Python class

Linear

`Linear`

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

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.nn import Linear
from max.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]

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

`forward()`

forward(x)

Applies a linear transformation to the 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.

Parameters:: x (Tensor) – The input tensor
Returns:: The result of applying the linear transformation to the tensor.
Return type:: Tensor

`in_dim`

property in_dim: Dim

The input dimension for the transformation.

`out_dim`

property out_dim: Dim

The output dimension for the transformation.

`weight`

weight: Tensor

The weight Tensor for the linear transformation.

Note

For the legacy graph-based linear layer, see legacy/linear.

Linear​

bias​

forward()​

in_dim​

out_dim​

weight​