mindspore.mint.nn.functional.linear

mindspore.mint.nn.functional.linear(input, weight, bias=None)

Applies the dense connected operation to the input. The dense function is defined as:

\[output = input * weight^{T} + bias\]

Warning

This is an experimental API that is subject to change or deletion.

Parameters

input (Tensor) – Input Tensor of shape \((*, in\_channels)\), where \(*\) means any number of additional dimensions.
weight (Tensor) – The weight applied to the input. The shape is \((out\_channels, in\_channels)\) or \((in\_channels)\).
bias (Tensor, optional) – Additive biases to the output. The shape is \((out\_channels)\) or \(()\). Defaults: None, the bias is 0.

Returns

Output whose shape is determined by the shape of the input and the weight.

Raises

TypeError – If input is not Tensor.
TypeError – If weight is not Tensor.
TypeError – If bias is not Tensor.

Supported Platforms:: Ascend

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor, mint
>>> input = Tensor([[-1., 1., 2.], [-3., -3., 1.]], mindspore.float32)
>>> weight = Tensor([[-2., -2., -2.], [0., -1., 0.]], mindspore.float32)
>>> bias = Tensor([0., 1.], mindspore.float32)
>>> output = mint.nn.functional.linear(input, weight, bias)
>>> print(output)
[[-4.  0.]
 [10.  4.]]