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mindspore.mint.nn.Linear

class mindspore.mint.nn.Linear(in_features, out_features, bias=True, weight_init=None, bias_init=None, dtype=None)

The linear connected layer.

Applies linear connected layer for the input. This layer implements the operation as:

$$\text{outputs}=X\ast kernel+bias$$

Warning

On the Ascend platform, if bias is False , the x cannot be greater than 6D in PYNATIVE or KBK mode.

where $X$ is the input tensors, $\text{kernel}$ is a weight matrix with the same data type as the $X$ created by the layer, and $\text{bias}$ is a bias vector with the same data type as the $X$ created by the layer (only if the parameter bias is True).

Warning

In PyNative mode, if bias is False , the x cannot be greater than 6D.

Parameters

• in_features (int) – The number of features in the input space.

• out_features (int) – The number of features in the output space.

• bias (bool, optional) – Specifies whether the layer uses a bias vector $\text{bias}$. Default: True.

• weight_init (Union[Tensor, str, Initializer, numbers.Number], optional) – The trainable weight_init parameter. The dtype is same as x. The values of str refer to the function initializer. Default: None , weight will be initialized using HeUniform.

• bias_init (Union[Tensor, str, Initializer, numbers.Number], optional) – The trainable bias_init parameter. The dtype is same as x. The values of str refer to the function initializer. Default: None , bias will be initialized using Uniform.

• dtype (mindspore.dtype, optional) – Data type of Parameter. Default: None . If dtype is None , dtype is set to mstype.float32 when initializing the method. When weight_init is Tensor, Parameter has the same data type as weight_init , in other cases, Parameter has the same data type as dtype, the same goes for bias_init.

Inputs:

• x (Tensor) - Tensor of shape $(\ast ,in\mathrm{\_}features)$. The in_features in Args should be equal to $in\mathrm{\_}features$ in Inputs.

Outputs:

Tensor of shape $(\ast ,out\mathrm{\_}features)$.

Raises

• TypeError – If in_features or out_features is not an int.

• TypeError – If bias is not a bool.

• ValueError – If length of shape of weight_init is not equal to 2 or shape[0] of weight_init is not equal to out_features or shape[1] of weight_init is not equal to in_features.

• ValueError – If length of shape of bias_init is not equal to 1 or shape[0] of bias_init is not equal to out_features.

• RuntimeError – On the Ascend platform, if bias is False and x is greater than 6D in PYNATIVE or KBK mode.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore import mint
>>> import numpy as np
>>> x = Tensor(np.array([[180, 234, 154], [244, 48, 247]]), mindspore.float32)
>>> net = mint.nn.Linear(3, 4)
>>> output = net(x)
>>> print(output.shape)
(2, 4)