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: y=x1TAx2+by = x_1^T A x_2 + b.

  • in1_features (int) – size of each first input sample

  • in2_features (int) – size of each second input sample

  • out_features (int) – size of each output sample

  • bias (bool) – If set to False, the layer will not learn an additive bias. Default: True

  • Input1: (,Hin1)(*, H_{in1}) where Hin1=in1_featuresH_{in1}=\text{in1\_features} and * means any number of additional dimensions including none. All but the last dimension of the inputs should be the same.

  • Input2: (,Hin2)(*, H_{in2}) where Hin2=in2_featuresH_{in2}=\text{in2\_features}.

  • Output: (,Hout)(*, H_{out}) where Hout=out_featuresH_{out}=\text{out\_features} and all but the last dimension are the same shape as the input.

  • weight (torch.Tensor) – the learnable weights of the module of shape (out_features,in1_features,in2_features)(\text{out\_features}, \text{in1\_features}, \text{in2\_features}). The values are initialized from U(k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k}), where k=1in1_featuresk = \frac{1}{\text{in1\_features}}

  • bias – the learnable bias of the module of shape (out_features)(\text{out\_features}). If bias is True, the values are initialized from U(k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k}), where k=1in1_featuresk = \frac{1}{\text{in1\_features}}


>>> 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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