pytorch -- CNN 文本分类 -- 《 Convolutional Neural Networks for Sentence Classification》
论文 《 Convolutional Neural Networks for Sentence Classification》通过CNN实现了文本分类。
论文地址: 666666
模型图：
模型解释可以看论文，给出code and comment:https://github.com/graykode/nlp-tutorial
1 # -*- coding: utf-8 -*- 2 # @time : 2019/11/9 13:55 3 4 import numpy as np 5 import torch 6 import torch.nn as nn 7 import torch.optim as optim 8 from torch.autograd import Variable 9 import torch.nn.functional as F 10 11 dtype = torch.FloatTensor 12 13 # Text-CNN Parameter 14 embedding_size = 2 # n-gram 15 sequence_length = 3 16 num_classes = 2 # 0 or 1 17 filter_sizes = [2, 2, 2] # n-gram window 18 num_filters = 3 19 20 # 3 words sentences (=sequence_length is 3) 21 sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"] 22 labels = [1, 1, 1, 0, 0, 0] # 1 is good, 0 is not good. 23 24 word_list = " ".join(sentences).split() 25 word_list = list(set(word_list)) 26 word_dict = {w: i for i, w in enumerate(word_list)} 27 vocab_size = len(word_dict) 28 29 inputs = [] 30 for sen in sentences: 31 inputs.append(np.asarray([word_dict[n] for n in sen.split()])) 32 33 targets = [] 34 for out in labels: 35 targets.append(out) # To using Torch Softmax Loss function 36 37 input_batch = Variable(torch.LongTensor(inputs)) 38 target_batch = Variable(torch.LongTensor(targets)) 39 40 41 class TextCNN(nn.Module): 42 def __init__(self): 43 super(TextCNN, self).__init__() 44 45 self.num_filters_total = num_filters * len(filter_sizes) 46 self.W = nn.Parameter(torch.empty(vocab_size, embedding_size).uniform_(-1, 1)).type(dtype) 47 self.Weight = nn.Parameter(torch.empty(self.num_filters_total, num_classes).uniform_(-1, 1)).type(dtype) 48 self.Bias = nn.Parameter(0.1 * torch.ones([num_classes])).type(dtype) 49 50 def forward(self, X): 51 embedded_chars = self.W[X] # [batch_size, sequence_length, sequence_length] 52 embedded_chars = embedded_chars.unsqueeze(1) # add channel(=1) [batch, channel(=1), sequence_length, embedding_size] 53 54 pooled_outputs = [] 55 for filter_size in filter_sizes: 56 # conv : [input_channel(=1), output_channel(=3), (filter_height, filter_width), bias_option] 57 conv = nn.Conv2d(1, num_filters, (filter_size, embedding_size), bias=True)(embedded_chars) 58 h = F.relu(conv) 59 # mp : ((filter_height, filter_width)) 60 mp = nn.MaxPool2d((sequence_length - filter_size + 1, 1)) 61 # pooled : [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3)] 62 pooled = mp(h).permute(0, 3, 2, 1) 63 pooled_outputs.append(pooled) 64 65 h_pool = torch.cat(pooled_outputs, len(filter_sizes)) # [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3) * 3] 66 h_pool_flat = torch.reshape(h_pool, [-1, self.num_filters_total]) # [batch_size(=6), output_height * output_width * (output_channel * 3)] 67 68 model = torch.mm(h_pool_flat, self.Weight) + self.Bias # [batch_size, num_classes] 69 return model 70 71 model = TextCNN() 72 73 criterion = nn.CrossEntropyLoss() 74 optimizer = optim.Adam(model.parameters(), lr=0.001) 75 76 # Training 77 for epoch in range(5000): 78 optimizer.zero_grad() 79 output = model(input_batch) 80 81 # output : [batch_size, num_classes], target_batch : [batch_size] (LongTensor, not one-hot) 82 loss = criterion(output, target_batch) 83 if (epoch + 1) % 1000 == 0: 84 print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss)) 85 86 loss.backward() 87 optimizer.step() 88 89 # Test 90 test_text = 'sorry hate you' 91 tests = [np.asarray([word_dict[n] for n in test_text.split()])] 92 test_batch = Variable(torch.LongTensor(tests)) 93 94 # Predict 95 predict = model(test_batch).data.max(1, keepdim=True)[1] 96 if predict[0][0] == 0: 97 print(test_text,"is Bad Mean...") 98 else: 99 print(test_text,"is Good Mean!!")
