Differences with torch.nn.CrossEntropyLoss
torch.nn.CrossEntropyLoss
class torch.nn.CrossEntropyLoss(
weight=None,
size_average=None,
ignore_index=-100,
reduce=None,
reduction='mean'
)(input, target) -> Tensor
For more information, see torch.nn.CrossEntropyLoss.
mindspore.nn.CrossEntropyLoss
class mindspore.nn.CrossEntropyLoss(
weight=None,
ignore_index=-100,
reduction='mean',
label_smoothing=0.0
)(logits, labels) -> Tensor
For more information, see mindspore.nn.CrossEntropyLoss.
Differences
PyTorch: Calculate the cross-entropy loss between the predicted and target values.
MindSpore: MindSpore implements the same function as PyTorch, and the target value supports two different data forms: class index and class probabilistic.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter 1 |
weight |
weight |
- |
Parameter 2 |
size_average |
- |
PyTorch has deprecated this parameter, function taken over by reduction |
|
Parameter 3 |
ignore_index |
ignore_index |
- |
|
Parameter 4 |
reduce |
- |
PyTorch has deprecated this parameter, function taken over by reduction |
|
Parameter 5 |
reduction |
reduction |
- |
|
Parameter 6 |
- |
label_smoothing |
Label smoothing value, used as a regularization means to prevent overfitting of the model when calculating Loss. The range of values is [0.0, 1.0]. Default value: 0.0. |
|
Inputs |
Input 1 |
input |
logits |
Same function, different parameter names |
Input 2 |
target |
labels |
Same function, different parameter names |
Code Example
Both PyTorch and MindSpore support the case where the target value is class index.
# PyTorch
import torch
import numpy as np
input_torch = np.array([[1.624, -0.611, -0.528, -1.072, 0.865], [-2.301, 1.744, -0.761, 0.319, -0.249], [1.462, -2.060, -0.322, -0.384, 1.133]])
target_torch = np.array([1, 0, 4])
loss = torch.nn.CrossEntropyLoss()
input_torch = torch.tensor(input_torch, requires_grad=True)
target_torch = torch.tensor(target_torch, dtype=torch.long)
output = loss(input_torch, target_torch)
print(round(float(output.detach().numpy()), 3))
# 2.764
# MindSpore
import mindspore
import numpy as np
input_ms = np.array([[1.624, -0.611, -0.528, -1.072, 0.865], [-2.301, 1.744, -0.761, 0.319, -0.249], [1.462, -2.060, -0.322, -0.384, 1.133]])
target_ms = np.array([1, 0, 4])
input_ms = mindspore.Tensor(input_ms, mindspore.float32)
target_ms = mindspore.Tensor(target_ms, mindspore.int32)
loss = mindspore.nn.CrossEntropyLoss()
output = loss(input_ms, target_ms)
print(round(float(output), 3))
# 2.764