针对你的问题“transformer代码torch”,我将提供一个完整的Transformer模型在PyTorch中的实现示例。这个示例将涵盖Transformer模型的基础结构,包括Encoder和Decoder部分,但请注意,实际应用中可能需要根据具体任务(如机器翻译、文本生成等)进行调整和优化。 1. Transformer模型结构概述 Transformer模型主要由Encoder和Decoder两部分...
def make_universal_model(src_vocab, tgt_vocab, dim_model=512, dim_ff=2048, h=8, dropout=0.1): c = copy.deepcopy attn = MultiHeadAttention(h, dim_model) ff = PositionwiseFeedForward(dim_model, dim_ff) pos_enc = PositionalEncoding(dim_model, dropout) time_enc = PositionalEncoding(dim...
attention_scores = attention_scores / math.sqrt(self.attention_head_size)# Apply the attention mask is (precomputed for all layers in BertModel forward() function)attention_scores = attention_scores + attention_mask# Normalize the attention scores to probabilities.attention_probs =nn.Softmax(dim=...
Source File: transformer_utils.py From Count-Sketch-Optimizers with Apache License 2.0 5 votes def buffered_mask(self, tensor): dim = tensor.size(-1) if self._mask is None: self._mask = torch.triu(fill_with_neg_inf(tensor.new(dim, dim)), 1) if self._mask.size(0) < dim: ...
self.pos_ffn=PoswiseFeedForwardNet() defforward(self,dec_inputs,enc_outputs,enc_attn_mask): dec_outputs,dec_self_attn=self.dec_self_attn(dec_inputs,dec_inputs,dec_inputs,None) dec_outputs,dec_enc_attn=self.dec_enc_attn(dec_outputs,enc_outputs,enc_outputs,enc_attn_mask) ...
out = self.fc(out)returnF.log_softmax(out, dim=-1) 开发者ID:aimagelab,项目名称:meshed-memory-transformer,代码行数:27,代码来源:decoders.py 示例4: comp ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importtriu[as 别名]defcomp(self, inpu):in_mat1 = torch....
f1 = nn.Linear(state_dim, l1) self.ln1 = nn.LayerNorm(l1) #Hidden Layer 2 self.f2 = nn.Linear(l1, l2) self.ln2 = nn.LayerNorm(l2) #Out self.w_out = nn.Linear(l2, action_dim) Example #17Source File: module.py From Transformer-TTS with MIT License 6 votes def __init__...