Comments (1)
Hi. Sorry for the late reply. I didn't notice that the issue was created.
It's been a while since I implemented the code, but as far as I remember, the reason for using weight.data.t()
in weight initialization is to make it compatible to the PyTorch's nn.Linear
.
PyTorch's nn.Linear
internally has a weight matrix whose shape is (out_features, in_features)
, however weight
matrix in this repo's implementation has the shape of (in_features, out_features)
.
Since kaiming_normal
initializer considers fan-in or fan-out values of a weight matrix, I transposed the weight
matrix before giving it to init.kaiming_normal
.
from lsoftmax-pytorch.
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from lsoftmax-pytorch.