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Factors To Consider For Improving Deep Learning Models Performance

January 12, 2022

Deep learning neural networks are becoming easier to define and fit, but they remain challenging to configure. In this article, we will learn about factors to consider that might affect the performance of the deep learning models.

There are no hard-and-fast rules for configuring a network to tackle a specific problem. This is the case when mathematical analysis cannot be used to determine which model type or setup is appropriate for a given dataset.

In the past, deep learning neural network models had to be coded from scratch. As a result, a good accuracy rate might not be possible to achieve, even at its best performance.

Nowadays, when given raw data, a deep learning system can now determine which properties contribute significantly to improving models’ performance, on its own.

You can read more about artificial neural networks here.

Table of contents

Pre-requisites

To follow along with this tutorial, you need to be familiar with the following:

Identifying the problem

The first step towards improving deep learning performance is to figure out what kind of performance issue your system or model is having.

After identifying the problem, carefully pick and assess a specific intervention that is appropriate for the problem.

There are three sorts of concerns that are straightforward to diagnose when it comes to poor deep learning performance:

Model optimization

Poor performance of models is a possible outcome of problems with hyperparameter optimization. Having issues with data optimization is a common occurrence.

Generalization

It can lead to overfitting or poor test set performance. If you want your system to perform better, you’ll need more data. You may want to obtain additional unlabeled data and train your feature extraction sub-model further, depending on your budget.

Model tuning

Tuning the algorithm, which is essentially a prediction problem on the final model, when the final model contains a lot of volatility.

The best way to solve this is to rank the outcomes of all your trials and focus on the top algorithms.

The concept of deep learning

In deep learning, an excessive volume of information i.e. datasets can be trained to gather functional facts from the data to help make predictions.

Classifications and predictions of data are based on responses to a series of binary true or false questions involving highly complicated mathematical calculations while processing the data.

This simply means a deep learning algorithm is designed in such a way that it can easily recognize, predict, and collect or reject information that does not fit (or help in prediction) the model during the training of the dataset.

For example, a facial recognition algorithm learns to identify and recognize face edges and lines first, then more critical parts of faces, and finally overall representations of faces. With time, the algorithm learns and improves, thus improving the chances of getting it right.

In this case, the facial recognition program will eventually recognize faces correctly. Collecting a bigger number of datasets (big data) would lead to better model learning that leads to better accuracy of predictions.

This example demonstrates that a large amount of data is required to accurately predict a deep learning system.

What are optimizers?

In deep learning, optimizers are algorithms or approaches that are used to reduce an error function (loss function) or increase production efficiency.

Optimizers are mathematical functions that are based on the learnable parameters of a model, such as weights and biases. They assist in determining how to alter the weights and learning rate of a neural network to minimize losses.

A loss function of a system calculates the difference between the current results and the expected results of the algorithm.

Determinants for deep learning system performance

Current infrastructure

Architecture

In such a problem, the engineer is faced with challenges like:

  • The size of the system that is about to be developed.
  • The number of datasets required to create the required system
  • How does the size of the system affect the performance of the system?

In such a problem, the engineer is faced with challenges like picking the best optimizer for minimizing the losses that may produce accurate results.

Model adaptability

In such a problem, the engineer is faced with challenges like:

  • Will the developed system adapt to further changes in the future without affecting the performance of the system?

Difficulties with default hyperparameters

Hyperparameters are values that control the learning process and define which model parameters the algorithm learns with. They are “top-level” model parameters that regulate the learning process.

You can read more about the common problems faced with hyperparameter optimization here.

Technical experience

Deep learning professionals are in high demand and are highly recommended in this field. To determine the learning rate and improve the system’s performance, experience is required.

Choice of optimizers

If the optimizer that we picked to train your system isn’t a perfect match, such a system will encounter errors such as overfitting, underfitting, loss gradient descent, and so on, thereby not achieving an accurate output.

How to get a perfect performance rate?

Data optimization

Having more depth of knowledge about a model is the best savage for accuracy in deep learning. Allow the additional facts to speak for themselves rather than relying on assumptions and questionable relationships. Models that have more data are more accurate and better.

Working with smaller datasets will be more straightforward. As a result, the training set’s overfitting is decreased.

