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Python class

Linear

`Linear`

class max.experimental.nn.Linear(in_dim, out_dim, *, bias=True)

source

Bases: Module

A unary linear transformation over an input tensor.

Linear is defined as f(x) = x @ W.T + B where W is the weight tensor and B is an optional bias tensor.

If W is not square then the transformation represents a dimensionality change. By convention the weight tensor is stored transposed.

from max.experimental.nn import Linear
from max.experimental.tensor import Tensor

model = Linear(5, 10)

assert dict(model.parameters) == {
    "weight": model.weight, "bias": model.bias
}

result = model(Tensor.ones([5]))
assert result.shape == [10]

Constructs a random linear transformation of the given dimensions.

Parameters:

in_dim (DimLike) – The dimensionality of the input to the transformation
out_dim (DimLike) – The dimensionality after applying the transformation to the input tensor of dim in_dim.
bias (Tensor | Literal[0]) – Whether to use a bias in the transformation.

`bias`

bias: Tensor | Literal[0]

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The bias Tensor for the linear transformation (or 0 if bias is disabled).

`forward()`

forward(x)

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Defines the computation performed by the module.

Users must override this method in their subclass to define the module’s computation.

Parameters:

*args – Positional arguments for the computation.
**kwargs – Keyword arguments for the computation.
x (Tensor)

Returns:

The result of applying the module to the input.

Raises:

NotImplementedError – If the subclass does not override this method.

Return type:

Tensor

`in_dim`

property in_dim: Dim

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The input dimension for the transformation.

`out_dim`

property out_dim: Dim

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The output dimension for the transformation.

`weight`

weight: Tensor

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The weight Tensor for the linear transformation.

Linear​

bias​

forward()​

in_dim​

out_dim​

weight​