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GRLMLP Class Reference
Packages » trg | Data objects » trg data objects

Class to keep all parameters of an expert MLP for the neuro trigger. More...

#include <GRLMLP.h>

Inheritance diagram for GRLMLP:

Public Member Functions

 GRLMLP ()
 default constructor.
 
 GRLMLP (std::vector< unsigned short > &nodes, unsigned short targets, const std::vector< float > &outputscale)
 constructor to set all parameters (not weights and relevantID ranges).
 
 ~GRLMLP ()
 destructor, empty because we don't allocate memory anywhere.
 
bool is_trained () const
 check if weights are default values or set by some trainer
 
unsigned get_number_of_layers () const
 get number of layers
 
unsigned get_number_of_nodes_layer (unsigned i_layer) const
 get number of nodes in a layer
 
unsigned get_number_of_weights () const
 get number of weights from length of weights vector
 
unsigned n_weights_cal () const
 calculate number of weights from number of nodes
 
unsigned n_bias_cal () const
 calculate number of weights from number of nodes
 
std::vector< float > get_weights () const
 get weights vector
 
std::vector< float > get_bias () const
 get bias vector
 
void set_weights (std::vector< float > &weights)
 set weights vector
 
void set_bias (std::vector< float > &bias)
 set bias vector
 
std::vector< int > get_total_bit_bias () const
 get bit width etc.
 
std::vector< int > get_int_bit_bias () const
 
std::vector< bool > get_is_signed_bias () const
 
std::vector< int > get_rounding_bias () const
 
std::vector< int > get_saturation_bias () const
 
std::vector< int > get_total_bit_accum () const
 
std::vector< int > get_int_bit_accum () const
 
std::vector< bool > get_is_signed_accum () const
 
std::vector< int > get_rounding_accum () const
 
std::vector< int > get_saturation_accum () const
 
std::vector< int > get_total_bit_weight () const
 
std::vector< int > get_int_bit_weight () const
 
std::vector< bool > get_is_signed_weight () const
 
std::vector< int > get_rounding_weight () const
 
std::vector< int > get_saturation_weight () const
 
std::vector< int > get_total_bit_relu () const
 
std::vector< int > get_int_bit_relu () const
 
std::vector< bool > get_is_signed_relu () const
 
std::vector< int > get_rounding_relu () const
 
std::vector< int > get_saturation_relu () const
 
std::vector< int > get_total_bit () const
 
std::vector< int > get_int_bit () const
 
std::vector< bool > get_is_signed () const
 
std::vector< int > get_rounding () const
 
std::vector< int > get_saturation () const
 
std::vector< std::vector< int > > get_W_input () const
 
std::vector< std::vector< int > > get_I_input () const
 
void set_total_bit_bias (const std::vector< int > &i)
 set bit width etc.
 
void set_int_bit_bias (const std::vector< int > &i)
 
void set_is_signed_bias (const std::vector< bool > &i)
 
void set_rounding_bias (const std::vector< int > &i)
 
void set_saturation_bias (const std::vector< int > &i)
 
void set_total_bit_accum (const std::vector< int > &i)
 
void set_int_bit_accum (const std::vector< int > &i)
 
void set_is_signed_accum (const std::vector< bool > &i)
 
void set_rounding_accum (const std::vector< int > &i)
 
void set_saturation_accum (const std::vector< int > &i)
 
void set_total_bit_weight (const std::vector< int > &i)
 
void set_int_bit_weight (const std::vector< int > &i)
 
void set_is_signed_weight (const std::vector< bool > &i)
 
void set_rounding_weight (const std::vector< int > &i)
 
void set_saturation_weight (const std::vector< int > &i)
 
void set_total_bit_relu (const std::vector< int > &i)
 
void set_int_bit_relu (const std::vector< int > &i)
 
void set_is_signed_relu (const std::vector< bool > &i)
 
void set_rounding_relu (const std::vector< int > &i)
 
void set_saturation_relu (const std::vector< int > &i)
 
void set_total_bit (const std::vector< int > &i)
 
void set_int_bit (const std::vector< int > &i)
 
void set_is_signed (const std::vector< bool > &i)
 
void set_rounding (const std::vector< int > &i)
 
void set_saturation (const std::vector< int > &i)
 
void set_W_input (const std::vector< std::vector< int > > &i)
 
void set_I_input (const std::vector< std::vector< int > > &i)
 
void Trained (bool trained)
 check if weights are default values or set by some trainer
 

Private Member Functions

 ClassDef (GRLMLP, 4)
 Needed to make the ROOT object storable.
 

