Self-Attention Layer 一次检查同一句子中的所有单词的注意力,这使得它成为一个简单的矩阵计算,并且能够在计算单元上并行计算。 此外,Self-Attention Layer 可以使用下面提到的 Multi-Head 架构来拓宽视野,也就是多头注意力机制。Self-Attention Layer 基本结构如下: 对于每个输入 ,首先经过Embedding层对每个输入...
self.output_conv = nn.Conv2d(out_channels//2, output_channels, kernel_size=1) self.relu = nn.ReLU(inplace=True) self.embeddings = SinusoidalEmbeddings(time_steps=time_steps, embed_dim=max(Channels)) for i in range(self.num_layers): layer = UnetLayer( upscale=Upscales[i], attention=A...
mixed_value_layer = self.value(input_tensor) 1. 2. 3. 切分为num_attention_heads个头,并变换维度。维度变化为:mixed_query_layer 到query_layer def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x...
key_layer = key_layer.transpose(-1, -2) outputs = torch.zeros([1, self.num_attention_heads, 512, 64]) foriinrange(512):# sequence length Qi = torch.narrow(query_layer, 2, i, 1)# (1, 16, 1, 64) sum_s = torch.zeros([1, self.num_atte...
query = self.query_layer(x) self.value_layer.weight.data = w_v.mT self.value_layer.bias.data = torch.Tensor([0.0]) value = self.value_layer(x)print('key:\n', key)print('query:\n', query)print('value:\n', value) attention_scores = torch.matmul(query, key.mT)# query * (ke...
其他的FeedFoward和LayerNorm等都通过pytorch中常用的接口来实现 forward forward的代码位于:https://github.com/pytorch/pytorch/blob/8ac9b20d4b090c213799e81acf48a55ea8d437d6/torch/nn/modules/transformer.py#L594 其中最核心的部分如下: 这里依次走了SelfAttention层(_sa_block)和FeedForward层(_ff_block)。
self.pinyin_embeddings.weight.requires_grad=True#attention layerself.attention_layer =nn.Sequential( nn.Linear(self.hidden_dims, self.hidden_dims), nn.ReLU(inplace=True) )#self.attention_weights = self.attention_weights.view(self.hidden_dims, 1)#双层lstmself.lstm_net =nn.LSTM(self.char_embed...
definit_conv(conv,glu=True):init.xavier_uniform_(conv.weight)ifconv.bias is not None:conv.bias.data.zero_()classSelfAttention(nn.Module):r""" Self attention Layer.Source paper:https://arxiv.org/abs/1805.08318""" def__init__(self,in_dim,activation=F.relu):super(SelfAttention,self)._...
将h作为输入,通过self.attention_layer得到attention的计算向量atten_w(shape:[batch_size, time_step, hidden_dims]); 将第二步的h进行tanh激活,得到m(shape:[batch_size, time_step, hidden_dims]),留待后续进行残差计算; 将atten_w的2、3维度进行调换,并与m进行矩阵的乘法运算,得到atten_context(shape:[ba...
先将一系列标记(tokens)传递到嵌入层(embedding layer),然后是位置编码层(positional encoding layer), 以说明单词的顺序(有关详细信息,请参阅下一段)。 nn.TransformerEncoder 由多层 nn.TransformerEncoderLayer 组成。 由于 nn.TransformerEncoder 中的自关注层(self-attention layers)只允许关注序列中的早期位置,因...