Bilinear

class torch.nn.Bilinear(in1_features, in2_features, out_features, bias=True, device=None, dtype=None)

Applies a bilinear transformation to the incoming data: \(y = x_1^T A x_2 + b\).

Parameters:

• 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

Shape:

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

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

• Output: \((*, H_{out})\) where \(H_{out}=\text{out\_features}\) 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 \((\text{out\_features}, \text{in1\_features}, \text{in2\_features})\). The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where \(k = \frac{1}{\text{in1\_features}}\)

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

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])