def __init__(self, d_model, num_heads): super(MultiHeadAttention, self).__init__() self.d_model = d_model self.num_heads = num_heads self.head_dim = d_model // num_heads # 定义线性层,并将其放置在GPU上(如果可用) # 这里的线性层用于对输入进行线性变换,以得到Q、K、V矩阵 self....
self.dropout = torch.nn.Dropout(0.5) def forward(self, x): input_ids = x['input_ids'].to(device) token_type_ids = x['token_type_ids'].to(device) attention_mask = x['attention_mask'].to(device) output = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_...
254 Factor Graph Attention https://github.com/idansc/fga Tuesday Poster 1.1 206 Idan Schwartz Idan Schwartz, Seunghak Yu, Tamir Hazan, Alexander Schwing 192 263 A Simple Baseline for Audio-Visual Scene-Aware Dialog https://github.com/idansc/simple-avsd Thursday Poster 3.2 196 Idan Schwart...
def __init__(self, d_model, num_heads): super(MultiHeadAttention, self).__init__() self.d_model = d_model self.num_heads = num_heads self.head_dim = d_model // num_heads # 定义线性层,并将其放置在GPU上(如果可用) # 这里的线性层用于对输入进行线性变换,以得到Q、K、V矩阵 self....
super(MultiHeadAttention, self).__init__() self.d_model = d_model self.num_heads = num_heads self.head_dim = d_model // num_heads # 定义线性层,并将其放置在GPU上(如果可用) # 这里的线性层用于对输入进行线性变换,以得到Q、K、V矩阵 ...