18.13. Backpropagation and Training of Neural Networks: Overfitting and Underfitting
Backpropagation is a fundamental algorithm in training neural networks, especially when it comes to deep learning. It is responsible for adjusting the weights of a neural network in order to minimize the difference between the predicted outputs and the expected outputs (training labels). This process is done by calculating the gradient of the loss function with respect to each weight in the network, which allows efficient updating of the weights in the direction that reduces error.
How does backpropagation work?
The backpropagation algorithm works in two main steps: forward propagation (forward pass) and backward propagation (backward pass). During forward propagation, input data is passed through the network to generate an output. In the backward propagation step, the gradient of the loss function is calculated and propagated back through the network, updating the weights as needed.
The loss function, also known as the cost function, measures how well the neural network is performing its task. A common loss function is cross-entropy for classification problems and mean squared error for regression problems. The goal of training is to minimize this loss function.
Training Challenges: Overfitting and Underfitting
During training a neural network, we may encounter two main problems: overfitting and underfitting.
Overfitting
Overfitting occurs when the neural network learns the training data set so well that it becomes unable to generalize to new data. This usually happens when the network has too many parameters (is too complex) relative to the amount of training data available. As a result, the network may capture noise or random patterns that are not representative of the overall process being modeled.
To combat overfitting, several techniques can be applied:
- Regularization: Adds a penalty term to the loss function to discourage large, complex weights in the network.
- Dropout: During training, some neurons are randomly dropped, which helps the network become less sensitive to specific weights.
- Early Stopping: Training stops before the network has a chance to overfit the training data.
- Data Augmentation: Augments the training dataset with modified data, which can help the network learn more generalizable features.
- Cross Validation: Uses different partitions of the dataset to train and validate the model, helping to ensure that the model generalizes well to new data.
Underfitting
On the other hand, underfitting occurs when the neural network is too simple to capture the complexity of the data. This means that the network does not learn even basic patterns from the training data, resulting in poor performance on both the training set and the test set.
To resolve underfitting, we can:
- Increase Network Complexity: Adding more layers or neurons can help the network capture more complex patterns.
- Extend Training Time: Allowing the network to train longer can help it learn patterns in the data better.
- Optimize Hyperparameters: Tuning hyperparameters such as learning rate and batch size can improve the learning process.
- Enrich Training Data: Adding more data or resources can give the network more information to learn from.
Conclusion
Backpropagation is a central piece in training neural networks, allowing them to learn from data efficiently. However, it is crucial to be aware of overfitting and underfitting issues, which can compromise the network's ability to generalize to new data. Through the use of techniques such as regularization, dropout, early stopping, data augmentation and cross-validation, we can mitigate the risk of overfitting. Similarly, to avoid underfitting, we can increase the network complexity, extend the training time, optimize the hyperparameters, and enrich the training data. With these strategies in mind, it is possible to train neural networks that not only fit well to training data, but also maintain high performance on never-before-seen data.
