Layer Class Reference

Inheritance diagram for Layer:

Collaboration diagram for Layer:

Public Member Functions
def	__init__ (self, name, tf_activation_str, dim_input, dim_output, p_bias, p_w, random_seed=None)
def	__call__ (self, x)
def	variable_to_summary (self, var, step, writer)
def	all_to_summary (self, step, writer)

Public Attributes
	tf_activation
	layer parameters More...
	shape
	layer shape
	w
	init parameters for uniform distribution
	b
	init parameters for bias
	input
	input
	output
	output

Private Member Functions
def	_init_bias (self, width, init_val, name=None)
def	_init_weight (self, shape, stddev, operation_seed, name=None)

Detailed Description

definition of a layer obj

Definition at line 21 of file tensorflow_dnn_model.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			name,
			tf_activation_str,
			dim_input,
			dim_output,
			p_bias,
			p_w,
			random_seed = None
	)

:param name: name of the layer.
:param tf_activation: string, name of an available tensorflow activations function
:param dim_input: dimension of the input
:param dim_output: dimension of the output
:param p_bias: initial bias
:param p_w: stddev of uniform distribution to initialize
:param random_seed: random seed used in initialising the weights
:return: None

Definition at line 26 of file tensorflow_dnn_model.py.

                 random_seed=None):
        """
        :param name: name of the layer.
        :param tf_activation: string, name of an available tensorflow activations function
        :param dim_input: dimension of the input
        :param dim_output: dimension of the output
        :param p_bias: initial bias
        :param p_w: stddev of uniform distribution to initialize
        :param random_seed: random seed used in initialising the weights
        :return: None
        """
        
        super().__init__(name=name)
 
        tf_activation_dict = {
            'tanh': tf.nn.tanh,
            'sigmoid': tf.nn.sigmoid,
            'relu': tf.nn.relu,
            'leaky_relu': tf.nn.leaky_relu,
        }
 
        if tf_activation_str not in tf_activation_dict:
            raise ValueError
 
        
        self.tf_activation = tf_activation_dict[tf_activation_str]
 
        
        self.shape = [dim_input, dim_output]
 
        
        self.w = self._init_weight(self.shape, p_w, random_seed)
 
        
        self.b = self._init_bias(self.shape[1], p_bias)
 
        
        self.input = None
 
        
        self.output = None
 

Member Function Documentation

__call__()

def __call__	(		self,
			x
	)

evaluate the layer

Definition at line 88 of file tensorflow_dnn_model.py.

_init_bias()

def _init_bias (   self,   width,   init_val,   name = None  )

private

define bias variables

Definition at line 69 of file tensorflow_dnn_model.py.

_init_weight()

def _init_weight (   self,   shape,   stddev,   operation_seed,   name = None  )

private

define weight variables

Definition at line 78 of file tensorflow_dnn_model.py.

all_to_summary()

def all_to_summary	(		self,
			step,
			writer
	)

Passes all layer variables to the tf.summary writer.

Definition at line 109 of file tensorflow_dnn_model.py.

variable_to_summary()

def variable_to_summary	(		self,
			var,
			step,
			writer
	)

Passes information about each variable to the summary writer.

Definition at line 94 of file tensorflow_dnn_model.py.

Member Data Documentation

tf_activation

tf_activation

layer parameters

activation function

Definition at line 52 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