Building a Custom Trainer for Deep Learning: Step-by-Step Guide

Training is the backbone of developing a machine learning application. It is during the training phase that machine learning engineers experiment with different models, adjust hyperparameters, and fine-tune the architecture to achieve the best results for their problem. In this article, we will delve into building a model trainer for a segmentation example as part of our Deep Learning in Production series.

When it comes to training a machine learning model, the process involves compiling the model, defining the optimizer, loss function, and metrics, and fitting the model to the training data. In our example, we define these components in a Trainer class, which is responsible for orchestrating the training process.

By creating a separate Trainer class, we adhere to the principle of separation of concerns, ensuring that each component of the application has a clear purpose and is maintainable. The Trainer class encapsulates the model, input data, loss function, optimizer, metric, and number of epochs required for training.

To train the model, we implement a custom training loop using TensorFlow, rather than relying solely on high-level APIs like Keras. This approach allows us to have fine-grained control over the training process, enabling us to tune every aspect of the model.

During the training loop, we iterate over the dataset in batches, perform a training step for each batch, update the model weights using backpropagation, and track the loss and accuracy metrics. We also incorporate checkpoints to save the model state periodically, ensuring that we can resume training from a specific point if needed.

Once the training is complete, we save the trained model for future use. Additionally, we utilize Tensorboard to visualize the training metrics, providing a graphical representation of the training process for better understanding and analysis.

In conclusion, building a custom model trainer requires attention to detail, adherence to best practices, and a deep understanding of the underlying principles of machine learning. By following the steps outlined in this article, you can create a robust and efficient training pipeline for your machine learning applications.

If you’re interested in exploring more topics related to training optimization, distributed training, and running training jobs on the cloud, stay tuned for upcoming articles in our Deep Learning in Production series. We are committed to providing practical insights and real-world examples to help you navigate the complexities of deploying machine learning models in production.

Thank you for joining us on this journey, and we look forward to sharing more insights with you in the future. Happy learning!