In Machine learning, dropout regularization is a technique that randomly drops out a few units in a layer throughout the training process. It is utilized to improve the generalization performance of neural networks when there are complex models with various parameters. PyTorch provides a “torch.nn.Dropout()” to perform this operation on tensors or data.

This guide will explain the method to utilize the “torch.nn.Dropout()” method in PyTorch.

**How to Utilize “torch.nn.Dropout()” Method in PyTorch?**

The “torch.nn.Dropout()” method takes one argument i.e. probability value “p” which is the probability of the element to be zero. This method drops out the specific units of a layer randomly.

The basic syntax is given below:

`torch.nn.Dropout(<probability-value>)`

**Example 1: Utilize “torch.nn.Dropout()” Method With Probability 0.5**

In the first section, we will define a desired tensor and use the “torch.nn.Dropout()” method with a probability of 0.5.

**Step 1: Import PyTorch Library**

First, install the “**torch**” library to use the “torch.nn.Dropout()” method:

`import torch`

**Step 2: Define a Tensor**

Next, define a tensor and display its elements. For example, we are defining the following 1D tensor from the list by utilizing the “torch.tensor()” function:

```
Tens = torch.tensor([0.7945, 0.5321, -0.9874, 1.2740, -0.7201])
print(Tens)
```

This has created the 1D tensor:

**Step 3: Define Dropout Layer**

Now, utilize the “**torch.nn.Dropout()**” method to define the dropout layer and pass the probability as a parameter. Here, we have defined the probability value “**.5**”:

`dropout_ly = torch.nn.Dropout(.5)`

**Step 4: Apply Dropout Layer**

Then, apply the above-defined dropout layer to the desired input tensor:

`output = dropout_ly(Tens)`

**Step 5: Print Output **

Finally, display the output tensor after dropout:

`print("Output Tensor:", output)`

In the below output, the results after dropout can be seen:

**Example 2: Utilize “torch.nn.Dropout()” Method With Probability 0.35**

In the second section, we will define a particular tensor and utilize the “torch.nn.Dropout()” method with a probability of 0.35.

**Step 1: Import PyTorch Library**

First, install the “**torch**” library:

`import torch`

**Step 2: Define a Tensor**

Then, define a particular tensor and display its elements. Here, we are defining the 2D tensor with random values though the “**torch.randn()**” function:

```
Tens2 = torch.randn(2, 3)
print(Tens2)
```

This has created a tensor with random values:

**Step 3: Define Dropout Layer**

Next, define the dropout layer using the “**torch.nn.Dropout()**” method and pass the probability as a parameter. Here, we defined the probability value “**.35**”:

`dropout_ly = torch.nn.Dropout(.35)`

**Step 4: Apply Dropout Layer**

Now, apply the above-defined dropout layer to the input tensor “Tens2”:

`output = dropout_ly(Tens2)`

**Step 5: Print Output **

Finally, display the output tensor after dropout:

`print("Output Tensor: \n", output)`

The below output shows the results after dropout:

We have efficiently explained the method to utilize the “torch.nn.Dropout()” method in PyTorch.

**Note**: Click on the provided link to access our Google Colab Notebook.

**Conclusion**

To utilize the “torch.nn.Dropout()” method in PyTorch, first, import the “torch” library. Next, define a desired tensor and display its elements. After that, use the “**torch.nn.Dropout()**” method to define and apply the dropout layer to the input tensor. Finally, print the output tensor. This article has explained the method to utilize the “torch.nn.Dropout()” method in PyTorch.