Private Attributes

std::vector< unsigned short > m_n_nodes
 Number of nodes in each layer, not including bias nodes.
 
std::vector< float > m_weights
 Weights of the network.
 
std::vector< float > m_bias
 bias of the network.
 
std::vector< int > m_total_bit_bias
 bit width etc.
 
std::vector< int > m_int_bit_bias
 
std::vector< bool > m_is_signed_bias
 
std::vector< int > m_rounding_bias
 
std::vector< int > m_saturation_bias
 
std::vector< int > m_total_bit_accum
 
std::vector< int > m_int_bit_accum
 
std::vector< bool > m_is_signed_accum
 
std::vector< int > m_rounding_accum
 
std::vector< int > m_saturation_accum
 
std::vector< int > m_total_bit_weight
 
std::vector< int > m_int_bit_weight
 
std::vector< bool > m_is_signed_weight
 
std::vector< int > m_rounding_weight
 
std::vector< int > m_saturation_weight
 
std::vector< int > m_total_bit_relu
 
std::vector< int > m_int_bit_relu
 
std::vector< bool > m_is_signed_relu
 
std::vector< int > m_rounding_relu
 
std::vector< int > m_saturation_relu
 
std::vector< int > m_total_bit
 
std::vector< int > m_int_bit
 
std::vector< bool > m_is_signed
 
std::vector< int > m_rounding
 
std::vector< int > m_saturation
 
std::vector< std::vector< int > > m_W_input
 
std::vector< std::vector< int > > m_I_input
 
bool m_trained
 Indicator whether the weights are just default values or have been set by some trainer (set to true when setWeights() is first called).
 
unsigned short m_target_vars
 output variables: 1: z, 2: theta, 3: (z, theta)
 
std::vector< float > m_output_scale
 Output[i] of the MLP is scaled from [-1, 1] to [outputScale[2i], outputScale[2i+1]].
 

Friends

class GRLNeuroTrainerModule
 

Detailed Description

Class to keep all parameters of an expert MLP for the neuro trigger.

Definition at line 21 of file GRLMLP.h.

Constructor & Destructor Documentation

◆ GRLMLP() [1/2]

GRLMLP ( )

default constructor.

Definition at line 14 of file GRLMLP.cc.

14 :
15 m_n_nodes{24, 24, 24, 24, 1}, m_trained(false), m_target_vars(1), m_output_scale{ 0., 1.}
16{
17 m_weights.assign(n_weights_cal(), 0.);
18 m_bias.assign(n_bias_cal(), 0.);
19}
Belle2::GRLMLP::m_weights
std::vector< float > m_weights
Weights of the network.
Definition GRLMLP.h:122
Belle2::GRLMLP::m_target_vars
unsigned short m_target_vars
output variables: 1: z, 2: theta, 3: (z, theta)
Definition GRLMLP.h:159
Belle2::GRLMLP::m_n_nodes
std::vector< unsigned short > m_n_nodes
Number of nodes in each layer, not including bias nodes.
Definition GRLMLP.h:120
Belle2::GRLMLP::n_weights_cal
unsigned n_weights_cal() const
calculate number of weights from number of nodes
Definition GRLMLP.cc:32
Belle2::GRLMLP::n_bias_cal
unsigned n_bias_cal() const
calculate number of weights from number of nodes
Definition GRLMLP.cc:45
Belle2::GRLMLP::m_output_scale
std::vector< float > m_output_scale
Output[i] of the MLP is scaled from [-1, 1] to [outputScale[2i], outputScale[2i+1]].
Definition GRLMLP.h:162
Belle2::GRLMLP::m_bias
std::vector< float > m_bias
bias of the network.
Definition GRLMLP.h:124
Belle2::GRLMLP::m_trained
bool m_trained
Indicator whether the weights are just default values or have been set by some trainer (set to true w...
Definition GRLMLP.h:156

◆ GRLMLP() [2/2]

GRLMLP ( std::vector< unsigned short > & nodes,
unsigned short targets,
const std::vector< float > & outputscale )

constructor to set all parameters (not weights and relevantID ranges).

Definition at line 21 of file GRLMLP.cc.

24 :
25 m_n_nodes(nodes), m_trained(false), m_target_vars(targets), m_output_scale(outputscale)
26{
27 m_weights.assign(n_weights_cal(), 0.);
28 m_bias.assign(n_bias_cal(), 0.);
29}

◆ ~GRLMLP()

~GRLMLP ( )
inline

destructor, empty because we don't allocate memory anywhere.

Definition at line 34 of file GRLMLP.h.

34{ }

Member Function Documentation

◆ get_bias()

std::vector< float > get_bias ( ) const
inline

get bias vector

Definition at line 51 of file GRLMLP.h.

51{ return m_bias; }

◆ get_I_input()

std::vector< std::vector< int > > get_I_input ( ) const
inline

Definition at line 84 of file GRLMLP.h.

84{ return m_I_input; }

◆ get_int_bit()

std::vector< int > get_int_bit ( ) const
inline

Definition at line 79 of file GRLMLP.h.

