一、 数据处理
本项目中利用古诗数据集作为训练集，编码器接收古诗的每个字的开头，解码器利用编码器的信息生成所有的诗句。为了诗句之间的连贯性，编码器同时也在诗头之前加上之前诗句的信息。举例：
“白日依山尽，黄河入海流，欲穷千里目，更上一层楼。” 可以生成两个样本：
样本一：编码器输入，“白”；解码器输入，“白日依山尽，黄河入海流”
样本二：编码器输入，“白日依山尽，黄河入海流。欲”；解码器输入，“欲穷千里目，更上一层楼。”
1.paddlenlp升级
!pip install -U paddlenlp
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting paddlenlp
[?25l Downloading https://pypi.tuna.tsinghua.edu.cn/packages/17/9b/4535ccf0e96c302a3066bd2e4d0f44b6b1a73487c6793024475b48466c32/paddlenlp-2.2.3-py3-none-any.whl (1.2MB)
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Installing collected packages: paddlenlp
Found existing installation: paddlenlp 2.1.1
Uninstalling paddlenlp-2.1.1:
Successfully uninstalled paddlenlp-2.1.1
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2.提取诗头
import re
poems_file = open("./data/data70759/poems_zh.txt", encoding="utf8")
# 对读取的每一行诗句，统计每一句的词头
poems_samples = []
poems_prefix = []
poems_heads = []
for line in poems_file.readlines():
line_ = re.sub('。', ' ', line)
line_ = line_.split()
# 生成训练样本
for i, p in enumerate(line_):
poems_heads.append(p[0])
poems_prefix.append('。'.join(line_[:i]))
poems_samples.append(p + '。')
# 输出文件信息
for i in range(20):
print("poems heads:{}, poems_prefix: {}, poems:{}".format(poems_heads[i], poems_prefix[i], poems_samples[i]))
poems heads:欲, poems_prefix: , poems:欲出未出光辣达，千山万山如火发。 poems heads:须, poems_prefix: 欲出未出光辣达，千山万山如火发, poems:须臾走向天上来，逐却残星赶却月。 poems heads:未, poems_prefix: , poems:未离海底千山黑，才到天中万国明。 poems heads:满, poems_prefix: , poems:满目江山四望幽，白云高卷嶂烟收。 poems heads:日, poems_prefix: 满目江山四望幽，白云高卷嶂烟收, poems:日回禽影穿疏木，风递猿声入小楼。 poems heads:远, poems_prefix: 满目江山四望幽，白云高卷嶂烟收。日回禽影穿疏木，风递猿声入小楼, poems:远岫似屏横碧落，断帆如叶截中流。 poems heads:片, poems_prefix: , poems:片片飞来静又闲，楼头江上复山前。 poems heads:飘, poems_prefix: 片片飞来静又闲，楼头江上复山前, poems:飘零尽日不归去，帖破清光万里天。 poems heads:因, poems_prefix: , poems:因登巨石知来处，勃勃元生绿藓痕。 poems heads:静, poems_prefix: 因登巨石知来处，勃勃元生绿藓痕, poems:静即等闲藏草木，动时顷刻徧乾坤。 poems heads:横, poems_prefix: 因登巨石知来处，勃勃元生绿藓痕。静即等闲藏草木，动时顷刻徧乾坤, poems:横天未必朋元恶，捧日还曾瑞至尊。 poems heads:不, poems_prefix: 因登巨石知来处，勃勃元生绿藓痕。静即等闲藏草木，动时顷刻徧乾坤。横天未必朋元恶，捧日还曾瑞至尊, poems:不独朝朝在巫峡，楚王何事谩劳魂。 poems heads:若, poems_prefix: , poems:若教作镇居中国，争得泥金在泰山。 poems heads:才, poems_prefix: , poems:才闻暖律先偷眼，既待和风始展眉。 poems heads:嚼, poems_prefix: , poems:嚼处春冰敲齿冷，咽时雪液沃心寒。 poems heads:蒙, poems_prefix: , poems:蒙君知重惠琼实，薄起金刀钉玉深。 poems heads:深, poems_prefix: , poems:深妆玉瓦平无垅，乱拂芦花细有声。 poems heads:片, poems_prefix: , poems:片逐银蟾落醉觥。 poems heads:巧, poems_prefix: , poems:巧剪银花乱，轻飞玉叶狂。 poems heads:寒, poems_prefix: , poems:寒艳芳姿色尽明。
3.生成词表
# 用PaddleNLP生成词表文件，由于诗文的句式较短，我们以单个字作为词单元生成词表 from paddlenlp.data import Vocab vocab = Vocab.build_vocab(poems_samples, unk_token="", pad_token=" ", bos_token="<", eos_token=">") vocab_size = len(vocab) print("vocab size", vocab_size) print("word to idx:", vocab.token_to_idx)
4.定义dataset
# 定义数据读取器
from paddle.io import Dataset, BatchSampler, DataLoader
import numpy as np
class PoemDataset(Dataset):
def __init__(self, poems_data, poems_heads, poems_prefix, vocab, encoder_max_len=128, decoder_max_len=32):
super(PoemDataset, self).__init__()
