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	initialize (self, data_set)
def	__call__ (self, x)
def	get_optimizer (self, epoch=0)
def	loss (self, predict_y, true_y)

Public Attributes
	mlp
	mlp net
	wd_coeffs
	weight decay coefficients
	global_step
	global step
	c_mom_init
	initial momentum
	c_mom_max
	maximum momentum
	c_mom_epochs
	momentum epochs
	c_mom_dec_rate
	momentum decay rate
	c_lr_init
	initial learning rate
	c_lr_min
	minimum learning rate
	c_lr_dec_rate
	learning rate decay rate
	c_stop_epochs
	number of epochs without improvement for early termination
	c_staircase
	use staircase
	batches_per_epoch
	batches per epoch unknown. More...
	optimizers
	define multiple optimizers
	optimizer_change_epochs
	used opimizers
	min_epochs
	min epochs
	max_epochs
	max 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

Private Member Functions
def	_default_termination_criterion (self, monitoring_param, epoch, prop_dec=1e-5)
def	_get_learning_rate (self)
def	_get_momentum (self)
def	_set_optimizer (self)

Detailed Description

define the default model

Definition at line 198 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
:param mlp: network model.
:param mom_init: initial momentum
:param mom_max: maximum momentum
:param mom_epochs: momentum epochs
:param lr_init: initial learning rate
:param lr_min: minimum learning rate
:param lr_dec_rate: learning rate decay factor
:param stop_epochs: number of epochs without improvement required for early termination
:param min_epochs: minimum number of epochs for training
:param max_epochs: maximum number of epochs for traning
:param wd_coeffs: weight decay coefficients. If not None must have one per mlp layer.
:param change_optimizer:
:param staircaise:
"param smooth_cross_entropy:

Definition at line 203 of file tensorflow_dnn_model.py.

Member Function Documentation

__call__()

def __call__	(		self,
			x
	)

Call the mlp

Definition at line 336 of file tensorflow_dnn_model.py.

_default_termination_criterion()

def _default_termination_criterion (   self,   monitoring_param,   epoch,   prop_dec = 1e-5  )

private

early stopping criterion

:param monitoring_param: the parameter to monitor for early termination
:param epoch: the current epoch
:param prop_dec:
:return:

Definition at line 342 of file tensorflow_dnn_model.py.

_get_learning_rate()

def _get_learning_rate (   self )

private

Returns the learning rate at the current global step.

Definition at line 367 of file tensorflow_dnn_model.py.

_get_momentum()

def _get_momentum (   self )

private

returns the momentum at the current global step.

Definition at line 376 of file tensorflow_dnn_model.py.

_set_optimizer()

def _set_optimizer (   self )

private

set optimizers

Definition at line 391 of file tensorflow_dnn_model.py.

get_optimizer()

def get_optimizer	(		self,
			epoch = 0
	)

get the optimizer. If multiple optimizers are booked gets the one appropriate for the epoch.

:param epoch: current epoch.

Definition at line 400 of file tensorflow_dnn_model.py.

initialize()

def initialize	(		self,
			data_set
	)

Finalises intitialization based of data_set specific information (number of batches per epoch)

Definition at line 316 of file tensorflow_dnn_model.py.

loss()

def loss	(		self,
			predict_y,
			true_y
	)

calculate the loss

:param predict_y: predicted labels
:param true_y: true labels

Definition at line 418 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 276 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