79{ return m_int_bit; }

◆ get_int_bit_accum()

std::vector< int > get_int_bit_accum ( ) const
inline

Definition at line 64 of file GRLMLP.h.

64{ return m_int_bit_accum; }

◆ get_int_bit_bias()

std::vector< int > get_int_bit_bias ( ) const
inline

Definition at line 59 of file GRLMLP.h.

59{ return m_int_bit_bias; }

◆ get_int_bit_relu()

std::vector< int > get_int_bit_relu ( ) const
inline

Definition at line 74 of file GRLMLP.h.

74{ return m_int_bit_relu; }

◆ get_int_bit_weight()

std::vector< int > get_int_bit_weight ( ) const
inline

Definition at line 69 of file GRLMLP.h.

69{ return m_int_bit_weight; }

◆ get_is_signed()

std::vector< bool > get_is_signed ( ) const
inline

Definition at line 80 of file GRLMLP.h.

80{ return m_is_signed; }

◆ get_is_signed_accum()

std::vector< bool > get_is_signed_accum ( ) const
inline

Definition at line 65 of file GRLMLP.h.

65{ return m_is_signed_accum; }

◆ get_is_signed_bias()

std::vector< bool > get_is_signed_bias ( ) const
inline

Definition at line 60 of file GRLMLP.h.

60{ return m_is_signed_bias; }

◆ get_is_signed_relu()

std::vector< bool > get_is_signed_relu ( ) const
inline

Definition at line 75 of file GRLMLP.h.

75{ return m_is_signed_relu; }

◆ get_is_signed_weight()

std::vector< bool > get_is_signed_weight ( ) const
inline

Definition at line 70 of file GRLMLP.h.

70{ return m_is_signed_weight; }

◆ get_number_of_layers()

unsigned get_number_of_layers ( ) const
inline

get number of layers

Definition at line 39 of file GRLMLP.h.

39{ return m_n_nodes.size(); }

◆ get_number_of_nodes_layer()

unsigned get_number_of_nodes_layer ( unsigned i_layer) const
inline

get number of nodes in a layer

Definition at line 41 of file GRLMLP.h.

41{ return m_n_nodes[i_layer]; }

◆ get_number_of_weights()

unsigned get_number_of_weights ( ) const
inline

get number of weights from length of weights vector

Definition at line 43 of file GRLMLP.h.

43{ return m_weights.size(); }

◆ get_rounding()

std::vector< int > get_rounding ( ) const
inline

Definition at line 81 of file GRLMLP.h.

81{ return m_rounding; }

◆ get_rounding_accum()

std::vector< int > get_rounding_accum ( ) const
inline

Definition at line 66 of file GRLMLP.h.

66{ return m_rounding_accum; }

◆ get_rounding_bias()

std::vector< int > get_rounding_bias ( ) const
inline

Definition at line 61 of file GRLMLP.h.

61{ return m_rounding_bias; }

◆ get_rounding_relu()

std::vector< int > get_rounding_relu ( ) const
inline

Definition at line 76 of file GRLMLP.h.

76{ return m_rounding_relu; }

◆ get_rounding_weight()

std::vector< int > get_rounding_weight ( ) const
inline

Definition at line 71 of file GRLMLP.h.

71{ return m_rounding_weight; }

◆ get_saturation()

std::vector< int > get_saturation ( ) const
inline

Definition at line 82 of file GRLMLP.h.

82{ return m_saturation; }

◆ get_saturation_accum()

std::vector< int > get_saturation_accum ( ) const
inline

Definition at line 67 of file GRLMLP.h.

67{ return m_saturation_accum; }

◆ get_saturation_bias()

std::vector< int > get_saturation_bias ( ) const
inline

Definition at line 62 of file GRLMLP.h.

62{ return m_saturation_bias; }

◆ get_saturation_relu()

std::vector< int > get_saturation_relu ( ) const
inline

Definition at line 77 of file GRLMLP.h.

77{ return m_saturation_relu; }

◆ get_saturation_weight()

std::vector< int > get_saturation_weight ( ) const
inline

Definition at line 72 of file GRLMLP.h.

72{ return m_saturation_weight; }

◆ get_total_bit()

std::vector< int > get_total_bit ( ) const
inline

Definition at line 78 of file GRLMLP.h.

78{ return m_total_bit; }

◆ get_total_bit_accum()

std::vector< int > get_total_bit_accum ( ) const
inline

Definition at line 63 of file GRLMLP.h.

63{ return m_total_bit_accum; }

◆ get_total_bit_bias()

std::vector< int > get_total_bit_bias ( ) const
inline

get bit width etc.

constant in each node

Definition at line 58 of file GRLMLP.h.

