Function 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 ]]