Where to Add Dropout in Neural Network?

In this blog, we will learn about the concept of ‘dropout’ in the context of neural networks, a crucial term familiar to data scientists and software engineers. Explored as a regularization technique, dropout plays a key role in preventing overfitting, ultimately enhancing the generalization performance of your model. Delving into best practices, we will specifically address the optimal points for integrating dropout within your neural network architecture.

As a data scientist or software engineer, you may have heard of the term “dropout” when it comes to neural networks. Dropout is a regularization technique that can help prevent overfitting in your model, which can result in better generalization performance. However, where exactly should you add dropout in your neural network? In this article, we will discuss the best practices for adding dropout in your neural network.

Table of Contents

  1. What is Dropout?
  2. Where to Add Dropout?
  3. How to Implement Dropout?
  4. Pros and Cons of Dropout
  5. Common Errors and Solutions
  6. Conclusion

What is Dropout?

Before we dive into where to add dropout, let’s first define what dropout is. Dropout is a technique used in deep learning to prevent overfitting. Overfitting occurs when a model is too complex and starts to fit the training data too closely, resulting in poor generalization performance on new, unseen data. Dropout works by randomly dropping out (setting to zero) a percentage of neurons in a layer during training. This forces the remaining neurons to learn more robust features that are not dependent on the presence of any single neuron. During testing, all neurons are active, but their outputs are scaled down by the dropout rate to compensate for the fact that more neurons were active during training.

Where to Add Dropout?

Now that we know what dropout is, the question remains: where should you add dropout in your neural network? The general rule of thumb is to add dropout after the last pooling layer. The reasoning behind this is that pooling layers reduce the spatial size of the feature maps, which reduces the number of parameters in the model and helps prevent overfitting. By adding dropout after the last pooling layer, you can further reduce the risk of overfitting and improve the generalization performance of your model.

However, there are some cases where you may want to add dropout to other layers in your neural network. For example, if you have a very deep neural network, you may want to add dropout after each layer to help prevent overfitting. This can be especially useful in convolutional neural networks (CNNs) where the number of parameters can quickly become large.

Another consideration is the size of your dataset. If you have a small dataset, adding dropout to every layer may not be effective and can actually hurt performance. In this case, it may be better to only add dropout to the last few layers of your network.

How to Implement Dropout?

Implementing dropout in your neural network is relatively straightforward. Most deep learning frameworks, such as TensorFlow and PyTorch, have built-in functions for implementing dropout. In TensorFlow, you can use the tf.keras.layers.Dropout layer to add dropout to your model. In PyTorch, you can use the nn.Dropout module.

Here’s an example of how to add dropout to a CNN in TensorFlow:

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.Dropout(0.5), # Add dropout after flattening layer with the dropout rate of 0.3
    tf.keras.layers.Dense(10, activation='softmax')

In this example, we add dropout after the flattening layer, which comes after the last pooling layer.

The following code shows how to implement dropout in Pytorch

import torch
import torch.nn as nn

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.dropout = nn.Dropout2d(0.3)  # Add dropout with a dropout rate of 0.3
        self.pool = nn.MaxPool2d(2, 2)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.dropout(x)
        x = self.pool(x)
        return x

Pros and Cons of Dropout


  • Helps prevent overfitting.
  • Introduces robustness in the model.
  • Reduces reliance on specific neurons, improving generalization.


  • May slow down training due to the random dropping of neurons.
  • Requires careful tuning of dropout rates for optimal performance.

Common Errors and Solutions

Incompatible Layer Types

  • Error: Attempting to apply dropout in layers that do not support this regularization technique, such as normalization layers.

  • Solution: Refer to the documentation for compatible layer types. Adjust the layer order or exclude dropout from layers that do not support it. Some frameworks provide specific layers integrating dropout.

Incorrect Dropout Rates

  • Error: Setting dropout rates too high can lead to underfitting, while rates too low may not effectively prevent overfitting.

  • Solution: Experiment with moderate dropout rates (e.g., 0.2 or 0.5) initially. Gradually adjust to find the optimal balance between preventing overfitting and preserving valuable information. Techniques like grid search can aid in efficient exploration of dropout rates and other hyperparameters.


In conclusion, dropout is a powerful technique for preventing overfitting in neural networks. The general rule of thumb is to add dropout after the last pooling layer, but depending on the size of your dataset and the complexity of your model, you may want to add dropout to other layers as well. Implementing dropout in your neural network is easy with built-in functions in popular deep learning frameworks. By following these best practices for adding dropout, you can improve the generalization performance of your model and avoid overfitting.

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