mindspore.mint.nn.Linear
- class mindspore.mint.nn.Linear(in_features, out_features, bias=True, weight_init=None, bias_init=None, dtype=None)[source]
The linear connected layer.
Applies linear connected layer for the input. This layer implements the operation as:
\[\text{outputs} = X * kernel + bias\]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 has_bias is True).
- 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) – Specifies whether the layer uses a bias vector \(\text{bias}\). Default:
True
.weight_init (Union[Tensor, str, Initializer, numbers.Number]) – 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]) – 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
) – Data type of Parameter. Default:None
.
- Inputs:
x (Tensor) - Tensor of shape \((*, in\_features)\). The in_features in Args should be equal to \(in\_features\) in Inputs.
- Outputs:
Tensor of shape \((*, out\_features)\).
- 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.
- 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)