比较与torch.nn.MSELoss的功能差异

torch.nn.MSELoss

torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean')(input, target) -> Tensor

更多内容详见torch.nn.MSELoss。

mindspore.nn.MSELoss

mindspore.nn.MSELoss(reduction='mean')(logits, labels) -> Tensor

更多内容详见mindspore.nn.MSELoss。

差异对比

PyTorch：用于计算输入x和y每一个元素的均方误差，reduction参数指定应用于loss的规约类型。

MindSpore：与PyTorch实现同样的功能。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	size_average	-	已弃用，被reduction取代，MindSpore无此参数
	参数2	reduce	-	已弃用，被reduction取代，MindSpore无此参数
	参数3	reduction	reduction	-
	参数4	input	logits	功能一致，参数名不同
	参数5	target	labels	功能一致，参数名不同

代码示例1

计算input和target的均方误差。

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

# 默认情况，reduction='mean'
loss = nn.MSELoss()
input_ = np.array([1, 1, 1, 1]).reshape((2, 2))
input = tensor(input_, dtype=torch.float32)
target_ = np.array([1, 2, 2, 1]).reshape((2, 2))
target = tensor(target_, dtype=torch.float32)
output = loss(input, target)
print(output.numpy())
# 0.5

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

loss = nn.MSELoss()
input_ = np.array([1, 1, 1, 1]).reshape((2, 2))
input = Tensor(input_, dtype=mindspore.float32)
target_ = np.array([1, 2, 2, 1]).reshape((2, 2))
target = Tensor(target_, dtype=mindspore.float32)
output = loss(input, target)
print(output)
# 0.5

代码示例2

计算input和target的均方误差，以求和方式规约。

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

# redcution='sum'
loss = nn.MSELoss(reduction='sum')
input_ = np.array([1, 1, 1, 1]).reshape((2, 2))
input = tensor(input_, dtype=torch.float32)
target_ = np.array([1, 2, 2, 1]).reshape((2, 2))
target = tensor(target_, dtype=torch.float32)
output = loss(input, target)
print(output.numpy())
# 2.0

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

loss = nn.MSELoss(reduction='sum')
input_ = np.array([1, 1, 1, 1]).reshape((2, 2))
input = Tensor(input_, dtype=mindspore.float32)
target_ = np.array([1, 2, 2, 1]).reshape((2, 2))
target = Tensor(target_, dtype=mindspore.float32)
output = loss(input, target)
print(output)
# 2.0