pytorch实现用CNN和LSTM对文本进行分类方式

Python  /  管理员 发布于 5年前   214

model.py：

#!/usr/bin/python# -*- coding: utf-8 -*- import torchfrom torch import nnimport numpy as npfrom torch.autograd import Variableimport torch.nn.functional as F class TextRNN(nn.Module):  """文本分类，RNN模型"""  def __init__(self):    super(TextRNN, self).__init__()    # 三个待输入的数据    self.embedding = nn.Embedding(5000, 64) # 进行词嵌入    # self.rnn = nn.LSTM(input_size=64, hidden_size=128, num_layers=2, bidirectional=True)    self.rnn = nn.GRU(input_size=64, hidden_size=128, num_layers=2, bidirectional=True)    self.f1 = nn.Sequential(nn.Linear(256,128),    nn.Dropout(0.8),    nn.ReLU())    self.f2 = nn.Sequential(nn.Linear(128,10),    nn.Softmax())   def forward(self, x):    x = self.embedding(x)    x,_ = self.rnn(x)    x = F.dropout(x,p=0.8)    x = self.f1(x[:,-1,:])    return self.f2(x) class TextCNN(nn.Module):  def __init__(self):    super(TextCNN, self).__init__()    self.embedding = nn.Embedding(5000,64)    self.conv = nn.Conv1d(64,256,5)    self.f1 = nn.Sequential(nn.Linear(256*596, 128),    nn.ReLU())    self.f2 = nn.Sequential(nn.Linear(128, 10),    nn.Softmax())  def forward(self, x):    x = self.embedding(x)    x = x.detach().numpy()    x = np.transpose(x,[0,2,1])    x = torch.Tensor(x)    x = Variable(x)    x = self.conv(x)    x = x.view(-1,256*596)    x = self.f1(x)    return self.f2(x)

train.py：

# coding: utf-8 from __future__ import print_functionimport torchfrom torch import nnfrom torch import optimfrom torch.autograd import Variableimport os import numpy as np from model import TextRNN,TextCNNfrom cnews_loader import read_vocab, read_category, batch_iter, process_file, build_vocab base_dir = 'cnews'train_dir = os.path.join(base_dir, 'cnews.train.txt')test_dir = os.path.join(base_dir, 'cnews.test.txt')val_dir = os.path.join(base_dir, 'cnews.val.txt')vocab_dir = os.path.join(base_dir, 'cnews.vocab.txt')  def train():  x_train, y_train = process_file(train_dir, word_to_id, cat_to_id,600)#获取训练数据每个字的id和对应标签的oe-hot形式  x_val, y_val = process_file(val_dir, word_to_id, cat_to_id,600)  #使用LSTM或者CNN  model = TextRNN()  # model = TextCNN()  #选择损失函数  Loss = nn.MultiLabelSoftMarginLoss()  # Loss = nn.BCELoss()  # Loss = nn.MSELoss()  optimizer = optim.Adam(model.parameters(),lr=0.001)  best_val_acc = 0  for epoch in range(1000):    batch_train = batch_iter(x_train, y_train,100)    for x_batch, y_batch in batch_train:      x = np.array(x_batch)      y = np.array(y_batch)      x = torch.LongTensor(x)      y = torch.Tensor(y)      # y = torch.LongTensor(y)      x = Variable(x)      y = Variable(y)      out = model(x)      loss = Loss(out,y)      optimizer.zero_grad()      loss.backward()      optimizer.step()      accracy = np.mean((torch.argmax(out,1)==torch.argmax(y,1)).numpy())    #对模型进行验证    if (epoch+1)%20 == 0:      batch_val = batch_iter(x_val, y_val, 100)      for x_batch, y_batch in batch_train:        x = np.array(x_batch)        y = np.array(y_batch)        x = torch.LongTensor(x)        y = torch.Tensor(y)        # y = torch.LongTensor(y)        x = Variable(x)        y = Variable(y)        out = model(x)        loss = Loss(out, y)        optimizer.zero_grad()        loss.backward()        optimizer.step()        accracy = np.mean((torch.argmax(out, 1) == torch.argmax(y, 1)).numpy())        if accracy > best_val_acc:          torch.save(model.state_dict(),'model_params.pkl')          best_val_acc = accracy        print(accracy) if __name__ == '__main__':  #获取文本的类别及其对应id的字典  categories, cat_to_id = read_category()  #获取训练文本中所有出现过的字及其所对应的id  words, word_to_id = read_vocab(vocab_dir)  #获取字数  vocab_size = len(words)  train()

