Differences with torch.nn.NLLLoss

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torch.nn.NLLLoss

torch.nn.NLLLoss(
    weight=None,
    size_average=None,
    ignore_index=-100,
    reduce=None,
    reduction='mean'
)(input, target)

For more information, see torch.nn.NLLLoss.

mindspore.nn.NLLLoss

class mindspore.nn.NLLLoss(
    weight=None,
    ignore_index=-100,
    reduction='mean'
)(logits, labels)

For more information, see mindspore.nn.NLLLoss.

Differences

PyTorch: Calculate the negative log-likelihood loss between the predicted and target values.

MindSpore: There are no functional differences except for two parameters that have been deprecated in PyTorch.

Categories

Subcategories

PyTorch

MindSpore

Differences

Parameters

Parameter 1

weight

weight

Specify the weight of each category

Parameter 2

size_average

-

Deprecated, replaced by reduction. MindSpore does not have this parameter

Parameter 3

ignore_index

ignore_index

Specify the values to be ignored in the labels (generally padding values) so that they do not have an effect on the gradient

Parameter 4

reduce

-

Deprecated, replaced by reduction. MindSpore does not have this parameter

Parameter 5

reduction

reduction

Specify the calculation method to be applied to the output results

Inputs

Input 1

input

logits

The functions are the same, but the parameter names are different

Input 2

target

labels

The functions are the same, but the parameter names are different

Code Example

import numpy as np

data = np.random.randn(2, 2, 3, 3)

# In MindSpore
import mindspore as ms

loss = ms.nn.NLLLoss(ignore_index=-110, reduction="none")
input = ms.Tensor(data, dtype=ms.float32)
target = ms.ops.zeros((2, 3, 3), dtype=ms.int32)
output = loss(input, target)
print(output)
# Out:
# [[[ 0.7047795   0.8196785  -0.7913506 ]
#   [ 0.22157642 -0.18818447 -0.65975004]
#   [ 1.7223285  -0.9269855   0.46461168]]
#
#  [[ 0.21305805 -2.213903    0.36110482]
#   [-0.1900587  -0.56938815  0.12274747]
#   [ 1.149195   -0.8739661  -1.7944012 ]]]


# In PyTorch
import torch

loss = torch.nn.NLLLoss(ignore_index=-110, reduction="none")
input = torch.tensor(data, dtype=torch.float32)
target = torch.zeros((2, 3, 3), dtype=torch.long)
output = loss(input, target)
print(output)
# Out:
# tensor([[[ 0.7048,  0.8197, -0.7914],
#          [ 0.2216, -0.1882, -0.6598],
#          [ 1.7223, -0.9270,  0.4646]],
#         [[ 0.2131, -2.2139,  0.3611],
#          [-0.1901, -0.5694,  0.1227],
#          [ 1.1492, -0.8740, -1.7944]]])