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: $y = x_1^T A x_2 + b$ .

Parameters

in1_features (int) – size of each first input sample, must be > 0
in2_features (int) – size of each second input sample, must be > 0
out_features (int) – size of each output sample, must be > 0
bias (bool) – If set to False, the layer will not learn an additive bias. Default: True

Shape:

Input1: $(*, H_\text{in1})$ where $H_\text{in1}=\text{in1\_features}$ and $*$ means any number of additional dimensions including none. All but the last dimension of the inputs should be the same.
Input2: $(*, H_\text{in2})$ where $H_\text{in2}=\text{in2\_features}$ .
Output: $(*, H_\text{out})$ where $H_\text{out}=\text{out\_features}$ 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])

extra_repr() [source]

Return the extra representation of the module.

Return type: str

forward(input1, input2) [source]

Runs the forward pass.

Return type: Tensor

reset_parameters() [source]: Resets parameters based on their initialization used in __init__.