The Definitive Resource for Deep Learning Regularisation Methods

Training samples and labels provide a machine learning model with the ability to recognise patterns in the data and adjust its parameters accordingly. This process is known as training. Once the model has been trained, the parameters or weights can be applied to the testing dataset in order to make predictions of labels or outputs for samples that the model has not been exposed to previously. This form of reasoning is referred to as inference.

Models that fit well vs those that overfit

It can be concluded that the model in question has overfit, meaning that it has effectively learned the patterns present in the training data but is unable to generalise to novel data. A model that is correctly fit is one that accurately captures the patterns in the data supplied in the training dataset and performs well on the testing dataset. If a model is performing well on the training dataset but poorly on the testing dataset, it is likely an overfit model.

In essence, overfitting is a phenomenon that occurs when a machine learning model is able to achieve a high level of accuracy when tested on the data it was trained on, but has a much lower accuracy rate when tested on new, unseen data. This can be indicative of a model that has been over-trained and is not able to generalise the data, meaning that it is unable to accurately predict outcomes for data it has not seen before.

Techniques used to prevent overfitting

When the training accuracy or measure is significantly higher than the validation accuracy or metric, it is an indication of overfitting. To address this issue, there are a number of remedial measures that can be implemented such as: increasing the amount of training data, regularisation, pruning, using cross-validation, and using dropout. All of these methods can help reduce overfitting and improve the generalizability of the model.

  1. To improve pattern recognition, more training data should be used.
  2. Improved model generalisation using supplementary data.
  3. In other words, if the validation metrics are lowering or the loss is growing during model training, you should terminate the training early.
  4. Methods of regularisation

Data augmentation and the acquisition of additional training data are two strategies that can be used to enhance the performance of a model without making any changes to its core design. Instead of attempting to address the issue of overfitting directly, early stopping can be employed to stop the training process at the correct time before the model is prone to overfitting. Regularisation is a more effective approach to avoiding overfitting.

Different Regularisation Methods

Overfitting can be addressed through the application of regularisation, a technique that modifies the training process of a model to make structural changes. The most commonly used regularisation methods are:

  1. Regularisation on the L2 Level
  2. Using L1 regularisation
  3. Statistically-reliable reductions in dropout rates

Each is discussed in depth below.

To regularise L2 values

L2 regularisation, also known as ridge regression, is a technique employed in the field of regression analysis. This type of regularisation modifies the loss or cost function by adding a regularizer term which is the square of the size of the coefficients or weights.

It appears that the loss function has been modified to incorporate a percentage of the sum of the squared weight values, which implies that the weight update process remains consistent when gradient descent is employed on the loss. This suggests that each feature is being accorded a roughly equal amount of weight in the calculation. The following are the implications of this approach:

  • In order to prevent the model from being overly tailored to the training data, a hyperparameter known as Lambda is adjusted to ensure that even features with low predictive power are given a small, yet nonzero, weight.
  • When the weights are all of a similar magnitude and the input characteristics are within the same range, L2 regularisation is most effective in terms of its performance.
  • More complicated patterns may be learned from data using this method, and the model is less likely to be overfit.

Using L1 regularisation

Lasso regression is a type of regularised linear regression technique known as L1 regularisation. In this method, the objective or loss function is augmented with an additional term that reflects the absolute magnitude of the coefficients or weights associated with the model. This helps to reduce the complexity of the model and to avoid overfitting.

By employing L1 regularisation, the overall loss function is adjusted to incorporate a proportional increase based on the magnitude of the weights present. This enables us to effectively eliminate certain coefficients with relatively small values by pushing them closer to zero. When utilising L1 regularisation, the following effects will become evident:

  • L1 regularisation is a technique used to penalise the cost function in a model by taking into account the absolute values of the coefficients associated with the features. This encourages feature selection, which is the process of identifying the most relevant characteristics and discarding the less important ones.
  • In other words, the model will be able to pick up on outliers in the dataset without too much trouble when you use this method.
  • The method will be unable to discern subtle relationships in the data.

Statistically-reliable reductions in dropout rates

In order to obtain more robust characteristics from the model, dropout regularisation is employed, which randomly deactivates part of the neurons during training, thus helping to prevent overfitting.

  • Our neural network is now completely interconnected. If all neurons are exposed to the entirety of the training dataset, some of them may remember the patterns that they observe. However, this can be problematic as it can lead to overfitting, meaning that the model is not proficient at generalising to new data.
  • The use of a sparsely connected neural network implies that only a small selection of neurons will be utilised during the training process. As a consequence, the neurons are encouraged to identify salient features and patterns from the training data, thereby reducing the chances of overfitting occurring.

Characteristics of dropout regularisation include:

  • At each time period, a certain percentage of neurons are randomly disabled throughout all layers of a neural network, a process known as ‘dropout’. This technique of randomly disabling neurons allows for an efficient learning process, as distinct neurons are eliminated at each time period.
  • Dropout is implemented by setting the values of p, which represent the percentage of neurons to be removed.
  • Neuronal dropout strengthens models by reducing their reliance on a small number of central nodes.
  • Dropout is used only during the training stage of model development and is not used at all during the inference phase.
  • In order to reduce the amount of data that the layers can view during inference, the outputs of the layers must be scaled by a factor of p after the training is completed. This is necessary as a result of the dropout procedure, which is implemented to prevent the transmission of all the data to the subsequent layer.

Some of the most widely used regularisation techniques for reducing the risk of overfitting a model are L1 regularisation, L2 regularisation, and dropout. Each of these methods has the potential to improve model performance on test data, and their application should be carefully considered in accordance with the specific dataset and use case in question.

Join the Top 1% of Remote Developers and Designers

Works connects the top 1% of remote developers and designers with the leading brands and startups around the world. We focus on sophisticated, challenging tier-one projects which require highly skilled talent and problem solvers.
seasoned project manager reviewing remote software engineer's progress on software development project, hired from Works blog.join_marketplace.your_wayexperienced remote UI / UX designer working remotely at home while working on UI / UX & product design projects on Works blog.join_marketplace.freelance_jobs