Tanh and Softsign in PyTorch

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*Memos:

• My post explains Tanh, Softsign, Sigmoid and Softmax.
• My post explains heaviside() and Identity().
• My post explains ReLU() and LeakyReLU().
• 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 Sigmoid() and Softmax().

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

• The 1st argument is input(Required-Type:tensor of int, float, complex or bool). *A float tensor is returned except for a complex input tensor.
• You can also use torch.tanh() with a tensor.

import torch
from torch import nn

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

tanh = nn.Tanh()
tanh(input=my_tensor)
my_tensor.tanh()
# tensor([1.0000, -0.9951, 0.0000, 0.7616, 0.9999, -0.9640, -0.7616, 0.9993])

tanh
# Tanh()

my_tensor = torch.tensor([[8., -3., 0., 1.],
                          [5., -2., -1., 4.]])
tanh = nn.Tanh()
tanh(input=my_tensor)
# tensor([[1.0000, -0.9951, 0.0000, 0.7616],
#         [0.9999, -0.9640, -0.7616, 0.9993]])

my_tensor = torch.tensor([[[8., -3.], [0., 1.]],
                          [[5., -2.], [-1., 4.]]])
tanh = nn.Tanh()
tanh(input=my_tensor)
# tensor([[[1.0000, -0.9951], [0.0000, 0.7616]],
#         [[0.9999, -0.9640], [-0.7616, 0.9993]]])

my_tensor = torch.tensor([[[8, -3], [0, 1]],
                          [[5, -2], [-1, 4]]])
tanh = nn.Tanh()
tanh(input=my_tensor)
# tensor([[[1.0000, -0.9951], [0.0000, 0.7616]],
#         [[0.9999, -0.9640], [-0.7616, 0.9993]]])

my_tensor = torch.tensor([[[8.+0.j, -3.+0.j], [0.+0.j, 1.+0.j]],
                          [[5.+0.j, -2.+0.j], [-1.+0.j, 4.+0.j]]])
tanh = nn.Tanh()
tanh(input=my_tensor)
# tensor([[[1.0000+0.j, -0.9951+0.j], [0.0000+0.j, 0.7616+0.j]],
#         [[0.9999+0.j, -0.9640+0.j], [-0.7616+0.j, 0.9993+0.j]]])

my_tensor = torch.tensor([[[True, False], [True, False]],
                          [[False, True], [False, True]]])
tanh = nn.Tanh()
tanh(input=my_tensor)
# tensor([[[0.7616, 0.0000], [0.7616, 0.0000]],
#         [[0.0000, 0.7616], [0.0000, 0.7616]]])

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

• The 1st argument is input(Required-Type:tensor of int, float or complex). *A float tensor is returned except for a complex input tensor.

import torch
from torch import nn

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

softsign = nn.Softsign()
softsign(input=my_tensor)
# tensor([0.8889, -0.7500, 0.0000, 0.5000, 0.8333, -0.6667, -0.5000, 0.8000])

softsign
# Softsign()

my_tensor = torch.tensor([[8., -3., 0., 1.],
                          [5., -2., -1., 4.]])
softsign = nn.Softsign()
softsign(input=my_tensor)
# tensor([[0.8889, -0.7500, 0.0000, 0.5000],
#         [0.8333, -0.6667, -0.5000, 0.8000]])

my_tensor = torch.tensor([[[8., -3.], [0., 1.]],
                          [[5., -2.], [-1., 4.]]])
softsign = nn.Softsign()
softsign(input=my_tensor)
# tensor([[[0.8889, -0.7500], [0.0000, 0.5000]],
#         [[0.8333, -0.6667], [-0.5000, 0.8000]]])

my_tensor = torch.tensor([[[8, -3], [0, 1]],
                          [[5, -2], [-1, 4]]])
softsign = nn.Softsign()
softsign(input=my_tensor)
# tensor([[[0.8889, -0.7500], [0.0000, 0.5000]],
#         [[0.8333, -0.6667], [-0.5000, 0.8000]]])

my_tensor = torch.tensor([[[8.+0.j, -3.+0.j], [0.+0.j, 1.+0.j]],
                          [[5.+0.j, -2.+0.j], [-1.+0.j, 4.+0.j]]])
softsign = nn.Softsign()
softsign(input=my_tensor)
# tensor([[[0.8889+0.j, -0.7500+0.j], [0.0000+0.j, 0.5000+0.j]],
#         [[0.8333+0.j, -0.6667+0.j], [-0.5000+0.j, 0.8000+0.j]]])