What is a custom training loop and when would you use one?

Q: What is a custom training loop and when would you use one?

Learn the answer to "What is a custom training loop and when would you use one?" with detailed explanations, code examples, and best practices on DeployU.

The Scenario

You are an ML engineer at a creative AI company. You are working on a new project to generate realistic images of human faces. You have decided to use a Generative Adversarial Network (GAN) for this task.

A GAN consists of two neural networks: a generator and a discriminator. The generator creates new images, and the discriminator tries to distinguish between real images and fake images created by the generator. The two networks are trained in an adversarial process, where the generator tries to fool the discriminator and the discriminator tries to correctly identify the fake images.

This non-standard training procedure cannot be implemented with the built-in fit method in Keras.

The Challenge

Explain how you would write a custom training loop to train a GAN. What are the key steps involved, and how would you use tf.GradientTape to compute the gradients for the generator and the discriminator separately?

Wrong Approach

A junior engineer might try to force the GAN training procedure into the `fit` method, which would not work. They might also not be aware of how to use `tf.GradientTape` to compute the gradients for two separate networks.

Addresses symptoms, not root cause

Right Approach

A senior engineer would know that a custom training loop is the only way to train a GAN. They would be able to explain how to use `tf.GradientTape` to compute the gradients for the generator and the discriminator separately, and they would have a clear plan for how to implement the entire training loop.

Step 1: `fit` vs. Custom Training Loop

Feature	`fit` Method	Custom Training Loop
Flexibility	Limited, only works for standard training procedures.	Very flexible, can be used to implement any kind of training procedure.
Control	High-level, provides limited control over the training process.	Low-level, provides full control over the training process.
Ease of Use	Very easy to use.	More complex to implement.
Use Cases	Standard classification and regression tasks.	GANs, reinforcement learning, and other non-standard training procedures.

Step 2: Define the GAN Components

The first step is to define the generator and discriminator networks, the loss functions, and the optimizers.

import tensorflow as tf

# ... (define generator, discriminator, loss functions, and optimizers) ...

Step 3: Implement the Custom Training Loop

The next step is to implement the custom training loop.

@tf.function
def train_step(images):
    noise = tf.random.normal([BATCH_SIZE, noise_dim])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)

        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)

        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

The key to training a GAN is to use two separate GradientTape blocks to compute the gradients for the generator and the discriminator separately.

Step 4: Run the Training Loop

Finally, we can run the training loop.

def train(dataset, epochs):
    for epoch in range(epochs):
        for image_batch in dataset:
            train_step(image_batch)

By writing a custom training loop, we have full control over the training process and can implement the non-standard training procedure required for a GAN.

Systematic, production-ready debugging

Practice Question

You are training a reinforcement learning agent, which requires a custom training procedure that is not supported by the `fit` method. Which of the following would you use?

Questions