零基础入门NLP-TASK5基于深度学习的文本分类2 零基础入门NLP新闻文本分类

TextCNN
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#模型搭建 self.filter_sizes = [2, 3, 4] # n-gram window self.out_channel = 100 self.convs = nn.ModuleList([nn.Conv2d(1, self.out_channel, (filter_size, input_size), bias=True) for filter_size in self.filter_sizes]) #前向传播 pooled_outputs = [] for i in range(len(self.filter_sizes)): filter_height = sent_len - self.filter_sizes[i] + 1 conv = self.convs[i](batch_embed) hidden = F.relu(conv) # sen_num x out_channel x filter_height x 1 mp = nn.MaxPool2d((filter_height, 1)) # (filter_height, filter_width) # sen_num x out_channel x 1 x 1 -> sen_num x out_channel pooled = mp(hidden).reshape(sen_num, self.out_channel) pooled_outputs.append(pooled)

TextRNN

#模型搭建 input_size = config.word_dims self.word_lstm = LSTM( input_size=input_size, hidden_size=config.word_hidden_size, num_layers=config.word_num_layers, batch_first=True, bidirectional=True, dropout_in=config.dropout_input, dropout_out=config.dropout_hidden, ) #前向传播 hiddens, _ = self.word_lstm(batch_embed, batch_masks) # sent_len x sen_num x hidden*2 hiddens.transpose_(1, 0) # sen_num x sent_len x hidden*2 if self.training: hiddens = drop_sequence_sharedmask(hiddens, self.dropout_mlp)

baseline 【零基础入门NLP-TASK5基于深度学习的文本分类2】https://tianchi.aliyun.com/notebook-ai/detail?spm=5176.12586969.1002.12.64063dadfiXoX7&postId=118259