torch.nn.Dropout() Method
In PyTorch, torch.nn.Dropout() method randomly replaced some of the elements of an input tensor by 0 with a given probability. This method only supports the non-complex-valued inputs. before moving further let’s see the syntax of the given method.
Syntax: torch.nn.Dropout(p=0.5, inplace=False)
Parameters:
- P: P is the probability of an element is replaced with 0 or not. Default value of P is 0.5
- inplace: This will used to do this operation in-place. Default value of inplace is False.
Return: This method return a tensor after replaced some of the elements of input tensor by 0 with a given probability P.
Example 1:
In this example, we will use torch.nn.Dropout() method with probability 0.35. It means there is a 35% chance of an element of input tensor to be replaced with 0.
Python
# Import required libraryimport torch# define a tensortens = torch.tensor([-0.7345, 0.4347, -0.1237, 1.3379, 0.2343])# print the tensorprint("Original tensor:", tens)# use torch.nn.Dropout() method with # probability p=0.35drop = torch.nn.Dropout(.35)Output_tens = drop(tens)# Display Outputprint(" Output Tensor:", Output_tens) |
Output:
Example 2:
In this example, we will use torch.nn.Dropout() method with probability is 0.85 and in place is True. It means there is an 85% chance of an element of input tensor to be replaced with 0.
Python
# Import the required libraryimport torch# define a tensortens = torch.tensor([[-0.1345, -0.7437, 1.2377], [0.9337, 1.6473, 0.4346], [-0.6345, 0.9344, -0.2456]])# print the tensorprint("\n Original tensor: \n", tens)# use torch.nn.Dropout() method with # probability p=0.85# perform this operation in-place by# using inplace=Truedrop = torch.nn.Dropout(.85)Output_tens = drop(tens)# Display Tensorprint("\n Output Tensor: \n", Output_tens) |
Output:
How to Use torch.nn.Dropout() Method in Python PyTorch
In this article, we are going to discuss how you use torch.nn.Dropout() Method in Python PyTorch.