*Memos:
- My post explains Step function, Identity and ReLU.
- 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 Tanh() and Softsign().
- My post explains Sigmoid() and Softmax().
heaviside() can get the 0D or more D tensor of the zero or more values computed by Heaviside step function from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
heaviside()can be used with torch or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,floatorbool). - The 2nd argument with
torchor the 1st argument with a tensor isvalues(Required-Type:tensorofint,floatorbool).
import torch from torch import nn my_tensor = torch.tensor([8, -3, 0, 1, 5, -2, -1, 4]) torch.heaviside(input=my_tensor, values=torch.tensor(0)) my_tensor.heaviside(values=torch.tensor(0)) # tensor([1, 0, 0, 1, 1, 0, 0, 1]) torch.heaviside(input=my_tensor, values=torch.tensor([0, 1, 2, 3, 4, 5, 6, 7])) # tensor([1, 0, 2, 1, 1, 0, 0, 1]) my_tensor = torch.tensor([[8, -3, 0, 1], [5, 0, -1, 4]]) torch.heaviside(input=my_tensor, values=torch.tensor(0)) # tensor([[1, 0, 0, 1], # [1, 0, 0, 1]]) torch.heaviside(input=my_tensor, values=torch.tensor([[0, 1, 2, 3], [4, 5, 6, 7]])) # tensor([[1, 0, 2, 1], # [1, 5, 0, 1]]) my_tensor = torch.tensor([[[8, -3], [0, 1]], [[5, 0], [-1, 4]]]) torch.heaviside(input=my_tensor, values=torch.tensor(0)) # tensor([[[1, 0], [0, 1]], # [[1, 0], [0, 1]]]) torch.heaviside(input=my_tensor, values=torch.tensor([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])) # tensor([[[1, 0], [2, 1]], # [[1, 5], [0, 1]]]) my_tensor = torch.tensor([[[8., -3.], [0., 1.]], [[5., 0.], [-1., 4.]]]) torch.heaviside(input=my_tensor, values=torch.tensor([[[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]])) # tensor([[[1., 0.], [2., 1.]], # [[1., 5.], [0., 1.]]]) my_tensor = torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]]) torch.heaviside(input=my_tensor, values=torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]])) # tensor([[[True, False], [True, False]], # [[False, True], [False, True]]]) Identity() can just get the same tensor as the input tensor which is the 0D or more D tensor of zero or more elements as shown below:
*Memos:
- For initialization, you can set 0 or more arguments but there is no influence.
- The 1st argument is
input(Required-Type:tensorofintorfloat).
import torch from torch import nn my_tensor = torch.tensor([8, -3, 0, 1, 5, -2, -1, 4]) identity = nn.Identity() identity(input=my_tensor) # tensor([8, -3, 0, 1, 5, -2, -1, 4]) identity # Identity() identity = nn.Identity(num1=3, num2=5) identity(input=my_tensor) # tensor([8, -3, 0, 1, 5, -2, -1, 4]) my_tensor = torch.tensor([[8, -3, 0, 1], [5, -2, -1, 4]]) identity = nn.Identity() identity(input=my_tensor) # tensor([[8, -3, 0, 1], # [5, -2, -1, 4]]) my_tensor = torch.tensor([[[8, -3], [0, 1]], [[5, -2], [-1, 4]]]) identity = nn.Identity() identity(input=my_tensor) # tensor([[[8, -3], [0, 1]], # [[5, -2], [-1, 4]]]) my_tensor = torch.tensor([[[8., -3.], [0., 1.]], [[5., -2.], [-1., 4.]]]) identity = nn.Identity() identity(input=my_tensor) # tensor([[[8., -3.], [0., 1.]], # [[5., -2.], [-1., 4.]]]) 
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