Function Differences with torch.nn.Linear

torch.nn.Linear

class torch.nn.Linear(
    in_features,
    out_features,
    bias=True
)(input) -> Tensor

For more information, see torch.nn.Linear.

mindspore.nn.Dense

class mindspore.nn.Dense(
    in_channels,
    out_channels,
    weight_init='normal',
    bias_init='zeros',
    has_bias=True,
    activation=None
)(x) -> Tensor

For more information, see mindspore.nn.Dense.

Differences

Pytorch: Fully connected layer that implements the matrix multiplication operation.

MindSpore: The implementation function of the API in MindSpore is basically the same as that of PyTorch, and it is possible to add activation functions after the fully connected layer.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	in_features	in_channels	Same function, different parameter names
	Parameter 2	out_features	out_channels	Same function, different parameter names
	Parameter 3	bias	has_bias	Same function, different parameter names
	Parameter 4	-	weight_init	Initialization method for the weight parameter, which is not available for PyTorch
	Parameter 5	-	bias_init	Initialization method for the bias parameter, which is not available for PyTorch
	Parameter 6	-	activation	Activation function applied to the output of the fully connected layer, which is not available for PyTorch
Input	Single input	input	x	Same function, only different parameter names

Code Example

The two APIs achieve the same function and have the same usage.

# PyTorch
import torch
from torch import nn
import numpy as np

net = nn.Linear(3, 4)
x = torch.tensor(np.array([[180, 234, 154], [244, 48, 247]]), dtype=torch.float)
output = net(x)
print(output.detach().numpy().shape)
# (2, 4)

# MindSpore
import mindspore
from mindspore import Tensor, nn
import numpy as np

x = Tensor(np.array([[180, 234, 154], [244, 48, 247]]), mindspore.float32)
net = nn.Dense(3, 4)
output = net(x)
print(output.shape)
# (2, 4)