如果 Proj_size > 0,则形状为 (4*hidden_size, proj_size) ~LSTM.bias_ih_l[k]– 学习得到的第k层的input-hidden 的偏置 (b_ii|b_if|b_ig|b_io), 形状为 (4*hidden_size) ~LSTM.bias_hh_l[k]– 学习得到的第k层的hidden -hidden 的偏置 (b_hi|b_hf|b_hg|b_ho), 形状为 (4*hidden...
是不是相当于一个全连接层，把hid size映射为pro_j size。
是不是相当于一个全连接层，把hid size映射为pro_j size。
bidirectional : IfTrue, becomes a bidirectional LSTM (denote \(bid=2\)). Default: False (denote \(bid=1\)). 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_s...
bidirectional– IfTrue,使用双向bidirectional LSTM.默认值:False proj_size– If>0,将使用LSTM和相应大小的投影。默认值:0 Inputs: input, (h_0, c_0) input: tensor of shape(L, N, Hin)whenbatch_first=Falseor (N,L,Hin)whenbatch_first=True包含输入序列的特征。输入也可以是一个压缩的可变长度序列...
torch.nn.LSTM(input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0.0, bidirectional=False, proj_size=0, device=None, dtype=None) 如下图: •input_size:序列中每个元素特征的维度•hidden_size:每个元素hidden state的维度•num_layers:多个LSTM层可以堆叠在一起•bat...
proj_size:没用到,默认为0。 2.2 输入 LSTM层的输入格式为 [input,(h_0,c_0)] 其中input为(L, N, H)形式的tensor,(h_0, c_0)可以先略过不看。 N表示的是batch_size,若使用了batch_first=True,则应该是(N, L, H)。 L表示的是输入序列的长度(sequence length)。
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 sLSTMBlock(nn.Module): def __init__(self, input_size, hidden_size, num_heads, proj_factor=4/3): super(sLSTMBlock, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_heads = num_heads ...
super(sLSTMBlock, self).__init__() self.input_size=input_size self.hidden_size=hidden_size self.num_heads=num_heads self.head_size=hidden_size//num_heads self.proj_factor=proj_factor asserthidden_size%num_heads==0 assertproj_factor>0 ...