import torch
from torch import nn
from d2l import torch as d2l
带有注意力机制的解码器基本接口
class AttentionDecoder(d2l.Decoder):
"""带有注意力机制的解码器基本接口"""
def __init__(self, **kwargs):
super(AttentionDecoder, self).__init__(**kwargs)
@property
def attention_weights(self):
raise NotImplementedError
实现带有Bahdanau注意力的循环神经网络解码器
class Seq2SeqAttentionDecoder(AttentionDecoder):
def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,
dropout=0, **kwargs):
super(Seq2SeqAttentionDecoder, self).__init__(**kwargs)
self.attention = d2l.AdditiveAttention(num_hiddens, num_hiddens,
num_hiddens, dropout)
self.embedding = nn.Embedding(vocab_size, embed_size)
self.rnn = nn.GRU(embed_size + num_hiddens, num_hiddens, num_layers,
dropout=dropout)
self.dense = nn.Linear(num_hiddens, vocab_size)
def init_state(self, enc_outputs, enc_valid_lens, *args):
outputs, hidden_state = enc_outputs
return (outputs.permute(1, 0, 2), hidden_state, enc_valid_lens)
def forward(self, X, state):
enc_outputs, hidden_state, enc_valid_lens = state
X = self.embedding(X).permute(1, 0, 2)
outputs, self._attention_weights = [], []
for x in X:
query = torch.unsqueeze(hidden_state[-1], dim=1)
context = self.attention(query, enc_outputs, enc_outputs,
enc_valid_lens)
x = torch.cat((context, torch.unsqueeze(x, dim=1)), dim=-1)
out, hidden_state = self.rnn(x.permute(1, 0, 2), hidden_state)
outputs.append(out)
self._attention_weights.append(self.attention.attention_weights)
outputs = self.dense(torch.cat(outputs, dim=0))
return outputs.permute(1, 0, 2), [
enc_outputs, hidden_state, enc_valid_lens]
@property
def attention_weights(self):
return self._attention_weights
测试Bahdanau 注意力解码器
encoder = d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16,
num_layers=2)
encoder.eval()
decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8, num_hiddens=16,
num_layers=2)
decoder.eval()
X = torch.zeros((4, 7), dtype=torch.long)
state = decoder.init_state(encoder(X), None)
output, state = decoder(X, state)
output.shape, len(state), state[0].shape, len(state[1]), state[1][0].shape
(torch.Size([4, 7, 10]), 3, torch.Size([4, 7, 16]), 2, torch.Size([4, 16]))
训练
embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.1
batch_size, num_steps = 64, 10
lr, num_epochs, device = 0.005, 250, d2l.try_gpu()
train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)
encoder = d2l.Seq2SeqEncoder(len(src_vocab), embed_size, num_hiddens,
num_layers, dropout)
decoder = Seq2SeqAttentionDecoder(len(tgt_vocab), embed_size, num_hiddens,
num_layers, dropout)
net = d2l.EncoderDecoder(encoder, decoder)
d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)
loss 0.021, 4793.5 tokens/sec on cuda:0
将几个英语句子翻译成法语
engs = ['go .', "i lost .", 'he\'s calm .', 'i\'m home .']
fras = ['va !', 'j\'ai perdu .', 'il est calme .', 'je suis chez moi .']
for eng, fra in zip(engs, fras):
translation, dec_attention_weight_seq = d2l.predict_seq2seq(
net, eng, src_vocab, tgt_vocab, num_steps, device, True)
print(f'{eng} => {translation}, ',
f'bleu {d2l.bleu(translation, fra, k=2):.3f}')
go . => va !, bleu 1.000 i lost . => j'ai perdu ., bleu 1.000 he's calm . => il est mouillé ., bleu 0.658 i'm home . => je suis chez moi ., bleu 1.000
attention_weights = torch.cat(
[step[0][0][0] for step in dec_attention_weight_seq], 0).reshape(
(1, 1, -1, num_steps))
可视化注意力权重
d2l.show_heatmaps(
attention_weights[:, :, :, :len(engs[-1].split()) + 1].cpu(),
xlabel='Key posistions', ylabel='Query posistions')