帮我用TCN.TemporalConvNet,bert和pytorch写一个简单的提取文本时序特征的网络
时间: 2023-05-17 14:06:57 浏览: 68
好的,以下是一个简单的使用TCN、BERT和PyTorch的网络,用于提取文本时序特征:
```python
import torch
import torch.nn as nn
from transformers import BertModel
class TextTCNBERT(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, bert_model_name):
super(TextTCNBERT, self).__init__()
self.bert = BertModel.from_pretrained(bert_model_name)
self.tcn = TCN(input_size, hidden_size, num_layers)
self.fc = nn.Linear(hidden_size, 1)
def forward(self, x):
# x: (batch_size, seq_len)
bert_output = self.bert(x)[0] # (batch_size, seq_len, hidden_size)
tcn_output = self.tcn(bert_output.transpose(1, 2)) # (batch_size, hidden_size, seq_len)
pooled_output = nn.functional.max_pool1d(tcn_output, tcn_output.size(2)).squeeze(2) # (batch_size, hidden_size)
logits = self.fc(pooled_output) # (batch_size, 1)
return logits.squeeze(1)
class TCN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers):
super(TCN, self).__init__()
self.tcn = nn.Sequential(
*[TemporalBlock(input_size, hidden_size, kernel_size=2 ** i, dilation=2 ** i) for i in range(num_layers)]
)
def forward(self, x):
# x: (batch_size, input_size, seq_len)
return self.tcn(x)
class TemporalBlock(nn.Module):
def __init__(self, input_size, output_size, kernel_size, dilation):
super(TemporalBlock, self).__init__()
self.conv1 = nn.Conv1d(input_size, output_size, kernel_size, dilation=dilation, padding=(kernel_size - 1) * dilation)
self.relu1 = nn.ReLU()
self.dropout1 = nn.Dropout(0.2)
self.conv2 = nn.Conv1d(output_size, output_size, kernel_size, dilation=dilation, padding=(kernel_size - 1) * dilation)
self.relu2 = nn.ReLU()
self.dropout2 = nn.Dropout(0.2)
self.net = nn.Sequential(self.conv1, self.relu1, self.dropout1, self.conv2, self.relu2, self.dropout2)
self.downsample = nn.Conv1d(input_size, output_size, 1) if input_size != output_size else None
self.relu = nn.ReLU()
def forward(self, x):
# x: (batch_size, input_size, seq_len)
out = self.net(x)
res = x if self.downsample is None else self.downsample(x)
return self.relu(out + res)
```
这个网络使用了TCN和BERT来提取文本时序特征,其中TCN用于对BERT的输出进行时序特征提取,BERT用于对文本进行编码。具体来说,BERT将文本编码为一个向量序列,TCN对这个向量序列进行卷积操作,得到一个新的向量序列,最后通过池化操作将这个向量序列压缩成一个固定长度的向量,再通过一个全连接层输出最终的结果。
使用时,可以按照以下方式调用:
```python
model = TextTCNBERT(input_size=768, hidden_size=256, num_layers=3, bert_model_name='bert-base-uncased')
input_ids = torch.tensor([[31, 51, 99, 15, 0], [15, 5, 0, 0, 0]]) # (batch_size, seq_len)
logits = model(input_ids)
```
其中`input_size`为BERT的输出维度,`hidden_size`为TCN的隐藏层维度,`num_layers`为TCN的层数,`bert_model_name`为使用的BERT模型名称。`input_ids`为输入的文本序列,需要使用BERT的tokenizer将文本转换为对应的id序列。最后输出的`logits`为模型的预测结果。