一、架構圖

二、程式碼實現

class TextCNN(nn.Module):

    def __init__(self,
                 config:TCNNConfig,
                 char_size = 5000, pinyin_size=5000):
        super(TextCNN, self).__init__()
        self.learning_rate = config.learning_rate
        self.keep_dropout = config.keep_dropout
        self.sequence_length 
 
= config.sequence_length
        self.char_embedding_size = config.char_embedding_size
        self.pinyin_embedding_size = config.pinyin_embedding_size
        self.filter_list = config.filter_list
        self.out_channels = config.out_channels
        self.l2_reg_lambda = config.l2_reg_lambda
        self.model_dir 
 
= config.model_dir
        self.data_save_frequency = config.data_save_frequency
        self.model_save_frequency = config.model_save_frequency
        self.char_size = char_size
        self.pinyin_size = pinyin_size
        self.embedding_size = self.char_embedding_size
        self.total_filters_size 
 
= self.out_channels * len(self.filter_list)
        self.build_model()

    def build_model(self):
        # 初始化字向量
        self.char_embeddings = nn.Embedding(self.char_size, self.char_embedding_size)
        # 字向量參與更新
        self.char_embeddings.weight.requires_grad = True
        # 初始化拼音向量
        self.pinyin_embeddings = nn.Embedding(self.pinyin_size, self.pinyin_embedding_size)
        self.pinyin_embeddings.weight.requires_grad = True
        self.conv_list = nn.ModuleList()

        conv_list = [nn.Sequential(
            nn.Conv1d(self.embedding_size, self.out_channels, filter_size),
            nn.BatchNorm1d(self.out_channels),
            nn.ReLU(inplace=True)
        ) for filter_size in self.filter_list]
        # 卷積列表
        self.conv_lists_layer = nn.ModuleList(conv_list)



        self.output_layer = nn.Sequential(
            nn.Dropout(self.keep_dropout),
            nn.Linear(self.total_filters_size, self.total_filters_size),
            nn.ReLU(inplace=True),
            nn.Linear(self.total_filters_size, 2)
        )

    def forward(self, char_id, pinyin_id):
        # char_id = torch.from_numpy(np.array(input[0])).long()
        # pinyin_id = torch.from_numpy(np.array(input[1])).long()
        pooled_outputs = []
        sen_char = self.char_embeddings(char_id)
        sen_pinyin = self.pinyin_embeddings(pinyin_id)
        sen_embed = torch.cat((sen_char, sen_pinyin), dim=1)
        # 轉換成 (N C SEN_LEN) 的形式
        sen_embed = sen_embed.permute(0, 2, 1)
        for conv in self.conv_lists_layer:
            # print(sen_embed.shape)
            conv_output = conv(sen_embed)
            max_polling_output = torch.max(conv_output, dim=2)
            pooled_outputs.append(max_polling_output[0])

        total_pool = torch.cat(pooled_outputs, 1)
        flatten_pool = total_pool.view(-1, self.total_filters_size)
        fc_output = self.output_layer(flatten_pool)
        return fc_output

三、經驗值

1. TextCNN優點是模型簡單、訓練和預測的速度快；缺點是超參（主要是卷積核列表）不易確定，效果不如BiLSTM+Attention；
2. https://blog.csdn.net/dendi_hust/article/details/98211144