transpose(2, 1) # [B,H,T] v = self.v.repeat(encoder_outputs.size(0), 1).unsqueeze(1) # [B,1,H] energy = torch.bmm(v, energy) # [B,1,T] attn_weights = F.softmax(energy, dim=2) # [B,1,T] # getting context context = torch.bmm(attn_weights, encoder_outputs) # [B...
bmm(attn_weights.unsqueeze(0), encoder_outputs.unsqueeze(0)) output = torch.cat((embedded[0], attn_applied[0]), 1) output = self.attn_combine(output).unsqueeze(0) output = F.relu(output) output, hidden = self.gru(output, hidden) output = F.log_softmax(self.out(output[0]), dim...
context = attn_weights.bmm(encoder_outputs.transpose(0, 1)) # 使用Luong的公式五连接加权上下文向量和GRU输出 rnn_output = rnn_output.squeeze(0) context = context.squeeze(1) concat_input = torch.cat((rnn_output, context), 1) concat_output = torch.tanh(self.concat(concat_input)) # 使用Luo...
The quantization spec of(transpose_3, cat)is itself. Then it is into an infinite loop afterinput_edge_root_qspec = _find_root_qspec(input_edge_root_qspec, edge_or_node_to_qspec, shared_with_map). the above node/edge is fromedge_or_node_to_qspecin prepare.py duringprepare_qat_pt...
add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training=True, key_padding_mask=None, need_weights=True, attn_mask=None, use_separate_proj_weight=False, q_proj_weight=None, k_proj_weight=None, v_proj_weight=None, static_k=None, static_v=None, average_attn_weights=None,...
encoder_outputs, hidden): seq_len = encoder_outputs.size(1) # 计算注意力权重 attn_weights = torch.bmm...2)) attn_weights = torch.softmax(attn_weights, dim=2) # 加权求和得到上下文向量 context = torch.bmm 1.8K51 【动手实现】DCN ...
transpose(1,2)).squeeze(2) attn_dist = F.softmax(scores, dim = 1) self.attn.append(attn_dist.data) if attn_type == "hard": _, argmax = attn_dist.max(1) one_hot = Variable(torch.zeros_like(attn_dist.data).scatter_(-1, argmax.data.unsqueeze(1), 1)) context = torch.bmm...
importtorchimporttorch.nnasnnimporttorch.nn.functionalasFclassResNetBlock(nn.Module):def__init__(self,in_channels,out_channels,stride=1):# 下面这行代码的作用是调用父类nn.Module的构造函数,这是在创建自定义的神经网络模块时的必须步骤。super(ResNetBlock,self).__init__()self.conv1=nn.Conv2d(in...
x = x.transpose(0,1).contiguous() positions = torch.arange(len(x), device=x.device).unsqueeze(-1) h = self.tokens_embeddings(x) h = h + self.position_embeddings(positions).expand_as(h) h = self.dropout(h) attn_mask =Noneifself.causal: ...
final_rep = torch.bmm(unpacked_out_s.permute(0,2,1), s_attn).squeeze(-1) pred = self.fc(final_rep)returnpred 開發者ID:bearcave9,項目名稱:Weekend-Projects,代碼行數:30,代碼來源:AOA_LSTM.py 示例3: predict_proba ▲點讚 3▼