基于 lstm的英文翻译系统
基于 lstm的英文翻译系统
代码原理是根据论文： Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
链接： https://arxiv.org/pdf/1406.1078.pdf?source=post_page---------------------------
import torch
if torch.cuda.is_available():
# Tell PyTorch to use the GPU.
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
There are 1 GPU(s) available.
We will use the GPU: GeForce GTX 1070
导入包
import os
import sys
import math
import nltk
import random
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from collections import Counter
导入数据
def load_data(in_file):
cn = []
en = []
num_examples = 0
with open(in_file, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip().split("\t")
en.append(["BOS"] + nltk.word_tokenize(line[0].lower()) + ["EOS"])
# split chinese sentence into characters
cn.append(["BOS"] + [c for c in line[1]] + ["EOS"])
return en, cn
分别建立英语和中文词典
train_file = "./data/train.txt"
dev_file = "./data/dev.txt"
train_en, train_cn = load_data(train_file)
dev_en, dev_cn = load_data(dev_file)
UNK_IDX = 0
PAD_IDX = 1
def build_dict(sentences, max_words=50000):
word_count = Counter()
for sentence in sentences:
for s in sentence:
word_count[s] += 1
ls = word_count.most_common(max_words)
total_words = len(ls) + 2
word_dict = {w[0]: index+2 for index, w in enumerate(ls)}
word_dict["UNK"] = UNK_IDX
word_dict["PAD"] = PAD_IDX
return word_dict, total_words
en_dict, en_total_words = build_dict(train_en)
cn_dict, cn_total_words = build_dict(train_cn)
inv_en_dict = {v: k for k, v in en_dict.items()}
inv_cn_dict = {v: k for k, v in cn_dict.items()}
编码句子
def encode(en_sentences, cn_sentences, en_dict, cn_dict, sort_by_len=True):
'''
Encode the sequences.
'''
length = len(en_sentences)
out_en_sentences = [[en_dict.get(w, 0) for w in sent] for sent in en_sentences]
out_cn_sentences = [[cn_dict.get(w, 0) for w in sent] for sent in cn_sentences]
# sort sentences by english lengths
def len_argsort(seq):
return sorted(range(len(seq)), key=lambda x: len(seq[x]))
# 把中文和英文按照同样的顺序排序
if sort_by_len:
sorted_index = len_argsort(out_en_sentences)
out_en_sentences = [out_en_sentences[i] for i in sorted_index]
out_cn_sentences = [out_cn_sentences[i] for i in sorted_index]
return out_en_sentences, out_cn_sentences
train_en, train_cn = encode(train_en, train_cn, en_dict, cn_dict)
dev_en, dev_cn = encode(dev_en, dev_cn, en_dict, cn_dict)
简单查看一下平行语料
k = 10000
print(" ".join([inv_cn_dict[i] for i in train_cn[k]]))
print(" ".join([inv_en_dict[i] for i in train_en[k]]))
BOS 打 牌 很 有 意 思 。 EOS
BOS playing cards is very interesting . EOS
为模型训练处理数据
def get_minibatches(n, minibatch_size, shuffle=True):
idx_list = np.arange(0, n, minibatch_size) # [0, 1, ..., n-1]
if shuffle:
np.random.shuffle(idx_list)
minibatches = []
for idx in idx_list:
minibatches.append(np.arange(idx, min(idx + minibatch_size, n)))
return minibatches
def prepare_data(seqs):
lengths = [len(seq) for seq in seqs]
n_samples = len(seqs)
max_len = np.max(lengths)
x = np.zeros((n_samples, max_len)).astype('int32')
x_lengths = np.array(lengths).astype("int32")
for idx, seq in enumerate(seqs):
x[idx, :lengths[idx]] = seq
return x, x_lengths #x_mask
def gen_examples(en_sentences, cn_sentences, batch_size):
minibatches = get_minibatches(len(en_sentences), batch_size)
all_ex = []
for minibatch in minibatches:
mb_en_sentences = [en_sentences[t] for t in minibatch]
mb_cn_sentences = [cn_sentences[t] for t in minibatch]
mb_x, mb_x_len = prepare_data(mb_en_sentences)
mb_y, mb_y_len = prepare_data(mb_cn_sentences)
all_ex.append((mb_x, mb_x_len, mb_y, mb_y_len))
