def init_weights(self): initrange = 0.1 self.decoder.bias.data.zero_() self.decoder.weight.data.uniform_(-initrange, initrange) def forward(self,src): src = src.unsqueeze(2) if self.src_mask is None or self.src_mask.size(0) != len(src): device = src.device mask = self._generate_square_subsequent_mask(len(src)).to(device) self.src_mask = mask src = self.pos_encoder(src) #print('##src',src.shape,self.src_mask.shape) output_1 = self.transformer_encoder(src) #, self.src_mask) output = output_1[0, :, :] output=torch.sum(output,dim=0) # output = self.decoder(output_1[-1]).squeeze(1) return output def _generate_square_subsequent_mask(self, sz): mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) return mask
时间: 2024-04-03 16:36:39 浏览: 17
这段代码看起来是一个基于Transformer的模型,主要包括了初始化权重和前向传播两个函数。在初始化权重的函数中,将decoder的bias设为0,权重采用均匀分布初始化。在前向传播的函数中,首先将输入的src张量增加一个维度,然后通过位置编码器对输入进行编码,接着将编码后的输入传入Transformer的encoder中进行编码,得到输出output_1。在这段代码中,未使用self.src_mask,因此可以考虑将其删除。最后,将output_1中的第一个元素作为输出,通过对第一个维度的求和来实现将输出从3D张量降为2D张量。
相关问题
def init_weights(self): initrange = 0.1 self.decoder.bias.data.zero_() self.decoder.weight.data.uniform_(-initrange, initrange)
这段代码是一个初始化权重的函数,主要包括对decoder的bias和weight进行初始化。首先将初始化范围设为0.1,然后将decoder的bias设为0。最后,对decoder的weight采用均匀分布初始化,范围为[-initrange, initrange]。这个初始化过程可以帮助模型更快、更稳定地收敛,提高模型的训练效果。
nn.Transformer模型代码
***.Transformer模型的代码示例:
```python
import torch
import torch.nn as nn
class TransformerModel(nn.Module):
def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5):
super(TransformerModel, self).__init__()
from torch.nn import TransformerEncoder, TransformerEncoderLayer
self.model_type = 'Transformer'
self.src_mask = None
self.pos_encoder = PositionalEncoding(ninp, dropout)
encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
self.encoder = nn.Embedding(ntoken, ninp)
self.ninp = ninp
self.decoder = nn.Linear(ninp, ntoken)
self.init_weights()
def _generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
return mask
def init_weights(self):
initrange = 0.1
self.encoder.weight.data.uniform_(-initrange, initrange)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, src):
if self.src_mask is None or self.src_mask.size(0) != len(src):
device = src.device
mask = self._generate_square_subsequent_mask(len(src)).to(device)
self.src_mask = mask
src = self.encoder(src) * math.sqrt(self.ninp)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, self.src_mask)
output = self.decoder(output)
return output
```