Function Differences with torch.nn.MSELoss

torch.nn.MSELoss

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

For more information, see torch.nn.MSELoss.

mindspore.nn.MSELoss

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

For more information, see mindspore.nn.MSELoss.

Differences

PyTorch: Used to calculate the mean square error for each element of the input and target. The reduction parameter specifies the type of statute applied to the loss.

MindSpore: Implement functions consistent with PyTorch.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameters	Parameter 1	size_average	-	Deprecated, replaced by reduction
	Parameter 2	reduce	-	Deprecated, replaced by reduction
	Parameter 3	reduction	reduction	-
Inputs	Input 1	input	logits	Same function, different parameter names
	Input 2	target	labels	Same function, different parameter names

Code Example 1

Compute the mean square error of input and target. By default, reduction='mean'.

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

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

Code Example 2

Compute the mean square error of input and target for the summation mode statute.

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

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