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: \(y = xA^T + b\).

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: \((*, H_{in})\) where \(*\) means any number of dimensions including none and \(H_{in} = \text{in\_features}\).
Output: \((*, H_{out})\) where all but the last dimension are the same shape as the input and \(H_{out} = \text{out\_features}\).

Variables:

weight (torch.Tensor) – 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 \(k = \frac{1}{\text{in\_features}}\)
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])

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