Algorithm tuning

Parameters are known to drive deep learning algorithms. The result of the learning process is heavily influenced by these variables.

Parameter tuning is the process of determining the best value for each parameter in order to increase the model’s accuracy. To fine-tune these factors, you must first grasp their meaning and how they affect the model individually.

This technique can be repeated with a variety of successful system models. It’s impossible to predict which deep learning algorithm will suit your system the best.

Rank the outcomes of all your trials and focus your efforts on the best-performing algorithms.

Hyperparameter optimization

The key challenge with fine-tuning the hyperparameters of a neural network depends on the following factors:

Learning rate

The learning rate is a hyper-parameter that determines the weights adjusted with respect to the expected error each time the system weights are updated (loss gradient).

According to research, the Adam optimizer delivers the greatest and highest performance rate for large systems with enormous datasets.

Despite its excellent performance, it may nonetheless trap you in a local minimum that is specific to your problem. Therefore, the choice of learning rate must be determined only by experimentation.

Epochs and batch size

The batch size refers to the number of samples that must be processed before the model is updated. The number of epochs refers to the total number of times the training dataset has been traversed to train a model.

Experiment with batch sizes and training epochs to see what works best for the model.

Early-stopping

The difference between the empirical loss of the training set and the expected loss of the test set is the Generalization error of a deep learning system/model.

Generalization errors are positively affected in a session. Continuous training may improve accuracy on your data set, but it eventually reduces the model’s performance on data it hasn’t seen. Early halting can help with real-world performance.

You can read more early-stopping of model training here.

Regularization

Regularization is a reliable strategy for avoiding overfitting.

The following techniques can be used for regularization:

  • Switching off a percentage of neurons at random during training, which is known as a dropout. Dropout is a technique for preventing groups of neurons from overfitting one another.
  • The weight penalty is a technique for reducing the overfitting of a deep learning model on training data while improving the model’s performance on the new data.

In deep learning, weights that expand in size can be a major concern for degrading accuracy. Adding all of our parameters (weights) to our loss function is one method to penalize complexity.

The problem can be remedied by making changes to the learning algorithm to encourage the network to keep its weights low. This is known as weight regularization and is regarded as a general method for reducing training dataset overfitting and increasing model generalization.

You read about different methods on how to penalize and control larger weights here.

Ensemble Methods

This method essentially aggregates the output of several poor models to obtain superior results.

This can be accomplished with:

  • Bagging (Bootstrap aggregating)
  • Boosting

A common strategy for decreasing variation in a noisy dataset is Bagging.

The practice of picking and replacing a random sample of data from a training batch is known as bagging. As a result, each data point can be chosen multiple times.

Boosting is an ensemble learning approach that combines a collection of weak learners into strong learners to reduce training errors. A random sample of data is picked, a model is fitted, and the models are then trained progressively, with each model aiming to correct for the flaws of the one before it.

To improve the accuracy of your model, it’s always a good idea to use ensemble approaches for 2 main reasons:

  1. They are often more complicated than traditional procedures.
  2. Traditional approaches provide a wonderful starting point from which you can improve and gain inspiration for your ensembles.

You can read more about Ensemble methods here.

Configuring the learning rate

Display Rate Charts

According to Leslie N. Smith, a good learning rate can be estimated by beginning with a very low learning rate and gradually increasing it (either linearly or exponentially) with each iteration.

Note: Less training time, lesser money spent on GPU cloud computation.

Iteration chart

If we plot the learning rate (log) versus loss for each learning iteration, we will notice that as the learning rate increases, the loss stops falling and begins to climb.

In practice, our learning rate should ideally be near the bottom of the line, to the left (as demonstrated in the below graph). 0.001 to 0.01 in this situation.

Learning Rate Chart

Conclusion

With the vast and rapid development of technology, deep learning is the key to today’s smart world. This has brought ease in communication between humans and their daily environment.

Some real-life postulates are cited to help design the deep network algorithm. These factors include:

  • Architecture
  • Choice of hyperparameters
  • Technical know-how
  • Choice of optimizers

There are some steps to aim at to achieve a perfect performance rate in a deep learning system:

  • Data optimization.
  • Algorithm tuning.
  • Hyperparameters optimization
  • Ensemble methods

Further reading


Peer Review Contributions by: Srishilesh P S