self.poems_data = poems_data
self.poems_heads = poems_heads
self.poems_prefix = poems_prefix
self.vocab = vocab
self.tokenizer = lambda x: [vocab.token_to_idx[x_] for x_ in x]
self.encoder_max_len = encoder_max_len
self.decoder_max_len = decoder_max_len
def __getitem__(self, idx):
eos_id = vocab.token_to_idx[vocab.eos_token]
bos_id = vocab.token_to_idx[vocab.bos_token]
pad_id = vocab.token_to_idx[vocab.pad_token]
# 确保encoder和decoder的输出都小于最大长度
poet = self.poems_data[idx][:self.decoder_max_len - 2] # -2 包含bos_id和eos_id
prefix = self.poems_prefix[idx][- (self.encoder_max_len - 3):] # -3 包含bos_id, eos_id, 和head的编码
# 对输入输出编码
sample = [bos_id] + self.tokenizer(poet) + [eos_id]
prefix = self.tokenizer(prefix) if prefix else []
heads = prefix + [bos_id] + self.tokenizer(self.poems_heads[idx]) + [eos_id]
sample_len = len(sample)
heads_len = len(heads)
sample = sample + [pad_id] * (self.decoder_max_len - sample_len)
heads = heads + [pad_id] * (self.encoder_max_len - heads_len)
mask = [1] * (sample_len - 1) + [0] * (self.decoder_max_len - sample_len) # -1 to make equal to out[2]
out = [np.array(d, "int64") for d in [heads, heads_len, sample, sample, mask]]
out[2] = out[2][:-1]
out[3] = out[3][1:, np.newaxis]
return out
def shape(self):
return [([None, self.encoder_max_len], 'int64', 'src'),
([None, 1], 'int64', 'src_length'),
([None, self.decoder_max_len - 1],'int64', 'trg')], \
[([None, self.decoder_max_len - 1, 1], 'int64', 'label'),
([None, self.decoder_max_len - 1], 'int64', 'trg_mask')]
def __len__(self):
return len(self.poems_data)
dataset = PoemDataset(poems_samples, poems_heads, poems_prefix, vocab)
batch_sampler = BatchSampler(dataset, batch_size=2048)
data_loader = DataLoader(dataset, batch_sampler=batch_sampler)
二、定义模型并训练
1.模型定义
from Seq2Seq.models import Seq2SeqModel
from paddlenlp.metrics import Perplexity
from Seq2Seq.loss import CrossEntropyCriterion
import paddle
from paddle.static import InputSpec
# 参数
lr = 1e-6
max_epoch = 20
models_save_path = "./checkpoints"
encoder_attrs = {"vocab_size": vocab_size, "embed_dim": 200, "hidden_size": 128, "num_layers": 4, "dropout": .2,
"direction": "bidirectional", "mode": "GRU"}
decoder_attrs = {"vocab_size": vocab_size, "embed_dim": 200, "hidden_size": 128, "num_layers": 4, "direction": "forward",
"dropout": .2, "mode": "GRU", "use_attention": True}
# inputs shape and label shape
inputs_shape, labels_shape = dataset.shape()
inputs_list = [InputSpec(input_shape[0], input_shape[1], input_shape[2]) for input_shape in inputs_shape]
labels_list = [InputSpec(label_shape[0], label_shape[1], label_shape[2]) for label_shape in labels_shape]
net = Seq2SeqModel(encoder_attrs, decoder_attrs)
model = paddle.Model(net, inputs_list, labels_list)
model.load("./final_models/model")
opt = paddle.optimizer.Adam(learning_rate=lr, parameters=model.parameters())
model.prepare(opt, CrossEntropyCriterion(), Perplexity())
W0122 21:03:30.616776 166 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1 W0122 21:03:30.620450 166 device_context.cc:465] device: 0, cuDNN Version: 7.6.
