是不是相当于一个全连接层，把hid size映射为pro_j size。
N表示的是batch_size,和输入中一样,若使用了batch_first=True,则应该是(N, L, D*H)。 L表示的还是输入序列的长度。 H表示输出特征的维数,若给定了proj_size则为proj_size,否则就是隐藏层的大小(hidden_size)。 D是2,若bidirectional=True否则为1。这就是之前强调这个参数的原因,若不选择bidirectional,则隐...
proj_size = 3 # 要比hidden_size小 input = torch.randn(batch_size, seq_len, input_size) c_0 = torch.randn(batch_size, h_size) h_0 = torch.randn(batch_size, proj_size) # 注意这里从原来的 h_size 换成了 proj_size # 调用官方 LSTM API lstm_layer = nn.LSTM(input_size, h_size...
.view(1, 1, n_ctx, n_ctx)) self.dropout = nn.Dropout(0.1) self.c_proj = Conv1D(d_model, d_model) def split_heads(self, x): "return shape [`batch`, `head`, `sequence`, `features`]" new_shape = x.size()[:-1] + (self.n_head, x.size(-1)//self....
proj_size– If>0, will use LSTM with projections of corresponding size. Default: 0 Inputs: input, (h_0, c_0) input:tensorof shape(L,Hin)(L,Hin)for unbatched input,(L,N,Hin)(L,N,Hin)when batch_first=False or(N,L,Hin)(N,L,Hin)when batch_first=True containing the features ...
class MambaBlock(nn.Module):def __init__(self, seq_len, d_model, state_size, device):super(MambaBlock, self).__init__() self.inp_proj = nn.Linear(d_model, 2*d_model, device=device)self.out_proj = nn.Linear(2*d_model, d_m...
proj_size : If> 0, will use LSTM with projections (投影) of corresponding size (denote \(H_{out}=proj_size\)). Default: 0 (denote \(H_{out}=n\)). 换句话说,proj_size 表现了细胞状态与隐藏状态的维数是否一致. 声明LSTM 网络的输入与输出: ...
proj_drop=0., attn_drop=0., init_values=None, drop_path=0., act_layer=None, norm_layer=None, mlp_layer=None ): super().__init__( hidden_size=dim, ffn_hidden_size=int(dim * mlp_ratio), num_attention_heads=num_heads,
self.register_parameter('v_proj_weight',None) ifbias: self.in_proj_bias = Parameter(torch.empty(3* embed_dim)) else: self.register_parameter('in_proj_bias',None) # 后期会将所有头的注意力拼接在一起然后乘上权重矩阵输出 # out_proj是为了后期准备的...
proj_size if self.proj_size > 0 else self.hidden_size h_zeros = torch.zeros(self.num_layers * num_directions, max_batch_size, real_hidden_size, dtype=input.dtype, device=input.device) c_zeros = torch.zeros(self.num_layers * num_directions, max_batch_size, self.hidden_size,...