大蟒蛇python教程共享Python PaddleNLP实现自动生成虎年藏头诗

目录

• 一、 数据处理
• 1.paddlenlp升级
• 2.提取诗头
• 3.生成词表
• 4.定义dataset
• 二、定义模型并训练
• 1.模型定义
• 2.模型训练
• 3.模型保存
• 三、生成藏头诗
• 总结

!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)       |████████████████████████████████| 1.2mb 11.2mb/s eta 0:00:01  [?25hrequirement already satisfied, skipping upgrade: h5py in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp) (2.9.0)  requirement already satisfied, skipping upgrade: colorlog in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp) (4.1.0)  requirement already satisfied, skipping upgrade: colorama in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp) (0.4.4)  requirement already satisfied, skipping upgrade: seqeval in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp) (1.2.2)  requirement already satisfied, skipping upgrade: jieba in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp) (0.42.1)  requirement already satisfied, skipping upgrade: multiprocess in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlenlp) (0.70.11.1)  requirement already satisfied, skipping upgrade: six in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from h5py->paddlenlp) (1.16.0)  requirement already satisfied, skipping upgrade: numpy>=1.7 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from h5py->paddlenlp) (1.20.3)  requirement already satisfied, skipping upgrade: scikit-learn>=0.21.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from seqeval->paddlenlp) (0.24.2)  requirement already satisfied, skipping upgrade: dill>=0.3.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from multiprocess->paddlenlp) (0.3.3)  requirement already satisfied, skipping upgrade: scipy>=0.19.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn>=0.21.3->seqeval->paddlenlp) (1.6.3)  requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn>=0.21.3->seqeval->paddlenlp) (2.1.0)  requirement already satisfied, skipping upgrade: joblib>=0.11 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from scikit-learn>=0.21.3->seqeval->paddlenlp) (0.14.1)  installing collected packages: paddlenlp    found existing installation: paddlenlp 2.1.1      uninstalling paddlenlp-2.1.1:        successfully uninstalled paddlenlp-2.1.1  successfully installed paddlenlp-2.2.3

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。

# 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="<unk>", pad_token="<pad>", bos_token="<", eos_token=">")  vocab_size = len(vocab)    print("vocab size", vocab_size)  print("word to idx:", vocab.token_to_idx)

# 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)

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.

# 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)

# 保存  model.save("./final_models/model")

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