mxnet.gluon.contrib.estimator.event_handler

Gluon EventHandlers for Estimators

Classes

BatchBegin()

BatchEnd()

CheckpointHandler(model_dir[, model_prefix, ...])

Save the model after user define period

EarlyStoppingHandler(monitor[, min_delta, ...])

Early stop training if monitored value is not improving

EpochBegin()

EpochEnd()

EventHandler()

GradientUpdateHandler([priority])

Gradient Update Handler that apply gradients on network weights

LoggingHandler([log_interval, metrics, priority])

Basic Logging Handler that applies to every Gluon estimator by default.

MetricHandler(metrics[, priority])

Metric Handler that update metric values at batch end

StoppingHandler([max_epoch, max_batch])

Stop conditions to stop training Stop training if maximum number of batches or epochs reached.

TrainBegin()

TrainEnd()

ValidationHandler(val_data, eval_fn[, ...])

Validation Handler that evaluate model on validation dataset
class mxnet.gluon.contrib.estimator.event_handler.CheckpointHandler(model_dir, model_prefix='model', monitor=None, verbose=0, save_best=False, mode='auto', epoch_period=1, batch_period=None, max_checkpoints=5, resume_from_checkpoint=False)[source]

Bases: TrainBegin, BatchEnd, EpochEnd

Save the model after user define period

CheckpointHandler saves the network architecture after first batch if the model can be fully hybridized, saves model parameters and trainer states after user defined period, default saves every epoch.

Parameters:

  • model_dir (str) – File directory to save all the model related files including model architecture, model parameters, and trainer states.

  • model_prefix (str default 'model') – Prefix to add for all checkpoint file names.

  • monitor (EvalMetric, default None) – The metrics to monitor and determine if model has improved

  • verbose (int, default 0) – Verbosity mode, 1 means inform user every time a checkpoint is saved

  • save_best (bool, default False) – If True, monitor must not be None, CheckpointHandler will save the model parameters and trainer states with the best monitored value.

  • mode (str, default 'auto') – One of {auto, min, max}, if save_best=True, the comparison to make and determine if the monitored value has improved. if ‘auto’ mode, CheckpointHandler will try to use min or max based on the monitored metric name.

  • epoch_period (int, default 1) – Epoch intervals between saving the network. By default, checkpoints are saved every epoch.

  • batch_period (int, default None) – Batch intervals between saving the network. By default, checkpoints are not saved based on the number of batches.

  • max_checkpoints (int, default 5) – Maximum number of checkpoint files to keep in the model_dir, older checkpoints will be removed. Best checkpoint file is not counted.

  • resume_from_checkpoint (bool, default False) – Whether to resume training from checkpoint in model_dir. If True and checkpoints found, CheckpointHandler will load net parameters and trainer states, and train the remaining of epochs and batches.

class mxnet.gluon.contrib.estimator.event_handler.EarlyStoppingHandler(monitor, min_delta=0, patience=0, mode='auto', baseline=None)[source]

Bases: TrainBegin, EpochEnd, TrainEnd

Early stop training if monitored value is not improving

Parameters:

  • monitor (EvalMetric) – The metric to monitor, and stop training if this metric does not improve.

  • min_delta (float, default 0) – Minimal change in monitored value to be considered as an improvement.

  • patience (int, default 0) – Number of epochs to wait for improvement before terminate training.

  • mode (str, default 'auto') – One of {auto, min, max}, if save_best_only=True, the comparison to make and determine if the monitored value has improved. if ‘auto’ mode, checkpoint handler will try to use min or max based on the monitored metric name.

  • baseline (float) – Baseline value to compare the monitored value with.

class mxnet.gluon.contrib.estimator.event_handler.GradientUpdateHandler(priority=-2000)[source]

Bases: BatchEnd

Gradient Update Handler that apply gradients on network weights

GradientUpdateHandler takes the priority level. It updates weight parameters at the end of each batch

Parameters:

priority (scalar, default -2000) – priority level of the gradient update handler. Priority level is sorted in ascending order. The lower the number is, the higher priority level the handler is.

class mxnet.gluon.contrib.estimator.event_handler.LoggingHandler(log_interval='epoch', metrics=None, priority=inf)[source]

Bases: TrainBegin, TrainEnd, EpochBegin, EpochEnd, BatchBegin, BatchEnd

Basic Logging Handler that applies to every Gluon estimator by default.

LoggingHandler logs hyper-parameters, training statistics, and other useful information during training

Parameters:

  • log_interval (int or str, default 'epoch') – Logging interval during training. log_interval=’epoch’: display metrics every epoch log_interval=integer k: display metrics every interval of k batches

  • metrics (list of EvalMetrics) – Metrics to be logged, logged at batch end, epoch end, train end.

  • priority (scalar, default np.inf) – Priority level of the LoggingHandler. Priority level is sorted in ascending order. The lower the number is, the higher priority level the handler is.

class mxnet.gluon.contrib.estimator.event_handler.MetricHandler(metrics, priority=-1000)[source]

Bases: EpochBegin, BatchEnd

Metric Handler that update metric values at batch end

MetricHandler takes model predictions and true labels and update the metrics, it also update metric wrapper for loss with loss values. Validation loss and metrics will be handled by ValidationHandler

Parameters:

  • metrics (List of EvalMetrics) – Metrics to be updated at batch end.

  • priority (scalar) – Priority level of the MetricHandler. Priority level is sorted in ascending order. The lower the number is, the higher priority level the handler is.

class mxnet.gluon.contrib.estimator.event_handler.StoppingHandler(max_epoch=None, max_batch=None)[source]

Bases: TrainBegin, BatchEnd, EpochEnd

Stop conditions to stop training Stop training if maximum number of batches or epochs reached.

Parameters:

  • max_epoch (int, default None) – Number of maximum epochs to train.

  • max_batch (int, default None) – Number of maximum batches to train.

class mxnet.gluon.contrib.estimator.event_handler.ValidationHandler(val_data, eval_fn, epoch_period=1, batch_period=None, priority=-1000, event_handlers=None)[source]

Bases: TrainBegin, BatchEnd, EpochEnd

Validation Handler that evaluate model on validation dataset

ValidationHandler takes validation dataset, an evaluation function, metrics to be evaluated, and how often to run the validation. You can provide custom evaluation function or use the one provided my Estimator

Parameters:

  • val_data (DataLoader) – Validation data set to run evaluation.

  • eval_fn (function) – A function defines how to run evaluation and calculate loss and metrics.

  • epoch_period (int, default 1) – How often to run validation at epoch end, by default ValidationHandler validate every epoch.

  • batch_period (int, default None) – How often to run validation at batch end, by default ValidationHandler does not validate at batch end.

  • priority (scalar, default -1000) – Priority level of the ValidationHandler. Priority level is sorted in ascending order. The lower the number is, the higher priority level the handler is.

  • event_handlers (EventHandler or list of EventHandlers) – List of EventHandler to apply during validaiton. This argument is used by self.eval_fn function in order to process customized event handlers.