Trainer Class Reference

Public Member Functions
def	__init__ (self, model, data_set, sess, log_dir=None, save_name=None, monitoring_size=100000, input_placeholders=None)
def	train_model (self)

Public Attributes
	model
	model
	data_set
	data set
	monitoring_size
	monitoring size
	sess
	tf.session
	log_dir
	log directory
	x
	input placeholder features
	y_
	input placeholder targets
	monitoring_params
	monitoring params for early stopping criterion, loss function, etc
	termination_criterion
	termination criterion
	max_epochs
	global_training_parameters
	current_epoch
	current epoch
	minimizer
	optimizer
	train_log_dict
	train_log_dict
	saver
	saver
	save_name
	save name
	train_monitor
	train_monitor
	valid_monitor
	valid monitor
	train_writer
	train writer
	test_writer
	test writer
	merged_summary
	summary
	epoch_parameters
	epoch parameters

Private Member Functions
def	_prepare_monitoring (self)
def	_prepare_tensorboard (self, log_dir)
def	_add_to_basf2_collections (self)
def	_save_best_state (self, monitoring_params, label_name='mean_cross_entropy')
def	_closing_ops (self)
def	_train_epoch (self, current_epoch)

Private Attributes
	_time
	time

Detailed Description

handling the training of the network model

Definition at line 498 of file tensorflow_dnn_model.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			model,
			data_set,
			sess,
			log_dir = None,
			save_name = None,
			monitoring_size = 100000,
			input_placeholders = None
	)

class to train a predefined model
:param model: DefaultModel obj
:param data_set: TFData obj
:param sess: tensorflow.Session 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
:param input_placeholders: list of tf.placeholders, [features, targets]

Definition at line 503 of file tensorflow_dnn_model.py.

Member Function Documentation

_add_to_basf2_collections()

def _add_to_basf2_collections (   self )

private

add to basf2 collection

Definition at line 622 of file tensorflow_dnn_model.py.

_closing_ops()

def _closing_ops (   self )

private

closing ops

Definition at line 651 of file tensorflow_dnn_model.py.

_prepare_monitoring()

def _prepare_monitoring (   self )

private

checking dataset sizes for evaluation

Definition at line 593 of file tensorflow_dnn_model.py.

_prepare_tensorboard()

def _prepare_tensorboard (   self,   log_dir  )

private

prepare tensorboard

Definition at line 607 of file tensorflow_dnn_model.py.

_save_best_state()

def _save_best_state (   self,   monitoring_params,   label_name = 'mean_cross_entropy'  )

private

save model only if a global minimum is reached on validation set
:return:

Definition at line 632 of file tensorflow_dnn_model.py.

_train_epoch()

def _train_epoch (   self,   current_epoch  )

private

train epoch

Definition at line 666 of file tensorflow_dnn_model.py.

train_model()

def train_model

(

self

)

train model

Definition at line 730 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