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Bilinear
class torch.nn.Bilinear(in1_features, in2_features, out_features, bias=True, device=None, dtype=None)
[source]-
Applies a bilinear transformation to the incoming data:
- Parameters
- Shape:
-
- Input1: where and means any number of additional dimensions including none. All but the last dimension of the inputs should be the same.
- Input2: where .
- Output: where and all but the last dimension are the same shape as the input.
- Variables
-
- weight (torch.Tensor) – the learnable weights of the module of shape . The values are initialized from , where
- bias – the learnable bias of the module of shape
. If
bias
isTrue
, the values are initialized from , where
Examples:
>>> m = nn.Bilinear(20, 30, 40) >>> input1 = torch.randn(128, 20) >>> input2 = torch.randn(128, 30) >>> output = m(input1, input2) >>> print(output.size()) torch.Size([128, 40])
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https://pytorch.org/docs/2.1/generated/torch.nn.Bilinear.html