In this article from Q2BSTUDIO, a custom software and custom applications development company specialized in artificial intelligence and cybersecurity, we describe how to implement custom training loops in Keras using GradientTape and tf.function
The power of custom training loops lies in the detailed control of the forward and backward phases, enabling advanced optimizations and support for complex architectures such as generative adversarial networks or GANs
To begin, we import TensorFlow with a tf module and configure the optimizer, which can be Adam or any other suitable for our task
We define our training function where, inside tf.function, we wrap the gradient calculation with GradientTape and manually update the weights, ensuring efficiency and traceability of the computation graph
The next step is to iterate over the training data in batches, applying the defined function to obtain loss and accuracy metrics that we adapt to real-world use cases
In the case of GAN training, separate training functions are defined for the generator and discriminator, and updates are alternated to achieve a stable balance, ensuring quality results
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