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!!")