return all_ex
batch_size = 64
train_data = gen_examples(train_en, train_cn, batch_size)
random.shuffle(train_data)
dev_data = gen_examples(dev_en, dev_cn, batch_size)
sequence to sequence 中的encoder 部分
class Encoder(nn.Module):
def __init__(self, vocab_size, embed_size, enc_hidden_size, dec_hidden_size, dropout=0.2):
super(Encoder, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.rnn = nn.GRU(embed_size, enc_hidden_size, batch_first=True, bidirectional=True)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(enc_hidden_size * 2, dec_hidden_size)
def forward(self, x, lengths):
sorted_len, sorted_idx = lengths.sort(0, descending=True)
x_sorted = x[sorted_idx.long()]
embedded = self.dropout(self.embed(x_sorted))
packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, sorted_len.long().cpu().data.numpy(),
batch_first=True)
packed_out, hid = self.rnn(packed_embedded)
out, _ = nn.utils.rnn.pad_packed_sequence(packed_out, batch_first=True)
_, original_idx = sorted_idx.sort(0, descending=False)
out = out[original_idx.long()].contiguous()
hid = hid[:, original_idx.long()].contiguous()
hid = torch.cat([hid[-2], hid[-1]], dim=1)
hid = torch.tanh(self.fc(hid)).unsqueeze(0)
return out, hid
为decoder 加上attention 机制
class Attention(nn.Module):
def __init__(self, enc_hidden_size, dec_hidden_size):
super(Attention, self).__init__()
self.enc_hidden_size = enc_hidden_size
self.dec_hidden_size = dec_hidden_size
self.linear_in = nn.Linear(enc_hidden_size * 2, dec_hidden_size, bias=False)
self.linear_out = nn.Linear(enc_hidden_size * 2 + dec_hidden_size, dec_hidden_size)
def forward(self, output, context, mask):
# output: batch_size, output_len, dec_hidden_size
# context: batch_size, context_len, 2*enc_hidden_size
batch_size = output.size(0)
output_len = output.size(1)
input_len = context.size(1)
context_in = self.linear_in(context.view(batch_size * input_len, -1)).view(
batch_size, input_len, -1) # batch_size, context_len, dec_hidden_size
# context_in.transpose(1,2): batch_size, dec_hidden_size, context_len
# output: batch_size, output_len, dec_hidden_size
attn = torch.bmm(output, context_in.transpose(1, 2))
# batch_size, output_len, context_len
attn.data.masked_fill(mask.bool(), -1e6)
# attn.data.masked_fill(mask, -1e6)
attn = F.softmax(attn, dim=2)
# batch_size, output_len, context_len
context = torch.bmm(attn, context)
# batch_size, output_len, enc_hidden_size
output = torch.cat((context, output), dim=2) # batch_size, output_len, hidden_size*2
output = output.view(batch_size * output_len, -1)
output = torch.tanh(self.linear_out(output))
output = output.view(batch_size, output_len, -1)
return output, attn
sequence to sequence 中的Decoder 部分
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class Decoder(nn.Module):
def __init__(self, vocab_size, embed_size, enc_hidden_size, dec_hidden_size, dropout=0.2):
super(Decoder, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.attention = Attention(enc_hidden_size, dec_hidden_size)
self.rnn = nn.GRU(embed_size, hidden_size, batch_first=True)
self.out = nn.Linear(dec_hidden_size, vocab_size)
self.dropout = nn.Dropout(dropout)
def create_mask(self, x_len, y_len):
# a mask of shape x_len * y_len
device = x_len.device
max_x_len = x_len.max()
max_y_len = y_len.max()
x_mask = torch.arange(max_x_len, device=x_len.device)[None, :] < x_len[:, None]
y_mask = torch.arange(max_y_len, device=x_len.device)[None, :] < y_len[:, None]
# mask = (1 - x_mask[:, :, None] * y_mask[:, None, :]).byte()
mask = (~x_mask[:, :, None] * y_mask[:, None, :]).byte()