2.模型训练
# 训练，训练时间较长，已提供了训练好的模型（./final_models/model） model.fit(train_data=data_loader, epochs=max_epoch, eval_freq=1, save_freq=5, save_dir=models_save_path, shuffle=True)
3.模型保存
# 保存
model.save("./final_models/model")
三、生成藏头诗
import warnings
def post_process_seq(seq, bos_idx, eos_idx, output_bos=False, output_eos=False):
"""
Post-process the decoded sequence.
"""
eos_pos = len(seq) - 1
for i, idx in enumerate(seq):
if idx == eos_idx:
eos_pos = i
break
seq = [idx for idx in seq[:eos_pos + 1]
if (output_bos or idx != bos_idx) and (output_eos or idx != eos_idx)]
return seq
# 定义用于生成祝福语的类
from paddlenlp.data.tokenizer import JiebaTokenizer
class GenPoems():
# content (str): the str to generate poems, like "恭喜发财"
# vocab: the instance of paddlenlp.data.vocab.Vocab
# model: the Inference Model
def __init__(self, vocab, model):
self.bos_id = vocab.token_to_idx[vocab.bos_token]
self.eos_id = vocab.token_to_idx[vocab.eos_token]
self.pad_id = vocab.token_to_idx[vocab.pad_token]
self.tokenizer = lambda x: [vocab.token_to_idx[x_] for x_ in x]
self.model = model
self.vocab = vocab
def gen(self, content, max_len=128):
# max_len is the encoder_max_len in Seq2Seq Model.
out = []
vocab_list = list(vocab.token_to_idx.keys())
for w in content:
if w in vocab_list:
content = re.sub("([。，])", '', content)
heads = out[- (max_len - 3):] + [self.bos_id] + self.tokenizer(w) + [self.eos_id]
len_heads = len(heads)
heads = heads + [self.pad_id] * (max_len - len_heads)
x = paddle.to_tensor([heads], dtype="int64")
len_x = paddle.to_tensor([len_heads], dtype='int64')
pred = self.model.predict_batch(inputs = [x, len_x])[0]
out += self._get_results(pred)[0]
else:
warnings.warn("{} is not in vocab list, so it is skipped.".format(w))
pass
out = ''.join([self.vocab.idx_to_token[id] for id in out])
return out
def _get_results(self, pred):
pred = pred[:, :, np.newaxis] if len(pred.shape) == 2 else pred
pred = np.transpose(pred, [0, 2, 1])
outs = []
for beam in pred[0]:
id_list = post_process_seq(beam, self.bos_id, self.eos_id)
outs.append(id_list)
return outs
# 载入预测模型
from Seq2Seq.models import Seq2SeqInferModel
import paddle
encoder_attrs = {"vocab_size": vocab_size, "embed_dim": 200, "hidden_size": 128, "num_layers": 4, "dropout": .2,
"direction": "bidirectional", "mode": "GRU"}
decoder_attrs = {"vocab_size": vocab_size, "embed_dim": 200, "hidden_size": 128, "num_layers": 4, "direction": "forward",
"dropout": .2, "mode": "GRU", "use_attention": True}
infer_model = paddle.Model(Seq2SeqInferModel(encoder_attrs,
decoder_attrs,
bos_id=vocab.token_to_idx[vocab.bos_token],
eos_id=vocab.token_to_idx[vocab.eos_token],
beam_size=10,
max_out_len=256))
infer_model.load("./final_models/model")
# 送新年祝福
# 当然，表白也可以
generator = GenPoems(vocab, infer_model)
content = "生龙活虎"
poet = generator.gen(content)
for line in poet.strip().split('。'):
try:
print("{}\t{}。".format(line[0], line))
except:
pass
输出结果
生 生涯不可见，何处不相逢。
龙 龙虎不知何处，人间不见人间。
活 活人不是人间事，不觉人间不可识。
虎 虎豹相逢不可寻，不知何处不相识。
总结
这个项目介绍了如何训练一个生成藏头诗的模型，从结果可以看出，模型已经具有一定的生成诗句的能力。但是，限于训练集规模和训练时间，生成的诗句还有很大的改进空间，未来还将进一步优化这个模型，敬请期待。
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