Trainer Class Reference

Public Member Functions
def	__init__ (self, model, data_set, log_dir=None, save_name=None, monitoring_size=10000)
def	train_model (self)

Public Attributes
	model
	model
	data_set
	dataset
	monitoring_size
	monitoring size
	log_dir
	log dir
	termination_criterion
	termination criterion
	current_epoch
	initialise current epoch
	best_epoch
	initialise best epoch
	save_name
	set the path and name for saving the weightfiles
	train_monitor
	train_monitor
	valid_monitor
	test monitor
	train_writer
	tf.summary.writer for training
	valid_writer
	tf.summary.writer for validation
	optimizer
	set optimizer for this epoch

Private Member Functions
def	_prepare_monitoring (self)
def	_prepare_tensorboard (self, log_dir)
def	_train_epoch (self, current_epoch)
def	_save_best_state (self, cross_entropy)
def	_closing_ops (self)

Private Attributes
	_time
	current time

Detailed Description

handling the training of the network model

Definition at line 450 of file tensorflow_dnn_model.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			model,
			data_set,
			log_dir = None,
			save_name = None,
			monitoring_size = 10000
	)

class to train a predefined model
:param model: DefaultModel obj
:param data_set: TFData obj
:param log_dir: str, directory name of tensorboard logging
:param save_name: str, path and name for saving the weightfiles
:param monitoring_size: int, number of events of training fraction used for monitoring

Definition at line 455 of file tensorflow_dnn_model.py.

Member Function Documentation

_closing_ops()

def _closing_ops (   self )

private

closing operations

Definition at line 625 of file tensorflow_dnn_model.py.

_prepare_monitoring()

def _prepare_monitoring (   self )

private

checking dataset sizes for evaluation. These samples are used after each epoch to collect
summary statistics and test early stopping criteria.

Definition at line 508 of file tensorflow_dnn_model.py.

_prepare_tensorboard()

def _prepare_tensorboard (   self,   log_dir  )

private

prepare tensorboard

Definition at line 524 of file tensorflow_dnn_model.py.

_save_best_state()

def _save_best_state (   self,   cross_entropy  )

private

save model as a checkpoint only if a global minimum is reached on validation sample
:return:

Definition at line 607 of file tensorflow_dnn_model.py.

_train_epoch()

def _train_epoch (   self,   current_epoch  )

private

train epoch

Definition at line 538 of file tensorflow_dnn_model.py.

train_model()

def train_model

(

self

)

train model

Definition at line 634 of file tensorflow_dnn_model.py.

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The documentation for this class was generated from the following file:

• analysis/scripts/dft/tensorflow_dnn_model.py