return mask
def forward(self, ctx, ctx_lengths, y, y_lengths, hid):
sorted_len, sorted_idx = y_lengths.sort(0, descending=True)
y_sorted = y[sorted_idx.long()]
hid = hid[:, sorted_idx.long()]
y_sorted = self.dropout(self.embed(y_sorted)) # batch_size, output_length, embed_size
packed_seq = nn.utils.rnn.pack_padded_sequence(y_sorted, sorted_len.long().cpu().data.numpy(), batch_first=True)
out, hid = self.rnn(packed_seq, hid)
unpacked, _ = nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
_, original_idx = sorted_idx.sort(0, descending=False)
output_seq = unpacked[original_idx.long()].contiguous()
hid = hid[:, original_idx.long()].contiguous()
mask = self.create_mask(y_lengths, ctx_lengths)
output, attn = self.attention(output_seq, ctx, mask)
output = F.log_softmax(self.out(output), -1)
return output, hid, attn
定义语言模型
class Seq2Seq(nn.Module):
def __init__(self, encoder, decoder):
super(Seq2Seq, self).__init__()
self.encoder = encoder
self.decoder = decoder
def forward(self, x, x_lengths, y, y_lengths):
encoder_out, hid = self.encoder(x, x_lengths)
output, hid, attn = self.decoder(ctx=encoder_out,
ctx_lengths=x_lengths,
y=y,
y_lengths=y_lengths,
hid=hid)
return output, attn
def translate(self, x, x_lengths, y, max_length=100):
encoder_out, hid = self.encoder(x, x_lengths)
preds = []
batch_size = x.shape[0]
attns = []
for i in range(max_length):
output, hid, attn = self.decoder(ctx=encoder_out,
ctx_lengths=x_lengths,
y=y,
y_lengths=torch.ones(batch_size).long().to(y.device),
hid=hid)
y = output.max(2)[1].view(batch_size, 1)
preds.append(y)
attns.append(attn)
return torch.cat(preds, 1), torch.cat(attns, 1)
设计损失函数（masked cross entropy loss)
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
# input: (batch_size * seq_len) * vocab_size
input = input.contiguous().view(-1, input.size(2))
# target: batch_size * 1
target = target.contiguous().view(-1, 1)
mask = mask.contiguous().view(-1, 1)
output = -input.gather(1, target) * mask
output = torch.sum(output) / torch.sum(mask)
return output
初始化模型参数
dropout = 0.2
embed_size = hidden_size = 100
encoder = Encoder(vocab_size=en_total_words,
embed_size=embed_size,
enc_hidden_size=hidden_size,
dec_hidden_size=hidden_size,
dropout=dropout)
decoder = Decoder(vocab_size=cn_total_words,
embed_size=embed_size,
enc_hidden_size=hidden_size,
dec_hidden_size=hidden_size,
dropout=dropout)
model = Seq2Seq(encoder, decoder)
model = model.to(device)
loss_fn = LanguageModelCriterion().to(device)
optimizer = torch.optim.Adam(model.parameters())
定义评估函数
def evaluate(model, data):
model.eval()
total_num_words = total_loss = 0.
with torch.no_grad():
for it, (mb_x, mb_x_len, mb_y, mb_y_len) in enumerate(data):
mb_x = torch.from_numpy(mb_x).to(device).long()
mb_x_len = torch.from_numpy(mb_x_len).to(device).long()
mb_input = torch.from_numpy(mb_y[:, :-1]).to(device).long()
mb_output = torch.from_numpy(mb_y[:, 1:]).to(device).long()
mb_y_len = torch.from_numpy(mb_y_len-1).to(device).long()
mb_y_len[mb_y_len<=0] = 1
mb_pred, attn = model(mb_x, mb_x_len, mb_input, mb_y_len)
mb_out_mask = torch.arange(mb_y_len.max().item(), device=device)[None, :] < mb_y_len[:, None]
mb_out_mask = mb_out_mask.float()
loss = loss_fn(mb_pred, mb_output, mb_out_mask)
num_words = torch.sum(mb_y_len).item()
total_loss += loss.item() * num_words
total_num_words += num_words
print("Evaluation loss", total_loss/total_num_words)
定义训练函数
def train(model, data, num_epochs=20):
for epoch in range(num_epochs):
model.train()
total_num_words = total_loss = 0.
