In this article, we are going to discuss how you use torch.nn.Dropout() Method in Python PyTorch.
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:
