DefaultModel Class Reference

Inheritance diagram for DefaultModel:

Collaboration diagram for DefaultModel:

Public Member Functions
def	__init__ (self, mlp, mom_init=.9, mom_max=.99, mom_epochs=200, lr_init=.05, lr_min=1e-6, lr_dec_rate=.976, stop_epochs=10, min_epochs=200, max_epochs=1000, wd_coeffs=None, change_optimizer=None, staircase=True, smooth_cross_entropy=False)
def	get_minimizer (self, epoch=0)
def	initialize (self, data_set, input_placeholders=None)

Public Attributes
	wd_coeffs
	weight decay coefficients
	mlp
	network model
	output
	output
	global_step
	global step
	c_mom_init
	initial momentum
	c_mom_max
	maximum momentum
	c_mom_epochs
	momentum epochs
	c_lr_init
	initial learning rate
	c_lr_min
	minimal learning rate
	c_lr_dec_rate
	learning rate decay factor
	c_stop_epochs
	number of epochs until stopping
	c_staircase
	use staircase
	batches_per_epoch
	batches per epoch unknown. More...
	optimizers
	define multiple optimizers
	optimizer_change_epochs
	used opimizers
	monitoring_params
	params for monitoring
	monitoring_labels
	params for labeling
	mon_dict
	further monitoring variables
	x
	variable space must be known
	y_
	targets
	weights
	weights
	max_epochs
	other variables
	min_epochs
	min epochs
	termination_criterion
	termination criterion
	recent_params
	recent params
	best_value
	the best value will be set a default start value, then updated with the termination criterion
	step_countdown
	step countdown
	smooth_cross_entropy
	True for a small epsilon addition, false for a clipped network output.
	is_initialized
	check if initialized
	loss
	cross entropy with weight decay

Private Member Functions
def	_set_optimizer (self)
def	_set_loss (self)
def	_set_monitoring_params (self)
def	_default_termination_criterion (self, monitoring_params, epoch, label_name='mean_cross_entropy', prop_dec=1e-5)

Detailed Description

define the default model

Definition at line 222 of file tensorflow_dnn_model.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			mlp,
			mom_init = .9,
			mom_max = .99,
			mom_epochs = 200,
			lr_init = .05,
			lr_min = 1e-6,
			lr_dec_rate = .976,
			stop_epochs = 10,
			min_epochs = 200,
			max_epochs = 1000,
			wd_coeffs = None,
			change_optimizer = None,
			staircase = True,
			smooth_cross_entropy = False
	)

initialization function

Definition at line 227 of file tensorflow_dnn_model.py.

    def __init__(self, mlp, mom_init=.9, mom_max=.99, mom_epochs=200, lr_init=.05, lr_min=1e-6,
                 lr_dec_rate=.976,
                 stop_epochs=10, min_epochs=200, max_epochs=1000, wd_coeffs=None, change_optimizer=None,
                 staircase=True, smooth_cross_entropy=False):
        """
        initialization function
        """
 
        if wd_coeffs is not None:
            assert len(wd_coeffs) == len(mlp.layers)
 
        
        self.wd_coeffs = wd_coeffs
 
        
        self.mlp = mlp
 
        
        self.output = None
 
        with tf.name_scope('global_step'):
            
            self.global_step = tf.Variable(0, trainable=False)
 
        # optimizer params
        
        self.c_mom_init = mom_init
        
        self.c_mom_max = mom_max
        
        self.c_mom_epochs = mom_epochs
 
        
        self.c_lr_init = lr_init
        
        self.c_lr_min = lr_min
        
        self.c_lr_dec_rate = lr_dec_rate
 
        
        self.c_stop_epochs = stop_epochs
 
        
        self.c_staircase = staircase
 
        
        self.batches_per_epoch = None
 
        
        self.optimizers = []
        # list with epochs in which optimizers will be changed, if None is given, only the default optimizer will be
        
        self.optimizer_change_epochs = change_optimizer
        if change_optimizer is not None:
            self.optimizer_change_epochs.insert(0, 0)
            self.optimizer_change_epochs.append(sys.maxsize)
 
        
        self.monitoring_params = None
        
        self.monitoring_labels = None
 
        
        self.mon_dict = dict()
 
        
        self.x = None
        
        self.y_ = None
        
        self.weights = None
 
        
        self.max_epochs = max_epochs
 
        # termination criterion
        
        self.min_epochs = min_epochs
 
        
        self.termination_criterion = None
        
        self.recent_params = []
        
        self.best_value = numpy.inf
 
        
        self.step_countdown = self.c_stop_epochs
 
        
        self.smooth_cross_entropy = smooth_cross_entropy
 
        
        self.is_initialized = False
 

Member Function Documentation

_default_termination_criterion()

def _default_termination_criterion (   self,   monitoring_params,   epoch,   label_name = 'mean_cross_entropy',   prop_dec = 1e-5  )

private

:param monitoring_params:
:param epoch:
:param label_name:
:param steps:
:param prop_dec:
:return:

Definition at line 426 of file tensorflow_dnn_model.py.

_set_loss()

def _set_loss (   self )

private

set loss

Definition at line 365 of file tensorflow_dnn_model.py.

_set_monitoring_params()

def _set_monitoring_params (   self )

private

sets monitoring params

Definition at line 401 of file tensorflow_dnn_model.py.

_set_optimizer()

def _set_optimizer (   self )

private

set optimizer

Definition at line 322 of file tensorflow_dnn_model.py.

get_minimizer()

def get_minimizer	(		self,
			epoch = 0
	)

get minimizer

Definition at line 454 of file tensorflow_dnn_model.py.

initialize()

def initialize	(		self,
			data_set,
			input_placeholders = None
	)

initialized

Definition at line 471 of file tensorflow_dnn_model.py.

Member Data Documentation

batches_per_epoch

batches_per_epoch

batches per epoch unknown.

needs to be set with initialize

Definition at line 270 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