How to avoid overfitting in neural networks

Neural networks are computational models that identify and learn underlying patterns within data and make predictions based on them. These models are inspired by the working of a human brain. A neural network aims to design a network that behaves similarly on the training and testing data. However, the performance of these networks can be diminished because of overfitting and underfitting. These are common occurrences encountered while training a deep neural network. Neural networks aim to learn & generalize the pattern found in the training data so that it can perform similarly on the test data or new data. This was not easy to achieve with the traditional machine learning algorithms. However, the advanced deep learning algorithms provide us with tools that can manage this in a better way.

Understanding bias and variance

Before we get into the details of overfitting and underfitting, let's explore the concepts of bias and variance.

What is overfitting?

When the model tries to learn not only the patterns in the training set but also the noise from it, then we say that the model has overfitted as it will perform poorly on unseen data because of its inability to generalize the patterns in a dataset. Overfitting during training can be spotted when the error on training data decreases to a very small value, but the error on the test data increases to a large value. 

The secret to a successful neural network is generalization. When applied to new, unseen data, overfit models' true performance is challenged, despite the fact that they may show high accuracy on training data. In real-world circumstances, a well-generalized model is more beneficial since it can accurately predict outcomes for data it has never seen.

Reasons for overfitting

Before we learn how to avoid overfitting in neural networks, it’s important to understand some of the common reasons behind overfitting: 

Techniques to avoid overfitting

While overfitting is a common problem in training a neural network, a few ways can help us avoid it. Let's explore some of these effective strategies:

Cross-validation

Cross-validation is a method used to assess the generalizability of a model. This involves partitioning the data into multiple subsets or "folds," rather than relying on a single partition of the data, i.e., test and training sets. After the model is trained with different combinations of these folds, its performance is averaged over all folds. This method provides a more accurate assessment of the model's ability to generalize to new data.

Regularization

Weight regularization is a technique that reduces model overfitting by penalizing high weights in the network. It prevents the model from picking up on the noise in the training data. Two popular regularisation methods are L1 and L2:

• Lasso Regression (L1 regularization):
  In L1 regularization, the penalty term is proportional to the absolute values of the model’s coefficients. This penalty promotes sparsity in the model by decreasing less relevant features toward zero and essentially chooses a subset of features that provide the most contributions to the model's predictive capacity. L1 regularization can be used when the dataset is simple.

  Here,

    $m$: Number of features

    $n$: Number of examples

    $y_i$: Actual target value

    $\hat{y_i}$: Predicted target value

• Ridge Regression (L2 regularization):
  Ridge regression, commonly known as the L2 regularisation, increases the loss function by adding the “squared magnitude” of the coefficient as a penalty term. This penalty encourages smaller weights in the model, effectively reducing the complexity of the model and preventing it from fitting the training data too closely. L2 regularization is a better choice if the data is too complex, as it can identify the underlying patterns in the data. 

  Here,

    $m$: Number of features

    $n$: Number of examples

    $y_i$: Actual target value

    $\hat{y_i}$: Predicted target value

Dropout

Dropout is another effective way to reduce overfitting in a model by deactivating a random subset of neurons at each iteration to bring randomness into the training process. This ensures that the model isn’t highly dependent on any one connection, which encourages more thorough feature learning and lessens overfitting. Many deep learning frameworks implement dropouts as a layer that receives inputs from the previous layer. The dropout layer randomly selects neurons that are not fired to the next layer.  By turning off some neurons, the network performs better on test data.

Early stopping

It is used in training neural networks to prevent overfitting and boost generalization performance. As the model learns from the training data, its performance is monitored. Training is stopped early if the performance on the validation dataset starts to decrease or no longer improves after a certain number of iterations. Early stopping stops the model from overfitting the training data by terminating training when there is little chance of additional improvement on the validation dataset. It aids in finding a balance between making sure the model performs well on training data and adapting effectively to unseen test data.

Data augmentation

It is a technique used in deep neural network training to artificially increase the variety of the training dataset by applying transformations to existing training data. This is particularly useful in scenarios where the training data is limited, or the dataset is unbalanced, i.e., some classes or categories are less represented than others. The goal of data augmentation is to create more training examples that accurately reflect the distribution of the underlying data. For example, consider image-based training data; we can increase the size of the dataset by introducing variations to the training images, such as rotations, scaling, cropping, or brightness and contrast changes. This technique helps the model generalize better to test data and become more robust to real-world noise and variations.

Conclusion

In conclusion, training a neural network that performs well on training and testing datasets is a major challenge because of problems such as overfitting and underfitting. By understanding the tradeoff between bias and variance and using techniques like data augmentation, regularization, and early stopping, we can effectively train robust neural network models that are generalized enough to perform well on unseen data.