Differences with torch.nn.ReLU
torch.nn.ReLU
class torch.nn.ReLU(inplace=False)(input) -> Tensor
For more information, see torch.nn.ReLU.
mindspore.nn.ReLU
class mindspore.nn.ReLU()(x) -> Tensor
For more information, see mindspore.nn.ReLU.
Differences
PyTorch: ReLU activation function.
MindSpore: MindSpore implements the same function as PyTorch, but with different parameter settings.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameter |
Parameter 1 |
inplace |
- |
Whether to execute in-place, default: False. MindSpore does not have this parameter. |
Input |
Single input |
input |
x |
Same function, different parameter names |
Code Example
The two APIs achieve the same function and have the same usage.
# PyTorch
import torch
from torch import tensor
from torch import nn
import numpy as np
x = tensor(np.array([[0.1, -0.6], [-0.9, 0.8]]), dtype=torch.float32)
m = nn.ReLU()
out = m(x)
output = out.detach().numpy()
print(output)
# [[0.1 0. ]
# [0. 0.8]]
# MindSpore
import mindspore
import mindspore.nn as nn
from mindspore import Tensor
import numpy as np
x = Tensor(np.array([[0.1, -0.6], [-0.9, 0.8]]), dtype=mindspore.float32)
relu = nn.ReLU()
output = relu(x)
print(output)
# [[0.1 0. ]
# [0. 0.8]]