test.py：

# coding: utf-8 from __future__ import print_function import osimport tensorflow.contrib.keras as krimport torchfrom torch import nnfrom cnews_loader import read_category, read_vocabfrom model import TextRNNfrom torch.autograd import Variableimport numpy as nptry:  bool(type(unicode))except NameError:  unicode = str base_dir = 'cnews'vocab_dir = os.path.join(base_dir, 'cnews.vocab.txt') class TextCNN(nn.Module):  def __init__(self):    super(TextCNN, self).__init__()    self.embedding = nn.Embedding(5000,64)    self.conv = nn.Conv1d(64,256,5)    self.f1 = nn.Sequential(nn.Linear(152576, 128),    nn.ReLU())    self.f2 = nn.Sequential(nn.Linear(128, 10),    nn.Softmax())  def forward(self, x):    x = self.embedding(x)    x = x.detach().numpy()    x = np.transpose(x,[0,2,1])    x = torch.Tensor(x)    x = Variable(x)    x = self.conv(x)    x = x.view(-1,152576)    x = self.f1(x)    return self.f2(x) class CnnModel:  def __init__(self):    self.categories, self.cat_to_id = read_category()    self.words, self.word_to_id = read_vocab(vocab_dir)    self.model = TextCNN()    self.model.load_state_dict(torch.load('model_params.pkl'))   def predict(self, message):    # 支持不论在python2还是python3下训练的模型都可以在2或者3的环境下运行    content = unicode(message)    data = [self.word_to_id[x] for x in content if x in self.word_to_id]    data = kr.preprocessing.sequence.pad_sequences([data],600)    data = torch.LongTensor(data)    y_pred_cls = self.model(data)    class_index = torch.argmax(y_pred_cls[0]).item()    return self.categories[class_index] class RnnModel:  def __init__(self):    self.categories, self.cat_to_id = read_category()    self.words, self.word_to_id = read_vocab(vocab_dir)    self.model = TextRNN()    self.model.load_state_dict(torch.load('model_rnn_params.pkl'))   def predict(self, message):    # 支持不论在python2还是python3下训练的模型都可以在2或者3的环境下运行    content = unicode(message)    data = [self.word_to_id[x] for x in content if x in self.word_to_id]    data = kr.preprocessing.sequence.pad_sequences([data], 600)    data = torch.LongTensor(data)    y_pred_cls = self.model(data)    class_index = torch.argmax(y_pred_cls[0]).item()    return self.categories[class_index]  if __name__ == '__main__':  model = CnnModel()  # model = RnnModel()  test_demo = ['湖人助教力助科比恢复手感 他也是阿泰的精神导师新浪体育讯记者戴高乐报道 上赛季，科比的右手食指遭遇重创，他的投篮手感也因此大受影响。不过很快科比就调整了自己的投篮手型，并通过这一方式让自己的投篮命中率回升。而在这科比背后，有一位特别助教对科比帮助很大，他就是查克・珀森。珀森上赛季担任湖人的特别助教，除了帮助科比调整投篮手型之外，他的另一个重要任务就是担任阿泰的精神导师。来到湖人队之后，阿泰收敛起了暴躁的脾气，成为湖人夺冠路上不可或缺的一员，珀森的“心灵按摩”功不可没。经历了上赛季的成功之后，珀森本赛季被“升职”成为湖人队的全职助教，每场比赛，他都会坐在球场边，帮助禅师杰克逊一起指挥湖人球员在场上拼杀。对于珀森的工作，禅师非常欣赏，“查克非常善于分析问题，”菲尔・杰克逊说，“他总是在寻找问题的答案，同时也在找造成这一问题的原因，这是我们都非常乐于看到的。我会在平时把防守中出现的一些问题交给他，然后他会通过组织球员练习找到解决的办法。他在球员时代曾是一名很好的外线投手，不过现在他与内线球员的配合也相当不错。',         '弗老大被裁美国媒体看热闹“特权”在中国像蠢蛋弗老大要走了。虽然他只在首钢男篮效力了13天，而且表现毫无亮点，大大地让球迷和俱乐部失望了，但就像中国人常说的“好聚好散”，队友还是友好地与他告别，俱乐部与他和平分手，球迷还请他留下了在北京的最后一次签名。相比之下，弗老大的同胞美国人却没那么“宽容”。他们嘲讽这位NBA前巨星的英雄迟暮，批评他在CBA的业余表现，还惊讶于中国人的“大方”。今天，北京首钢俱乐部将与弗朗西斯继续商讨解约一事。从昨日的进展来看，双方可以做到“买卖不成人意在”，但回到美国后，恐怕等待弗朗西斯的就没有这么轻松的环境了。进展@北京昨日与队友告别 最后一次为球迷签名弗朗西斯在13天里为首钢队打了4场比赛，3场的得分为0，只有一场得了2分。昨天是他来到北京的第14天，虽然他与首钢还未正式解约，但双方都明白“缘分已尽”。下午，弗朗西斯来到首钢俱乐部与队友们告别。弗朗西斯走到队友身边，依次与他们握手拥抱。“你们都对我很好，安排的条件也很好，我很喜欢这支球队，想融入你们，但我现在真的很不适应。希望你们']  for i in test_demo:    print(i,":",model.predict(i))

