Linear

class torch.nn.Linear(in_features: int, out_features: int, bias: bool = True)

Applies a linear transformation to the incoming data: $y = xA^T + b$

Parameters

• in_features – size of each input sample

• out_features – size of each output sample

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

Shape:

• Input: $(N, *, H_{in})$ where $*$ means any number of additional dimensions and $H_{in} = \text{in\_features}$

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

Variables

• ~Linear.weight – the learnable weights of the module of shape $(\text{out\_features}, \text{in\_features})$ . The values are initialized from $\mathcal{U}(-\sqrt{k}, \sqrt{k})$ , where $k = \frac{1}{\text{in\_features}}$

• ~Linear.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 $k = \frac{1}{\text{in\_features}}$

Examples:

>>> m = nn.Linear(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])