BiLSTM-Attention文本分类 文本分类 概述概述 上一篇中使用BiLSTM-Attention模型进行关系抽取，因为只放出了较为核心的代码，所以看上去比较混乱。这篇以简单的文本分类为demo，基于pytorch，全面解读BiLSTM- Attention。 文本分类实战 文本分类实战 整体构建 整体构建 首先，我们导入需要的包，包括模型，优化器，梯度求导等，将数据类型全部转化成tensor类型 import numpy as np import torch import torch.nn as nn import torch.optim as optim from torch.autograd import Variable import torch.nn.functional as F import matplotlib.pyplot as plt dtype = torch.FloatTensor 接下来我们确定一些基本的参数，并且简单地构造一个数据，实现情感的二分类。数据集中三个句子，一半正，一半负。label中1是好的情感，0是不好的情感。 embedding_dim = 3 n_hidden = 5 num_classes = 2 # 0 or 1 sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"] labels = [1, 1, 1, 0, 0, 0] 接着，我们需要构建词表，把数据集中出现过的词拿出来并给它一个编号： word_list = " ".join(sentences).split() word_list = list(set(word_list)) word_dict = {w: i for i, w in enumerate(word_list)} vocab_size = len(word_dict) 然后我们定义输入输出，输入其实是每个句子中的每个单词对应在词表中的id，将输入输出变成Variable，以便于求导： inputs = [] for sen in sentences: inputs.append(np.asarray([word_dict[n] for n in sen.split()])) targets = [] for out in labels: targets.append(out) input_batch = Variable(torch.LongTensor(inputs)) target_batch = Variable(torch.LongTensor(targets)) 接下来构造模型： class BiLSTM_Attention(nn.Module): def __init__(self): super(BiLSTM_Attention, self).__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.lstm = nn.LSTM(embedding_dim, n_hidden, bidirectional=True) self.out = nn.Linear(n_hidden * 2, num_classes) def attention_net(self, lstm_output, final_state): hidden = final_state.view(-1, n_hidden * 2, 1) attn_weights = torch.bmm(lstm_output, hidden).squeeze(2) soft_attn_weights = F.softmax(attn_weights, 1) context = torch.bmm(lstm_output.transpose(1, 2), soft_attn_weights.unsqueeze(2)).squeeze(2) return context, soft_attn_weights.data.numpy() def forward(self, X): input = self.embedding(X) input = input.permute(1, 0, 2) hidden_state = Variable(torch.zeros(1*2, len(X), n_hidden)) cell_state = Variable(torch.zeros(1*2, len(X), n_hidden)) output, (final_hidden_state, final_cell_state) = self.lstm(input, (hidden_state, cell_state)) output = output.permute(1, 0, 2) attn_output, attention = self.attention_net(output, final_hidden_state) return self.out(attn_output), attention 首先embedding中需要传入词表，以及嵌入的维度。有一个双向LSTM层，还有一个线性层以获取LSTM中的隐层参数。 这里详细说一下attention层的操作，首先hidden 的维度是 [batch_size, n_hidden * num_directions(=2), 1(=n_layer)]，接下来确定attention矩阵，将LSTM输出与hidden相乘，去掉第 三个维度。attn_weights的维度是[batch_size, n_step] ，两个矩阵相乘后的维度，[batch_size, n_hidden * num_directions(=2), n_step] * [batch_size, n_step, 1] = [batch_size, n_hidden * num_directions(=2), 1]，然后去掉了第三个维度的1。这样再经过softmax函数。再将权重函数与LSTM输出相乘得到context。最终context的维度就是 [batch_size, n_hidden * num_directions(=2)] 。 最后在forward方法中操作各个层，进行层的各种操作，获得输出和attention矩阵。 接下来就是将模型实例化，并确定损失函数，优化器： model = BiLSTM_Attention() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) 最后训练并测试： # Training for epoch in range(5000): optimizer.zero_grad() output, attention = model(input_batch) loss = criterion(output, target_batch) if (epoch + 1) % 1000 == 0: print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss)) loss.backward() optimizer.step() # Test test_text = 'sorry hate you' tests = [np.asarray([word_dict[n] for n in test_text.split()])] test_batch = Variable(torch.LongTensor(tests)) # Predict predict, _ = model(test_batch) predict = predict.data.max(1, keepdim=True)[1] if predict[0][0] == 0: print(test_text,"is Bad Mean...") else: print(test_text,"is Good Mean!!") 最终我们画图看下attention中结果： fig = plt.figure(figsize=(6, 3)) # [batch_size, n_step] ax = fig.add_subplot(1, 1, 1) ax.matshow(attention, cmap='viridis') ax.set_xticklabels(['']+['first_word', 'second_word', 'third_word'], fontdict={'fontsize': 14}, rotation=90) ax.set_yticklabels(['']+['batch_1', 'batch_2', 'batch_3', 'batch_4', 'batch_5', 'batch_6'], fontdict={'fontsize': 14}) 

plt.show() 调试调试 读取数据： 转换文本后的输入输出： 接下来跑完整个循环，看到结果，测试集中的这个句子分类为负： Attention矩阵： 作者：小炮哥哥