Name: ReLU and LeakyReLU in PyTorch
Rating: 3.4 (9141 reviews)
Author: hyperkai

*Memos:

My post explains Step function, Identity and ReLU.
My post explains Leaky ReLU, PReLU and FReLU.
My post explains heaviside() and Identity().
My post explains PReLU() and ELU().
My post explains SELU() and CELU().
My post explains GELU() and Mish().
My post explains SiLU() and Softplus().
My post explains Tanh() and Softsign().
My post explains Sigmoid() and Softmax().

ReLU() can get the 0D or more D tensor of the zero or more values computed by ReLU function from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

The 1st argument for initialization is inplace(Optional-Default:False-Type:bool): *Memos:
- It does in-place operation.
- Keep it False because it's problematic with True.
The 1st argument is input(Required-Type:tensor of int or float).
You can also use relu() with a tensor.

import torch
from torch import nn

my_tensor = torch.tensor([8, -3, 0, 1, 5, -2, -1, 4])

relu = nn.ReLU()
relu(input=my_tensor)
my_tensor.relu()
# tensor([8, 0, 0, 1, 5, 0, 0, 4])

relu
# ReLU()

relu.inplace
# False

relu = nn.ReLU(inplace=True)
relu(input=my_tensor)
# tensor([8, 0, 0, 1, 5, 0, 0, 4])

my_tensor = torch.tensor([[8, -3, 0, 1],
                          [5, 0, -1, 4]])
relu = nn.ReLU()
relu(input=my_tensor)
# tensor([[8, 0, 0, 1],
#         [5, 0, 0, 4]])

my_tensor = torch.tensor([[[8, -3], [0, 1]],
                          [[5, 0], [-1, 4]]])
relu = nn.ReLU()
relu(input=my_tensor)
# tensor([[[8, 0], [0, 1]],
#         [[5, 0], [0, 4]]])

my_tensor = torch.tensor([[[8., -3.], [0., 1.]],
                          [[5., 0.], [-1., 4.]]])
relu = nn.ReLU()
relu(input=my_tensor)
# tensor([[[8., 0.], [0., 1.]],
#         [[5., 0.], [0., 4.]]])

LeakyReLU() can get the 0D or more D tensor of the zero or more values computed by LeakyReLU function from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

The 1st argument for initialization is negative_slope(Optional-Default:0.01-Type:float). *It's applied to negative input values.
The 2nd argument for initialization is inplace(Optional-Default:False-Type:bool): *Memos:
- It does in-place operation.
- Keep it False because it's problematic with True.
The 1st argument is input(Required-Type:tensor of float).

import torch
from torch import nn

my_tensor = torch.tensor([8., -3., 0., 1., 5., -2., -1., 4.])

lrelu = nn.LeakyReLU()
lrelu(input=my_tensor)
# tensor([8.0000, -0.0300, 0.0000, 1.0000, 5.0000, -0.0200, -0.0100, 4.0000])

lrelu
# LeakyReLU(negative_slope=0.01)

lrelu.negative_slope
# 0.01

lrelu.inplace
# False

lrelu = nn.LeakyReLU(negative_slope=0.01, inplace=True)
lrelu(input=my_tensor)
# tensor([8.0000, -0.0300, 0.0000, 1.0000, 5.0000, -0.0200, -0.0100, 4.0000])

my_tensor = torch.tensor([[8., -3., 0., 1.],
                          [5., -2., -1., 4.]])
lrelu = nn.LeakyReLU()
lrelu(input=my_tensor)
# tensor([[8.0000, -0.0300, 0.0000, 1.0000],
#         [5.0000, -0.0200, -0.0100, 4.0000]])

my_tensor = torch.tensor([[[8., -3.], [0., 1.]],
                          [[5., -2.], [-1., 4.]]])
lrelu = nn.LeakyReLU()
lrelu(input=my_tensor)
# tensor([[[8.0000, -0.0300], [0.0000, 1.0000]],
#         [[5.0000, -0.0200], [-0.0100, 4.0000]]])