for it, (mb_x, mb_x_len, mb_y, mb_y_len) in enumerate(data):
mb_x = torch.from_numpy(mb_x).to(device).long()
mb_x_len = torch.from_numpy(mb_x_len).to(device).long()
mb_input = torch.from_numpy(mb_y[:, :-1]).to(device).long()
mb_output = torch.from_numpy(mb_y[:, 1:]).to(device).long()
mb_y_len = torch.from_numpy(mb_y_len - 1).to(device).long()
mb_y_len[mb_y_len <= 0] = 1
mb_pred, attn = model(mb_x, mb_x_len, mb_input, mb_y_len)
mb_out_mask = torch.arange(mb_y_len.max().item(), device=device)[None, :] < mb_y_len[:, None]
mb_out_mask = mb_out_mask.float()
loss = loss_fn(mb_pred, mb_output, mb_out_mask)
num_words = torch.sum(mb_y_len).item()
total_loss += loss.item() * num_words
total_num_words += num_words
# 更新模型
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.)
optimizer.step()
if it % 500 == 0:
print("Epoch", epoch, "iteration", it, "loss", loss.item())
print("Epoch", epoch, "Training loss", total_loss / total_num_words)
if epoch % 50 == 0:
evaluate(model, dev_data)
训练模型
train(model, train_data, num_epochs=200)
Epoch 0 iteration 0 loss 7.928357124328613
Epoch 0 iteration 500 loss 3.90830397605896
Epoch 0 Training loss 4.815643868381794
Evaluation loss 4.091342107835811
Epoch 1 iteration 0 loss 3.849057912826538
Epoch 1 iteration 500 loss 3.227210283279419
Epoch 1 Training loss 3.766976896258373
Epoch 2 iteration 0 loss 3.2430152893066406
Epoch 2 iteration 500 loss 2.896683931350708
Epoch 2 Training loss 3.2947107675981218
Epoch 3 iteration 0 loss 2.8801681995391846
Epoch 3 iteration 500 loss 2.655324697494507
Epoch 3 Training loss 2.99818192814612
Epoch 4 iteration 0 loss 2.606170892715454
Epoch 4 iteration 500 loss 2.485543727874756
Epoch 4 Training loss 2.7858131342012973
Epoch 5 iteration 0 loss 2.4095005989074707
Epoch 5 iteration 500 loss 2.3372409343719482
Epoch 5 Training loss 2.61820455699905
Epoch 6 iteration 0 loss 2.2848551273345947
Epoch 6 iteration 500 loss 2.2205848693847656
Epoch 6 Training loss 2.4839356306734097
Epoch 7 iteration 0 loss 2.169241189956665
Epoch 7 iteration 500 loss 2.1018216609954834
Epoch 7 Training loss 2.370443155886292
Epoch 8 iteration 0 loss 2.06174635887146
Epoch 8 iteration 500 loss 2.020695447921753
Epoch 8 Training loss 2.2732634094208275
Epoch 9 iteration 0 loss 1.984137773513794
Epoch 9 iteration 500 loss 1.939084529876709
Epoch 9 Training loss 2.1883279392436283
Epoch 10 iteration 0 loss 1.9452708959579468
Epoch 10 iteration 500 loss 1.8818047046661377
Epoch 10 Training loss 2.112133346478979
Epoch 11 iteration 0 loss 1.8716343641281128
Epoch 11 iteration 500 loss 1.8141098022460938
Epoch 11 Training loss 2.0430460208444448
Epoch 12 iteration 0 loss 1.7709314823150635
Epoch 12 iteration 500 loss 1.7577903270721436
Epoch 12 Training loss 1.9812295165150986
Epoch 13 iteration 0 loss 1.7548656463623047
Epoch 13 iteration 500 loss 1.732089638710022
Epoch 13 Training loss 1.927129452975913
Epoch 14 iteration 0 loss 1.711237907409668
Epoch 14 iteration 500 loss 1.6911147832870483
Epoch 14 Training loss 1.8765310167380684
Epoch 15 iteration 0 loss 1.6728557348251343
Epoch 15 iteration 500 loss 1.6582821607589722
Epoch 15 Training loss 1.8305548231946718
Epoch 16 iteration 0 loss 1.6360520124435425
Epoch 16 iteration 500 loss 1.5719242095947266
Epoch 16 Training loss 1.7837277017431035
Epoch 17 iteration 0 loss 1.6439002752304077
Epoch 17 iteration 500 loss 1.524785041809082
Epoch 17 Training loss 1.7447902788038288
