import torch
from torch import nn
from d2l import torch as d2l
class StackedRNNScratch(d2l.Module):
def __init__(self, num_inputs, num_hiddens, num_layers, sigma=0.01):
super().__init__()
self.save_hyperparameters()
self.rnns = nn.Sequential(*[d2l.RNNScratch(
num_inputs if i==0 else num_hiddens, num_hiddens, sigma)
for i in range(num_layers)])
@d2l.add_to_class(StackedRNNScratch)
def forward(self, inputs, Hs=None):
outputs = inputs
if Hs is None: Hs = [None] * self.num_layers
for i in range(self.num_layers):
outputs, Hs[i] = self.rnns[i](outputs, Hs[i])
outputs = torch.stack(outputs, 0)
return outputs, Hs
data = d2l.TimeMachine(batch_size=1024, num_steps=32)
rnn_block = StackedRNNScratch(num_inputs=len(data.vocab),
num_hiddens=32, num_layers=2)
model = d2l.RNNLMScratch(rnn_block, vocab_size=len(data.vocab), lr=2)
trainer = d2l.Trainer(max_epochs=100, gradient_clip_val=1, num_gpus=1)
trainer.fit(model, data)
class GRU(d2l.RNN):
"""The multilayer GRU model."""
def __init__(self, num_inputs, num_hiddens, num_layers, dropout=0):
d2l.Module.__init__(self)
self.save_hyperparameters()
self.rnn = nn.GRU(num_inputs, num_hiddens, num_layers,
dropout=dropout)
Select a nontrivial number of hidden layers
by specifying the value of num_layers
gru = GRU(num_inputs=len(data.vocab), num_hiddens=32, num_layers=2)
model = d2l.RNNLM(gru, vocab_size=len(data.vocab), lr=2)
trainer.fit(model, data)
model.predict('it has', 20, data.vocab, d2l.try_gpu())
'it has for and the time th'