Supervised Learner, support data parallelism, multi GPU, accept TFRecords data as input

tefla.core.learning_v2.SupervisedLearner (model, cnf, clip_by_global_norm=False, data_balancing=1, **kwargs)

Args

• 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_dir, data_dir_val=None, features_keys=None, weights_from=None, weights_dir='weights', max_to_keep=None, start_epoch=1, summary_every=10, training_set_size=None, val_set_size=None, dataset_name='cifar10', keep_moving_averages=False)

Args

• data_dir: str, training dataset directory (where tfrecords are staored for training)
• data_dir_val: str optional, validation dataset directory (where tfrecords are stored for validation)
• features_keys: a dict, tfrecords keys to datum features
• e.g.:
• features_keys = { 'image/encoded/image': tf.FixedLenFeature((), tf.string, default_value=''), 'image/format': tf.FixedLenFeature((), tf.string, default_value='jpg'), 'image/class/label': tf.FixedLenFeature([], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
• }
• weights_from: str, if not None, initializes model from exisiting weights
• training_set_size: int, number of training examples
• val_set_size: int, set if data_dir_val not None, number of validation examples
• dataset_name: a optional, Name of the dataset
• 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

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