The instance of the class should be created by using training_session() function.
Trainer(model, criterion, parameter_learners, progress_writers = NULL)
| model | - root node of the Function to train |
|---|---|
| criterion | (list of Function or Variable) - Function with one or two outputs, representing loss and, if given, evaluation metric (in this order) |
| parameter_learners | (list) – list of learners |
| progress_writers | (progress writer or list of them) – optionally, list of progress writers to automatically track training progress. |
A training session trains a model using the specified trainer and configs. Different aspects of training such as data sources, checkpointing, cross validation, progress printing can be configured using the corresponding config classes.
****** Properties: ******
evaluation_function - The evaluation function that the trainer is using.
loss_function - The loss function that the trainer is using.
model - The model that the trainer is training.
parameter_learners - The parameter learners that the trainer is using.
previous_minibatch_evaluation_average - The average evaluation criterion value per sample for the last minibatch trained
previous_minibatch_loss_average - The average training loss per sample for the last minibatch trained
previous_minibatch_sample_count - The number of samples in the last minibatch trained with
total_number_of_samples_seen - The number of samples seen globally between all workers from the beginning of training.
****** Associated Functions: ******
restore_trainer_from_checkpoint
save_trainer_checkpoint
summarize_training_progress
summarize_test_progress
test_minibatch
train_minibatch