Epoch 18 iteration 0 loss 1.58423912525177
Epoch 18 iteration 500 loss 1.5078282356262207
Epoch 18 Training loss 1.7080127060983032
Epoch 19 iteration 0 loss 1.5390608310699463
Epoch 19 iteration 500 loss 1.5225342512130737
Epoch 19 Training loss 1.6723873219620053
Epoch 20 iteration 0 loss 1.494099497795105
Epoch 20 iteration 500 loss 1.5124914646148682
Epoch 20 Training loss 1.6367066513444974
Epoch 21 iteration 0 loss 1.4822521209716797
Epoch 21 iteration 500 loss 1.467223048210144
Epoch 21 Training loss 1.606666042107959
Epoch 22 iteration 0 loss 1.4411041736602783
Epoch 22 iteration 500 loss 1.4498487710952759
Epoch 22 Training loss 1.5750408884265574
Epoch 23 iteration 0 loss 1.3950568437576294
Epoch 23 iteration 500 loss 1.3538126945495605
Epoch 23 Training loss 1.551325583339711
Epoch 24 iteration 0 loss 1.3918793201446533
Epoch 24 iteration 500 loss 1.3291021585464478
Epoch 24 Training loss 1.5217847593300489
Epoch 25 iteration 0 loss 1.365149736404419
Epoch 25 iteration 500 loss 1.3761420249938965
Epoch 25 Training loss 1.4973933488412807
Epoch 26 iteration 0 loss 1.3466569185256958
Epoch 26 iteration 500 loss 1.371177077293396
Epoch 26 Training loss 1.4720650266576034
Epoch 27 iteration 0 loss 1.3281514644622803
Epoch 27 iteration 500 loss 1.331527590751648
Epoch 27 Training loss 1.4490924394659275
Epoch 28 iteration 0 loss 1.3383699655532837
Epoch 28 iteration 500 loss 1.306111454963684
Epoch 28 Training loss 1.4280537929261388
Epoch 29 iteration 0 loss 1.3682355880737305
Epoch 29 iteration 500 loss 1.2585397958755493
Epoch 29 Training loss 1.4075209773713206
Epoch 30 iteration 0 loss 1.297282338142395
Epoch 30 iteration 500 loss 1.2389955520629883
Epoch 30 Training loss 1.386655172488642
Epoch 31 iteration 0 loss 1.2280360460281372
Epoch 31 iteration 500 loss 1.241192102432251
Epoch 31 Training loss 1.3661337603685542
Epoch 32 iteration 0 loss 1.2098947763442993
Epoch 32 iteration 500 loss 1.1983294486999512
Epoch 32 Training loss 1.3482332145965288
Epoch 33 iteration 0 loss 1.2415146827697754
Epoch 33 iteration 500 loss 1.2350493669509888
Epoch 33 Training loss 1.3326907000024077
Epoch 34 iteration 0 loss 1.2163398265838623
Epoch 34 iteration 500 loss 1.1608344316482544
Epoch 34 Training loss 1.3135639723825614
Epoch 35 iteration 0 loss 1.228286623954773
Epoch 35 iteration 500 loss 1.1782830953598022
Epoch 35 Training loss 1.296640716271889
Epoch 36 iteration 0 loss 1.205100417137146
Epoch 36 iteration 500 loss 1.2059627771377563
Epoch 36 Training loss 1.2816641990295445
Epoch 37 iteration 0 loss 1.1419614553451538
Epoch 37 iteration 500 loss 1.0831267833709717
Epoch 37 Training loss 1.266050579365604
Epoch 38 iteration 0 loss 1.1397496461868286
Epoch 38 iteration 500 loss 1.0981181859970093
Epoch 38 Training loss 1.2505547716558623
Epoch 39 iteration 0 loss 1.1959482431411743
Epoch 39 iteration 500 loss 1.1356191635131836
Epoch 39 Training loss 1.2390485424490791
Epoch 40 iteration 0 loss 1.1450738906860352
Epoch 40 iteration 500 loss 1.0957298278808594
Epoch 40 Training loss 1.2206933174895096
Epoch 41 iteration 0 loss 1.1270146369934082
Epoch 41 iteration 500 loss 1.0484026670455933
Epoch 41 Training loss 1.209546530202252
Epoch 42 iteration 0 loss 1.1364833116531372
Epoch 42 iteration 500 loss 1.0879396200180054
Epoch 42 Training loss 1.1973414120195522
Epoch 43 iteration 0 loss 1.0954558849334717
Epoch 43 iteration 500 loss 1.0777392387390137
