num_layers * num_directions, n_hidden]lstm_hidden = torch.sum(lstm_hidden, dim=1)#[batch_size, 1, n_hidden]lstm_hidden = lstm_hidden.unsqueeze(1)#atten_w [batch_size, 1, hidden_dims
hidden_state = Variable(torch.zeros(1*2, len(X), n_hidden))# [num_layers(=1) * num_directions(=2), batch_size, n_hidden]cell_state = Variable(torch.zeros(1*2, len(X), n_hidden))# [num_layers(=1) * num_directions(=2...
num_layers * num_directions, n_hidden]lstm_hidden = torch.sum(lstm_hidden, dim=1)#[batch_size, 1, n_hidden]lstm_hidden = lstm_hidden.unsqueeze(1)#atten_w [batch_size, 1, hidden_dims
""" 1. 实例化 num_layers 个TransformerEncoderLayer """ self.layers = nn.ModuleList([ TransformerEncoderLayer(d_model, num_heads, dim_feedforward, dropout) for _ in range(num_layers) ]) """ 2. 初始化位置编码器 """ self.pe = PositionalEncoding(num_hiddens=d_model, max_len=max_len)...
Attention 的 PyTorch应用实现 import torch import torch.nn as nn class BiLSTM_Attention(nn.Module): def __init__(self): super(BiLSTM_Attention, self).__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.lstm = nn.LSTM(embedding_dim, n_hidden, bidirectional=True) ...
AttentionDecoder):def__init__(self,vocab_size,embed_size,num_hiddens,num_layers,dropout=0,**...
第一步:构建Encoder: 代码: classencode(nn.Module):def__init__(self):super(encode,self).__init__() self.embedd = nn.Embedding(dic_num,dim_num) self.gru = nn.GRU(dim_num,hid_dim_num,num_layers,bidirectional=True) self.fn = nn.Linear(hid_dim_num*2,hid_dim_num) ...
pytorch源码来源: Diego999/pyGATgithub.com/Diego999/pyGAT 注:原文很简单,就不解读了,本文只解读该版本的源码,主要目的是供初学者练练手。 一、需要注意的地方 1、pytorch中 Variable、Parameter、Tensor之间的使用差异 参考博客:[1]Pytorch 中的 Tensor , Variable和Parameter区别与联系 ...
(1)):# 对于每个时间步,使用上下文向量和当前解码器输出进行计算# 根据具体实现细节进行调整output=self.decoder(input,context)outputs[:,t,:]=outputreturnoutputs,attn_weights# 实例化模型encoder=Encoder(input_dim,hidden_dim,num_layers)decoder=Decoder(output_dim,hidden_dim,hidden_dim)attention=Attention(...
attentions=True)# 设置输入输出等操作tokenizer=BertTokenizer.from_pretrained('/data/project/IC_Pytorch...