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Linear

class torch.nn.Linear(in_features, out_features, bias=True, device=None, dtype=None)[source]

Applies an affine linear transformation to the incoming data: y=xAT+by = xA^T + b.

This module supports TensorFloat32.

On certain ROCm devices, when using float16 inputs this module will use different precision for backward.

Parameters
  • in_features (int) – size of each 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:
  • Input: (,Hin)(*, H_{in}) where * means any number of dimensions including none and Hin=in_featuresH_{in} = \text{in\_features}.

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

Variables
  • weight (torch.Tensor) – the learnable weights of the module of shape (out_features,in_features)(\text{out\_features}, \text{in\_features}). The values are initialized from U(k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k}), where k=1in_featuresk = \frac{1}{\text{in\_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=1in_featuresk = \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])

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