比较与torch.nn.Linear的功能差异

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

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

# 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)