Executor

Inheritance Diagram

---

class ashpy.losses.executor.Executor(fn=None)

Bases: object

Carry a function and the way of executing it. Given a context.

Methods

__init__([fn])	Initialize the Executor.
call(context, **kwargs)	Execute the function, using the information provided by the context.
reduce_loss(call_fn)	Create a Decorator to reduce Losses.

Attributes

fn	Return the Keras loss function to execute.
global_batch_size	Global batch size comprises the batch size for each cpu.
weight	Return the loss weight.

__init__(fn=None)

Initialize the Executor.

Parameters:fn (tf.keras.losses.Loss) – A Keras Loss to execute. Return type:None Returns:None

call(context, **kwargs)

Execute the function, using the information provided by the context.

Parameters:context (ashpy.contexts.Context) – The function execution Context. Return type:Tensor Returns:tf.Tensor – Output Tensor.

fn

Return the Keras loss function to execute.

Return type:Loss Returns:tf.keras.losses.Loss – Keras Loss.

global_batch_size

Global batch size comprises the batch size for each cpu.

Calculated as batch_size_for_replica*replica_numbers.

Return type:int Returns:int – Global Batch size value.

static reduce_loss(call_fn)

Create a Decorator to reduce Losses. Used to simplify things.

Apply a reduce sum operation to the loss and divide the result by the batch size.

Parameters:call_fn (typing.Callable) – The executor call method. Return type:Callable Returns:typing.Callable – The decorated function.

weight

Return the loss weight.

This weight is multiplied by the loss value. This is useful when working with multiples losses.

Return type:Callable[…, float] Returns:typing.Callable – Callable returning the weight (float).