Epoch 43 Training loss 1.1830152717945142
Epoch 44 iteration 0 loss 1.1112639904022217
Epoch 44 iteration 500 loss 1.0734856128692627
Epoch 44 Training loss 1.1713028105248906
Epoch 45 iteration 0 loss 1.0966465473175049
Epoch 45 iteration 500 loss 1.0444735288619995
Epoch 45 Training loss 1.1590108651671145
Epoch 46 iteration 0 loss 1.0701093673706055
Epoch 46 iteration 500 loss 1.0505683422088623
Epoch 46 Training loss 1.1480596024264336
Epoch 47 iteration 0 loss 1.0556902885437012
Epoch 47 iteration 500 loss 0.998938262462616
Epoch 47 Training loss 1.136144786781804
Epoch 48 iteration 0 loss 1.1005561351776123
Epoch 48 iteration 500 loss 1.046020746231079
Epoch 48 Training loss 1.1245009635135628
Epoch 49 iteration 0 loss 1.0618535280227661
Epoch 49 iteration 500 loss 1.016508936882019
Epoch 49 Training loss 1.1140278308835816
Epoch 50 iteration 0 loss 1.0361311435699463
Epoch 50 iteration 500 loss 0.9854018092155457
Epoch 50 Training loss 1.103240595894343
Evaluation loss 1.1672870177730559
Epoch 51 iteration 0 loss 0.9864965081214905
Epoch 51 iteration 500 loss 0.9881776571273804
Epoch 51 Training loss 1.0957198824253787
Epoch 52 iteration 0 loss 1.0605281591415405
Epoch 52 iteration 500 loss 0.9425312876701355
Epoch 52 Training loss 1.0835173193434786
Epoch 53 iteration 0 loss 1.0059244632720947
Epoch 53 iteration 500 loss 1.012495756149292
Epoch 53 Training loss 1.0746916196923277
Epoch 54 iteration 0 loss 1.0311338901519775
Epoch 54 iteration 500 loss 0.9643288254737854
Epoch 54 Training loss 1.0639997538991715
Epoch 55 iteration 0 loss 0.9999511241912842
Epoch 55 iteration 500 loss 0.9228934645652771
Epoch 55 Training loss 1.0586248801784999
Epoch 56 iteration 0 loss 0.9765851497650146
Epoch 56 iteration 500 loss 0.9912199974060059
Epoch 56 Training loss 1.047446841975497
Epoch 57 iteration 0 loss 0.9578201770782471
Epoch 57 iteration 500 loss 0.9974180459976196
Epoch 57 Training loss 1.0401080069686683
Epoch 58 iteration 0 loss 0.9840922951698303
Epoch 58 iteration 500 loss 0.9113590121269226
Epoch 58 Training loss 1.0313254258577484
Epoch 59 iteration 0 loss 0.9581423997879028
Epoch 59 iteration 500 loss 0.9251217246055603
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Epoch 197 Training loss 0.5922638683993326
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Epoch 198 iteration 500 loss 0.5765736699104309
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Epoch 199 iteration 500 loss 0.5664358139038086
Epoch 199 Training loss 0.5932606708257029
测试模型结果
def model_result(i):
en_sent = " ".join([inv_en_dict[w] for w in dev_en[i]])
print(en_sent)
cn_sent = " ".join([inv_cn_dict[w] for w in dev_cn[i]])
print("".join(cn_sent))
mb_x = torch.from_numpy(np.array(dev_en[i]).reshape(1, -1)).long().to(device)
mb_x_len = torch.from_numpy(np.array([len(dev_en[i])])).long().to(device)
bos = torch.Tensor([[cn_dict["BOS"]]]).long().to(device)
translation, attn = model.translate(mb_x, mb_x_len, bos)
translation = [inv_cn_dict[i] for i in translation.data.cpu().numpy().reshape(-1)]
trans = []
for word in translation:
if word != "EOS":
trans.append(word)
else:
break
print("".join(trans))
for i in range(20,25):
model_result(i)
print()
BOS anything else ? EOS
BOS 还 有 别 的 吗 ？ EOS
还好别吃吗？
BOS i 'm sleepy . EOS
BOS 我 困 了 。 EOS
我累了。
BOS i ate caviar . EOS
BOS 我 吃 了 鱼 子 酱 。 EOS
我吃了鱼式。
BOS i like sports . EOS
BOS 我 喜 欢 运 动 。 EOS
我喜欢运动。
BOS she may come . EOS
BOS 她 可 以 来 。 EOS
她可以来。