref:`sec_rnn_scratch`"""
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 = net.begin_state(batch_size=X.shape[0], 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.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)
# 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_ch8(net, train_iter, vocab, lr, num_epochs, device,
use_random_iter=False):
"""Train a model (defined in Chapter 8).
Defined in :numref:`sec_rnn_scratch`"""
loss = nn.CrossEntropyLoss()
animator = d2l.Animator(xlabel='epoch', ylabel='perplexity',
legend=['train'], xlim=[10, num_epochs])
# Initialize
if isinstance(net, nn.Module):
updater = torch.optim.SGD(net.parameters(), lr)
else:
updater = lambda batch_size: d2l.sgd(net.params, lr, batch_size)
predict = lambda prefix: predict_ch8(prefix, 50, net, vocab, device)
# Train and predict
for epoch in range(num_epochs):
ppl, speed = train_epoch_ch8(
net, train_iter, loss, updater, device, use_random_iter)
if (epoch + 1) % 10 == 0:
print(predict('time traveller'))
animator.add(epoch + 1, [ppl])
print(f'perplexity {ppl:.1f}, {speed:.1f} tokens/sec on {str(device)}')
print(predict('time traveller'))
print(predict('traveller'))
class RNNModel(nn.Module):
"""The RNN model.
Defined in :numref:`sec_rnn-concise`"""
def __init__(self, rnn_layer, vocab_size, **kwargs):
super(RNNModel, self).__init__(**kwargs)
self.rnn = rnn_layer
self.vocab_size = vocab_size
self.num_hiddens = self.rnn.hidden_size
# If the RNN is bidirectional (to be introduced later),
# `num_directions` should be 2, else it should be 1.
if not self.rnn.bidirectional:
self.num_directions = 1
self.linear = nn.Linear(self.num_hiddens, self.vocab_size)
else:
self.num_directions = 2
self.linear = nn.Linear(self.num_hiddens * 2, self.vocab_size)
def forward(self, inputs, state):
X = F.one_hot(inputs.T.long(), self.vocab_size)
X = X.to(torch.float32)
Y, state = self.rnn(X, state)
# The fully connected layer will first change the shape of `Y` to
# (`num_steps` * `batch_size`, `num_hiddens`). Its output shape is
# (`num_steps` * `batch_size`, `vocab_size`).
output = self.linear(Y.reshape((-1, Y.shape[-1])))
return output, state
def begin_state(self, device, batch_size=1):
if not isinstance(self.rnn, nn.LSTM):
# `nn.GRU` takes a tensor as hidden state
return torch.zeros((self.num_directions * self.rnn.num_layers,
batch_size, self.num_hiddens),
device=device)
else:
# `nn.LSTM` takes a tuple of hidden states
return (torch.zeros((
self.num_directions * self.rnn.num_layers,
batch_size, self.num_hiddens), device=device),
torch.zeros((
self.num_directions * self.rnn.num_layers,
batch_size, self.num_hiddens), device=device))
Comments NOTHING