Linear#

API#

class warp_nn.modules.layers.Linear(in_features: int, out_features: int, *, bias: bool = True)[source]#

Bases: Module

Apply a linear transformation over the final dimension of the input.

\[\text{Linear}(x) = W \, x + b\]

Learnable parameters:

Name

Shape

Description

\(W\)

weight

(out_features, in_features)

Weights

\(b\)

bias

(out_features, 1)

Bias. Only if bias is true

The parameters are initialized from the uniform distribution \(u(-k, k)\) where \(k = \frac{1}{\sqrt{\text{in\_features}}}\).

Parameters:

  • in_features – The number of input features.

  • out_features – The number of output features.

  • bias – Whether to include a bias term.

__call__(
input: array,
) → array[source]#

Forward pass of the module.

Parameters:

input – The input array, with shape (batch_size, in_features).

Returns:

The output array, with shape (batch_size, out_features).