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

 # -*- coding: utf-8 -*-
 # @time : 2019/11/9  13:55
 
 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
 
 dtype = torch.FloatTensor
 
 # Text-CNN Parameter
 embedding_size = 2 # n-gram
 sequence_length = 3
 num_classes = 2  # 0 or 1
 filter_sizes = [2, 2, 2] # n-gram window
 num_filters = 3
 
 # 3 words sentences (=sequence_length is 3)
 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]  # 1 is good, 0 is not good.
 
 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)
 
 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) # To using Torch Softmax Loss function
 
 input_batch = Variable(torch.LongTensor(inputs))
 target_batch = Variable(torch.LongTensor(targets))
 
 class TextCNN(nn.Module):
     def __init__(self):
         super(TextCNN, self).__init__()
 
         self.num_filters_total = num_filters * len(filter_sizes)
         self.W = nn.Parameter(torch.empty(vocab_size, embedding_size).uniform_(-1, 1)).type(dtype)
         self.Weight = nn.Parameter(torch.empty(self.num_filters_total, num_classes).uniform_(-1, 1)).type(dtype)
         self.Bias = nn.Parameter(0.1 * torch.ones([num_classes])).type(dtype)
 
     def forward(self, X):
         embedded_chars = self.W[X] # [batch_size, sequence_length, sequence_length]
         embedded_chars = embedded_chars.unsqueeze(1) # add channel(=1) [batch, channel(=1), sequence_length, embedding_size]
 
         pooled_outputs = []
         for filter_size in filter_sizes:
             # conv : [input_channel(=1), output_channel(=3), (filter_height, filter_width), bias_option]
             conv = nn.Conv2d(1, num_filters, (filter_size, embedding_size), bias=True)(embedded_chars)
             h = F.relu(conv)
             # mp : ((filter_height, filter_width))
             mp = nn.MaxPool2d((sequence_length - filter_size + 1, 1))
             # pooled : [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3)]
             pooled = mp(h).permute(0, 3, 2, 1)
             pooled_outputs.append(pooled)
 
         h_pool = torch.cat(pooled_outputs, len(filter_sizes)) # [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3) * 3]
         h_pool_flat = torch.reshape(h_pool, [-1, self.num_filters_total]) # [batch_size(=6), output_height * output_width * (output_channel * 3)]
 
         model = torch.mm(h_pool_flat, self.Weight) + self.Bias # [batch_size, num_classes]
         return model
 
 model = TextCNN()
 
 criterion = nn.CrossEntropyLoss()
 optimizer = optim.Adam(model.parameters(), lr=0.001)
 
 # Training
 for epoch in range(5000):
     optimizer.zero_grad()
     output = model(input_batch)
 
     # output : [batch_size, num_classes], target_batch : [batch_size] (LongTensor, not one-hot)
     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).data.max(1, keepdim=True)[1]
 if predict[0][0] == 0:
     print(test_text,"is Bad Mean...")
 else:
     print(test_text,"is Good Mean!!")