# AshPy Internals¶

The two main concepts of AshPy internals are Context and Executor.

## Context¶

A Context is an object that contains all the needed information. Here needed depends on the application. In AshPy the Context concept links a generic training loop with the loss function calculation and the model evaluation. A Context is a useful class in which all the models, metrics, dataset and mode of your network are set. Passing the context around means that you can any time access to all what you need in order to perform any type of computation.

In AshPy we have (until now) three types of contexts:

### Classifier Context¶

The ClassifierContext is very simple, it contains only:

• classifier_model
• loss
• dataset
• metrics
• log_eval_mode
• global_step
• ckpt

In this way the loss function (Executor) can use the context in order to get the model and the needed information in order to correctly feed the model.

### GAN Context¶

The basic GANContext is composed by:

• dataset
• generator_model
• discriminator_model
• generator_loss
• discriminator_loss
• metrics
• log_eval_mode
• global_step
• ckpt

As we can see we have all information needed to define our training and evaluation loop.

### GANEncoder Context¶

The GANEncoderContext extends the GANContext, contains all the information of the base class plus:

• Encoder Model
• Encoder Loss

## Executor¶

The Executor is the main concept behind the loss function implementation in AshPy. An Executor is a class that helps in order to better generalize a training loop. With an Executor you can construct, for example, a custom loss function and put every computation you need inside it. You should define a call function inside your class and decorate it with @Executor.reduce header, if needed.

Inside the call function you can take advantage of a context.

Executors can be summed up, subtracted and multiplied by scalars.

An executor takes also care of the distribution strategy by reducing appropriately the loss (see Tensorflow Guide).

In this example we will see the implementation of the Generator Binary CrossEntropy loss.

The __init__ method is straightforward, we need only to instantiate tf.losses.BinaryCrossentropy object and then we pass it to our parent:

class GeneratorBCE(GANExecutor):

def __init__(self, from_logits=True):
self.name = "GeneratorBCE"
# call super passing the BinaryCrossentropy as function
super().__init__(tf.losses.BinaryCrossentropy(from_logits=from_logits))


Then we need to implement the call function respecting the signature:

def call(self, context, *, fake, condition, training, **kwargs):

# we need a function that gives us the correct inputs given the discriminator model
fake_inputs = self.get_discriminator_inputs(
context=context, fake_or_real=fake, condition=condition, training=training
)

# get the discriminator predictions from the discriminator model
d_fake = context.discriminator_model(fake_inputs, training=training)

# get the target prediction for the generator
value = self._fn(tf.ones_like(d_fake), d_fake)

# mean everything
return tf.reduce_mean(value)


The function get_discriminator_inputs() returns the correct discriminator inputs using the context. The discriminator input can be the output of the generator (unconditioned case) or the output of the generator together with the condition (conditioned case).

The the call() uses the discriminator model inside the context in order to obtain the output of the discriminator when evaluated in the fake_inputs.

After that the self._fn() (BinaryCrossentropy) is used to get the value of the loss. This loss is then averaged.

In this way the executor computes correctly the loss function.

This is ok if we do not want use our code in a distribution strategy.

If we want to use our executor in a distribution strategy the only modifications are:

@Executor.reduce_loss
def call(self, context, *, fake, condition, training, **kwargs):

# we need a function that gives us the correct inputs given the discriminator model
fake_inputs = self.get_discriminator_inputs(
context=context, fake_or_real=fake, condition=condition, training=training
)

# get the discriminator predictions from the discriminator model
d_fake = context.discriminator_model(fake_inputs, training=training)

# get the target prediction for the generator
value = self._fn(tf.ones_like(d_fake), d_fake)

# mean only over the axis 1
return tf.reduce_mean(value, axis=1)


The important things are:

• Executor.reduce_loss decoration: uses the Executor decorator in order to correctly reduce the loss
• tf.reduce_mean(value, axis=1) (last line), we perform only the mean over the axis 1. The output of the call function

should be a tf.Tensor with shape (N, 1) or (N,). This is because the decorator performs the mean over the axis 0.