def train():
embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.0
batch_size, num_steps = 64, 10
lr, num_epochs, device = 0.005, 200, d2l.try_gpu()
src_vocab, tgt_vocab, train_iter = 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)
model = d2l.EncoderDecoder(encoder, decoder)
d2l.train_s2s_ch9(model, train_iter, lr, num_epochs, device)
for blk in self.blks:
X, state = blk(X, state)
return self.dense(X), state
num_hiddens, num_layers, dropout, batch_size, num_steps = 32, 2, 0.1, 64,10
lr, num_epochs, device = 0.005, 200, d2l.try_gpu()
ffn_num_input, ffn_num_hiddens, num_heads = 32, 64, 4
key_size, query_size, value_size = 32, 32, 32
norm_shape = [32]
train_iter, src_vocab, tgt_vocab = d2l.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)
model = d2l.EncoderDecoder(encoder, decoder)
d2l.train_s2s_ch9(model, train_iter,lr,num_epochs,tgt_vocab,device)
#%%
engs = ['go .', "i lost .", 'i\'m home .', 'he\'s calm .']
fras = ['va !', 'j\'ai perdu .', 'je suis chez moi .', 'il est calme .']
# 在预测时，Transformer是一次encoder，多次decoer(直到预测出<eos>)
d2l.translate(engs, fras, model, src_vocab, tgt_vocab, num_steps, device)
# %%
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)}')
embed_size, num_hiddens, num_layers, dropout = 32, 34, 2, 0.1
batch_size, num_steps = 64, 10
lr, num_epochs, device = 0.005, 300, d2l.try_gpu()
train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)
encoder = Seq2SeqEncoder(len(src_vocab), embed_size, num_hiddens, num_layers,
dropout)
decoder = Seq2SeqDecoder(len(tgt_vocab), embed_size, num_hiddens, num_layers,
dropout)
net = d2l.EncoderDecoder(encoder, decoder)
train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)
#@save
def predict_seq2seq(net,
src_sentence,
src_vocab,
tgt_vocab,
num_steps,
device,
save_attention_weights=False):
"""Predict for sequence to sequence."""
# Set `net` to eval mode for inference
net.eval()
src_tokens = src_vocab[src_sentence.lower().split(' ')] + [
