Torchtrainer - A training framework for pytorch

Features

• Train/validate models for given number of epochs
• Hooks/Callbacks to add personalized behavior
• Different metrics of model accuracy/error
• Training/validation statistics monitors
• Cross fold validation iterators for splitting validation data from train data

Trainers

• BatchTrainer: Abstract class for all trainers that works with batched inputs
• SupervisedTrainer: Training for supervised tasks
• AutoencoderTrainer: Trainer for auto encoder tasks

Callbacks

• callbacks.Callback: Base callback class for all epoch/training events
• callbacks.History: Callback that record history of all training/validation metrics
• callbacks.Logger: Callback that display metrics per logging step
• callbacks.ProgbarLogger: Callback that displays progress bars to monitor training/validation metrics
• callbacks.CallbackContainer: Callback to group multiple hooks
• callbacks.ModelCheckpoint: Callback to save best model after every epoch
• callbacks.EarlyStopping: Callback to stop training when monitored quanity not improves
• callbacks.CSVLogger: Callback that export training/validation stadistics to a csv file

Meters

• meters.BaseMeter: Interface for all meters
• meters.BatchMeters: Superclass of meters that works with batchs
• meters.CategoricalAccuracy: Meter for accuracy on categorical targets
• meters.BinaryAccuracy: Meter for accuracy on binary targets (assuming normalized inputs)
• meters.BinaryAccuracyWithLogits: Binary accuracy meter with an integrated activation function (by default logistic function)
• meters.ConfusionMatrix: Meter for confusion matrix.
• meters.MSE: Mean Squared Error meter
• meters.MSLE: Mean Squared Log Error meter
• meters.RMSE: Rooted Mean Squared Error meter
• meters.RMSLE: Rooted Mean Squared Log Error meter

Cross validation

• utils.data.CrossFoldValidation: Itererator through cross-fold-validation folds

Datasets

• utils.data.datasets.SubsetDataset: Dataset that is a subset of the original dataset
• utils.data.datasets.ShrinkDatset: Shrinks a dataset
• utils.data.datasets.UnsuperviseDataset: Makes a dataset unsupervised