Differences with torch.nn.PReLU
torch.nn.PReLU
class torch.nn.PReLU(num_parameters=1, init=0.25)(input) -> Tensor
For more information, see torch.nn.PReLU.
mindspore.nn.PReLU
class mindspore.nn.PReLU(channel=1, w=0.25)(x) -> Tensor
For more information, see mindspore.nn.PReLU.
Differences
PyTorch: PReLU activation function.
MindSpore: MindSpore implements the same function as PyTorch, but with different parameter names.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter 1 |
num_parameters |
channel |
Same function, different parameter names |
Parameter 2 |
init |
w |
Same function, different parameter names |
|
Input |
Single input |
input |
x |
Same function, different parameter names |
Code Example 1
This function is the same for both APIs, same usage and same default value. Only the parameter names are different.
# 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, 0.9]]), dtype=torch.float32)
m = nn.PReLU()
out = m(x)
output = out.detach().numpy()
print(output)
# [[ 0.1 -0.15 ]
# [-0.225 0.9 ]]
# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.nn as nn
import numpy as np
x = Tensor(np.array([[0.1, -0.6], [-0.9, 0.9]]), mindspore.float32)
prelu = nn.PReLU()
output = prelu(x)
print(output)
# [[ 0.1 -0.15 ]
# [-0.225 0.9 ]]
Code Example 2
If do not use the default value, you can use MindSpore to achieve the same function by simply setting the corresponding parameter to an equal number.
# PyTorch
import torch
from torch import tensor
from torch import nn
import numpy as np
x = tensor(np.array([[0.1, -0.6], [-0.5, 0.9]]), dtype=torch.float32)
m = nn.PReLU(num_parameters=1, init=0.5)
out = m(x)
output = out.detach().numpy()
print(output)
# [[ 0.1 -0.3 ]
# [-0.25 0.9 ]]
# MindSpore
import mindspore
import mindspore.nn as nn
from mindspore import Tensor
import numpy as np
x = Tensor(np.array([[0.1, -0.6], [-0.5, 0.9]]), mindspore.float32)
prelu = nn.PReLU(channel=1, w=0.5)
output = prelu(x)
print(output)
# [[ 0.1 -0.3 ]
# [-0.25 0.9 ]]