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.proj_k = nn.Conv2d(in_c, in_c, 1, stride=1, padding=0):创建一个 1x1 卷积层proj_k,用于生成键(Key)向量,作用和proj_q类似。 self.proj_v = nn.Conv2d(in_c, in_c, 1, stride=1, padding=0):创建一个 1x1 卷积层proj_v,用于生成值(Value)向量。 self.proj = nn.Conv2d(in_c...
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...
bias.data.zero_() class SelfAttention(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).__init__() self.chanel_in = in_dim self.activation = activation self.f =...
size()[:-2] + (self.all_head_size,) # [bs, seqlen, 128] context_layer = context_layer.view(*new_context_layer_shape) return context_layer # [bs, seqlen, 128] 得到输出 在pytorch上的小实验,用法就是先将Self Attention类实例化,然后输入记得有两个,x_in和x_mask。x_mask = (input_...
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...
heads=1, concat=False, dropout=dropout, leaky_relu_slope=leaky_relu_slope ) def forward(self, input_tensor: torch.Tensor , adj_mat: torch.Tensor): # Apply the first Graph Attention layer x = self.gat1(input_tensor, adj_mat) x = F.elu(x) # Apply ELU activation f...
attention = nn.Linear(hidden_dim * 2, 1) self.output_layer = nn.Linear(hidden_dim, output_dim) def forward(self, src, tgt): # Encoder encoder_output, (hidden, cell) = self.encoder(src) # Decoder with Attention output = [] for i in range(tgt.size(0)): # 计算注意力权重 ...