Model Listeners
Adding hooks and listeners on DL4J models.
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Adding hooks and listeners on DL4J models.
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Listeners allow users to "hook" into certain events in Eclipse Deeplearning4j. This allows you to collect or print information useful for tasks like training. For example, a ScoreIterationListener allows you to print training scores from the output layer of a neural network.
To add one or more listeners to a MultiLayerNetwork or ComputationGraph, use the addListener method:
This TrainingListener implementation provides simple way for model evaluation during training. It can be launched every Xth Iteration/Epoch, depending on frequency and InvocationType constructor arguments
EvaluativeListener
This callback will be invoked after evaluation finished
iterationDone
param iterator Iterator to provide data for evaluation
param frequency Frequency (in number of iterations/epochs according to the invocation type) to perform evaluation
param type Type of value for ‘frequency’ - iteration end, epoch end, etc
Score iteration listener. Reports the score (value of the loss function )of the network during training every N iterations
ScoreIterationListener
param printIterations frequency with which to print scores (i.e., every printIterations parameter updates)
A group of listeners
CollectScoresIterationListener simply stores the model scores internally (along with the iteration) every 1 or N iterations (this is configurable). These scores can then be obtained or exported.
CollectScoresIterationListener
Constructor for collecting scores with default saving frequency of 1
iterationDone
Constructor for collecting scores with the specified frequency.
param frequency Frequency with which to collect/save scores
exportScores
Export the scores in tab-delimited (one per line) UTF-8 format.
exportScores
Export the scores in delimited (one per line) UTF-8 format with the specified delimiter
param outputStream Stream to write to
param delimiter Delimiter to use
exportScores
Export the scores to the specified file in delimited (one per line) UTF-8 format, tab delimited
param file File to write to
exportScores
Export the scores to the specified file in delimited (one per line) UTF-8 format, using the specified delimiter
param file File to write to
param delimiter Delimiter to use for writing scores
CheckpointListener: The goal of this listener is to periodically save a copy of the model during training.. Model saving may be done:
Every N epochs
Every N iterations
Every T time units (every 15 minutes, for example) Or some combination of the 3. Example 1: Saving a checkpoint every 2 epochs, keep all model files
Example 2: Saving a checkpoint every 1000 iterations, but keeping only the last 3 models (all older model files will be automatically deleted)
Example 3: Saving a checkpoint every 15 minutes, keeping the most recent 3 and otherwise every 4th checkpoint file:
Note that you can mix these: for example, to save every epoch and every 15 minutes (independent of last save time): To save every epoch, and every 15 minutes, since the last model save use: Note that is this last example, the sinceLast parameter is true. This means the 15-minute counter will be reset any time a model is saved.
CheckpointListener
List all available checkpoints. A checkpoint is ‘available’ if the file can be loaded. Any checkpoint files that have been automatically deleted (given the configuration) will not be returned here.
return List of checkpoint files that can be loaded
This TrainingListener implementation provides a way to “sleep” during specific Neural Network training phases. Suitable for debugging/testing purposes only.
PLEASE NOTE: All timers treat time values as milliseconds. PLEASE NOTE: Do not use it in production environment.
onEpochStart
In this mode parkNanos() call will be used, to make process really idle
A simple listener that collects scores to a list every N iterations. Can also optionally log the score.
Simple IterationListener that tracks time spend on training per iteration.
PerformanceListener
This method defines, if iteration number should be reported together with other data
param reportIteration
return
An iteration listener that provides details on parameters and gradients at each iteration during traning. Attempts to provide much of the same information as the UI histogram iteration listener, but in a text-based format (for example, when learning on a system accessed via SSH etc). i.e., is intended to aid network tuning and debugging This iteration listener is set up to calculate mean, min, max, and mean absolute value of each type of parameter and gradient in the network at each iteration.
Time Iteration Listener. This listener displays into INFO logs the remaining time in minutes and the date of the end of the process. Remaining time is estimated from the amount of time for training so far, and the total number of iterations specified by the user
TimeIterationListener
Constructor
param iterationCount The global number of iteration for training (all epochs)