cnews_loader.py：

# coding: utf-8 import sysfrom collections import Counter import numpy as npimport tensorflow.contrib.keras as kr if sys.version_info[0] > 2:  is_py3 = Trueelse:  reload(sys)  sys.setdefaultencoding("utf-8")  is_py3 = False  def native_word(word, encoding='utf-8'):  """如果在python2下面使用python3训练的模型，可考虑调用此函数转化一下字符编码"""  if not is_py3:    return word.encode(encoding)  else:    return word  def native_content(content):  if not is_py3:    return content.decode('utf-8')  else:    return content  def open_file(filename, mode='r'):  """  常用文件操作，可在python2和python3间切换.  mode: 'r' or 'w' for read or write  """  if is_py3:    return open(filename, mode, encoding='utf-8', errors='ignore')  else:    return open(filename, mode)  def read_file(filename):  """读取文件数据"""  contents, labels = [], []  with open_file(filename) as f:    for line in f:      try:        label, content = line.strip().split('\t')        if content:          contents.append(list(native_content(content)))          labels.append(native_content(label))      except:        pass  return contents, labels  def build_vocab(train_dir, vocab_dir, vocab_size=5000):  """根据训练集构建词汇表，存储"""  data_train, _ = read_file(train_dir)   all_data = []  for content in data_train:    all_data.extend(content)   counter = Counter(all_data)  count_pairs = counter.most_common(vocab_size - 1)  words, _ = list(zip(*count_pairs))  # 添加一个 <PAD> 来将所有文本pad为同一长度  words = ['<PAD>'] + list(words)  open_file(vocab_dir, mode='w').write('\n'.join(words) + '\n')  def read_vocab(vocab_dir):  """读取词汇表"""  # words = open_file(vocab_dir).read().strip().split('\n')  with open_file(vocab_dir) as fp:    # 如果是py2 则每个值都转化为unicode    words = [native_content(_.strip()) for _ in fp.readlines()]  word_to_id = dict(zip(words, range(len(words))))  return words, word_to_id  def read_category():  """读取分类目录，固定"""  categories = ['体育', '财经', '房产', '家居', '教育', '科技', '时尚', '时政', '游戏', '娱乐']   categories = [native_content(x) for x in categories]   cat_to_id = dict(zip(categories, range(len(categories))))   return categories, cat_to_id  def to_words(content, words):  """将id表示的内容转换为文字"""  return ''.join(words[x] for x in content)  def process_file(filename, word_to_id, cat_to_id, max_length=600):  """将文件转换为id表示"""  contents, labels = read_file(filename)#读取训练数据的每一句话及其所对应的类别  data_id, label_id = [], []  for i in range(len(contents)):    data_id.append([word_to_id[x] for x in contents[i] if x in word_to_id])#将每句话id化    label_id.append(cat_to_id[labels[i]])#每句话对应的类别的id  #  # # 使用keras提供的pad_sequences来将文本pad为固定长度  x_pad = kr.preprocessing.sequence.pad_sequences(data_id, max_length)  y_pad = kr.utils.to_categorical(label_id, num_classes=len(cat_to_id)) # 将标签转换为one-hot表示  #  return x_pad, y_pad  def batch_iter(x, y, batch_size=64):  """生成批次数据"""  data_len = len(x)  num_batch = int((data_len - 1) / batch_size) + 1   indices = np.random.permutation(np.arange(data_len))  x_shuffle = x[indices]  y_shuffle = y[indices]   for i in range(num_batch):    start_id = i * batch_size    end_id = min((i + 1) * batch_size, data_len)    yield x_shuffle[start_id:end_id], y_shuffle[start_id:end_id]

以上这篇pytorch实现用CNN和LSTM对文本进行分类方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。