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 library import torch # define a tensor tens = torch.tensor([ - 0.7345 , 0.4347 , - 0.1237 , 1.3379 , 0.2343 ]) # print the tensor print ( "Original tensor:" , tens) # use torch.nn.Dropout() method with # probability p=0.35 drop = torch.nn.Dropout(. 35 ) Output_tens = drop(tens) # Display Output print ( " 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 library import torch # define a tensor tens = torch.tensor([[ - 0.1345 , - 0.7437 , 1.2377 ], [ 0.9337 , 1.6473 , 0.4346 ], [ - 0.6345 , 0.9344 , - 0.2456 ]]) # print the tensor print ( "\n Original tensor: \n" , tens) # use torch.nn.Dropout() method with # probability p=0.85 # perform this operation in-place by # using inplace=True drop = torch.nn.Dropout(. 85 ) Output_tens = drop(tens) # Display Tensor print ( "\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.