Features

This page gives a run-down of many features FluxTraining . jl brings to the table. Most features are implemented as callbacks and using them is as simple as passing the callback when constructing a Learner and training with fit! :

			
			
			
			cb
			
			 
			
			=
			
			 
			
			
			CoolFeature🕶️Callback
			
			(
			
			)
			
			

			
			
			learner
			
			 
			
			=
			
			 
			
			
	
		
			Learner
			
			(
			
			model
			
			,
			
			 
			
			lossfn
			
			
			;
			
			 
			
			
			callbacks
			
			=
			
			
			[
			
			cb
			
			]
			
			,
			
			 
			
			
			data
			
			=
			
			
			(
			
			trainiter
			
			,
			
			 
			
			validiter
			
			)
			
			)
			
			

			
			
	
		
			fit!
			
			(
			
			learner
			
			,
			
			 
			
			nepochs
			
			)

Metrics

By default, Learner will track only the loss function. You can track other metric with the Metrics callback. See also Metric , AbstractMetric .

Hyperparameter scheduling

The Scheduler callback takes care of hyperparameter scheduling. See the Hyperparameter scheduling tutorial and also Scheduler , Schedule , HyperParameter .

Logging

For logging, use the logging callbacks:

• LogMetrics

• LogHyperParams

• LogHistograms

They each can have multiple logging backends, but right now the only one implemented in FluxTraining . jl is TensorBoardBackend . See also LoggerBackend , log_to , and Loggables.Loggable .

There is also an external package Wandb . jl that implements a logging backend for Weights & Biases .

Checkpointing

Use the Checkpointer callback to create model checkpoints after every epoch.

Early Stopping

Use EarlyStopping to stop when a stopping criterion is met. Supports all criteria in EarlyStopping . jl .