Supervised Trainer, support data parallelism, multi GPU

tefla.core.learning.SupervisedLearner (model, cnf, clip_by_global_norm=False, **kwargs)

model: model definition
cnf: dict, training configs
training_iterator: iterator to use for training data access, processing and augmentations
validation_iterator: iterator to use for validation data access, processing and augmentations
start_epoch: int, training start epoch; for resuming training provide the last
epoch number to resume training from, its a required parameter for training data balancing
resume_lr: float, learning rate to use for new training
classification: bool, classificattion or regression
clip_norm: bool, to clip gradient using gradient norm, stabilizes the training
n_iters_per_epoch: int, number of iteratiosn for each epoch; e.g: total_training_samples/batch_size
gpu_memory_fraction: amount of gpu memory to use
is_summary: bool, to write summary or not

Methods

fit (data_set, weights_from=None, weights_dir='weights', start_epoch=1, summary_every=10, keep_moving_averages=False, **kwargs)

data_set: dataset instance to use to access data for training/validation
weights_from: str, if not None, initializes model from exisiting weights
start_epoch: int, epoch number to start training from e.g. for retarining set the epoch number you want to resume training from
summary_every: int, epoch interval to write summary; higher value means lower frequency of summary writing
keep_moving_averages: a bool, keep moving averages of trainable variables