Training neural networks can be a challenging task.

We have to learn about all these machine learning models, principles, evaluation, and adjust our Python coding style to how data scientists expect you to import numpy as np and everything else.

But with the right techniques and tools, you can improve the performance and efficiency of your models.

In this blog post, we’ll touch on five intermediate tips that can help you enhance your neural network training using PyTorch and, frankly, make it easier. These tips cover various aspects, from data preprocessing to model architecture and optimization. Let's dive in!

Data Augmentation Techniques:

Data augmentation is a powerful technique that can significantly improve the performance of your neural networks.

When we apply random transformations to the training data, you can increase its diversity and reduce overfitting without even collecting more data!

PyTorch provides several built-in functions, such as rotation, scaling, and flipping, through the torchvision.transforms module, or you can check out the famous albumentations.

Experiment with different augmentation strategies and find the ones that work best for your specific task.

Regularization Methods:

Regularization techniques are essential for preventing overfitting and improving the generalization capabilities of your models.

PyTorch offers various regularization methods, such as L1 and L2 regularization, dropout, and other useful tools to adjust your model.

Incorporating these techniques into your model architecture can help you achieve better performance on unseen data.

Experiment with different regularization techniques and find the optimal balance between regularization strength and model performance.

And be aware, that this will likely “give you a worse number” but improve generalization in the real world.

Learning Rate Scheduling:

Choosing an appropriate learning rate is crucial for successful neural network training.

However, finding the right learning rate can be challenging. Learning rate scheduling is a technique that dynamically adjusts the learning rate during training to achieve faster convergence and better model performance.

PyTorch provides various scheduling options, such as ReduceLROnPlateau, and CosineAnnealingLR.

This ensures that the learning rate starts on a larger value to get into the right neighbourhood of your loss surface. Then it reduces the learning rate to converge onto that actual minimum.

ReduceLROnPlateau basically looks at the validation loss and reduces the learning rate when the loss does not improve anymore.

CosineAnnealingLR is a classic schedule that follows a cosine trajectory over the training time.

Explore these scheduling techniques and experiment with different settings to find the optimal learning rate schedule for your model.

Model Initialization:

Proper initialization of neural network weights can have a significant impact on training performance.

This may be more of an advanced intermediate technique, though…

Basically, when we say that our weights of the network are initialized randomly, we are lying a bit.

It’s random, but a specific conditioned flavour of random.

PyTorch provides different weight initialization methods, such as Xavier and He initialization. These can help alleviate the vanishing or exploding gradient problems. Experiment with different initialization techniques and evaluate their impact on model convergence and performance.

Additionally, consider using pre-trained models as initial weights for transfer learning tasks, as they can provide a good starting point for training on new datasets!

Monitoring and Visualizing Training Progress

Our machine learning experiments include a lot of iteration!

To gain insights into your model's behaviour and performance during training, it's crucial to monitor and visualize relevant metrics.

PyTorch offers tools like TensorBoard and the PyTorch Lightning framework, which allow you to log and visualize training progress, metrics, and even model architectures. Monitoring training loss, validation accuracy, and other metrics can help you identify issues early on and make informed decisions to improve your models.

But I’ve also grown to really like tools like Weights and Biases.

And a free bonus: if you start using config files early, you’ll have a much easier time figuring out how you trained a model!

Conclusion

Training neural networks with PyTorch is a rewarding experience when armed with the right knowledge and techniques.

Leverage data augmentation, regularization methods, learning rate scheduling, proper model initialization, and monitoring tools, and you can enhance the training process and achieve better performance and generalisation, while keeping your sanity.

Remember to experiment, iterate, and adapt these tips to your specific tasks and datasets.

Happy training!

PS: If you’d like a total of 100 tips, I have a video for you!