58{ return m_total_bit_bias; }

◆ get_total_bit_relu()

std::vector< int > get_total_bit_relu ( ) const
inline

Definition at line 73 of file GRLMLP.h.

73{ return m_total_bit_relu; }

◆ get_total_bit_weight()

std::vector< int > get_total_bit_weight ( ) const
inline

Definition at line 68 of file GRLMLP.h.

68{ return m_total_bit_weight; }

◆ get_W_input()

std::vector< std::vector< int > > get_W_input ( ) const
inline

Definition at line 83 of file GRLMLP.h.

83{ return m_W_input; }

◆ get_weights()

std::vector< float > get_weights ( ) const
inline

get weights vector

Definition at line 49 of file GRLMLP.h.

49{ return m_weights; }

◆ is_trained()

bool is_trained ( ) const
inline

check if weights are default values or set by some trainer

Definition at line 37 of file GRLMLP.h.

37{ return m_trained; }

◆ n_bias_cal()

unsigned n_bias_cal ( ) const

calculate number of weights from number of nodes

Definition at line 45 of file GRLMLP.cc.

46{
47 unsigned nbias = 0;
48 if (get_number_of_layers() > 1) {
49 for (unsigned il = 1; il < get_number_of_layers(); ++il) {
50 nbias += m_n_nodes[il] ;
51 }
52 }
53 return nbias;
54}
Belle2::GRLMLP::get_number_of_layers
unsigned get_number_of_layers() const
get number of layers
Definition GRLMLP.h:39

◆ n_weights_cal()

unsigned n_weights_cal ( ) const

calculate number of weights from number of nodes

Definition at line 32 of file GRLMLP.cc.

33{
34 unsigned n_weights = 0;
35 if (get_number_of_layers() > 1) {
36 n_weights = m_n_nodes[0] * m_n_nodes[1];
37 for (unsigned il = 1; il < get_number_of_layers() - 1; ++il) {
38 n_weights += m_n_nodes[il] * m_n_nodes[il + 1];
39 }
40 }
41 return n_weights;
42}

◆ set_bias()

void set_bias ( std::vector< float > & bias)
inline

set bias vector

Definition at line 55 of file GRLMLP.h.

55{ m_bias = bias; }

◆ set_I_input()

void set_I_input ( const std::vector< std::vector< int > > & i)
inline

Definition at line 113 of file GRLMLP.h.

113{ m_I_input = i; }

◆ set_int_bit()

void set_int_bit ( const std::vector< int > & i)
inline

Definition at line 108 of file GRLMLP.h.

108{ m_int_bit = i; }

◆ set_int_bit_accum()

void set_int_bit_accum ( const std::vector< int > & i)
inline

Definition at line 93 of file GRLMLP.h.

93{ m_int_bit_accum = i; }

◆ set_int_bit_bias()

void set_int_bit_bias ( const std::vector< int > & i)
inline

Definition at line 88 of file GRLMLP.h.

88{ m_int_bit_bias = i; }

◆ set_int_bit_relu()

void set_int_bit_relu ( const std::vector< int > & i)
inline

Definition at line 103 of file GRLMLP.h.

103{ m_int_bit_relu = i; }

◆ set_int_bit_weight()

void set_int_bit_weight ( const std::vector< int > & i)
inline

Definition at line 98 of file GRLMLP.h.

98{ m_int_bit_weight = i; }

◆ set_is_signed()

void set_is_signed ( const std::vector< bool > & i)
inline

Definition at line 109 of file GRLMLP.h.

109{ m_is_signed = i; }

◆ set_is_signed_accum()

void set_is_signed_accum ( const std::vector< bool > & i)
inline

Definition at line 94 of file GRLMLP.h.

94{ m_is_signed_accum = i; }

◆ set_is_signed_bias()

void set_is_signed_bias ( const std::vector< bool > & i)
inline

Definition at line 89 of file GRLMLP.h.

89{ m_is_signed_bias = i; }

◆ set_is_signed_relu()

void set_is_signed_relu ( const std::vector< bool > & i)
inline

Definition at line 104 of file GRLMLP.h.

104{ m_is_signed_relu = i; }

◆ set_is_signed_weight()

void set_is_signed_weight ( const std::vector< bool > & i)
inline

Definition at line 99 of file GRLMLP.h.

99{ m_is_signed_weight = i; }

◆ set_rounding()

void set_rounding ( const std::vector< int > & i)
inline

Definition at line 110 of file GRLMLP.h.

110{ m_rounding = i; }

◆ set_rounding_accum()

void set_rounding_accum ( const std::vector< int > & i)
inline

Definition at line 95 of file GRLMLP.h.

95{ m_rounding_accum = i; }

◆ set_rounding_bias()

void set_rounding_bias ( const std::vector< int > & i)
inline

Definition at line 90 of file GRLMLP.h.

90{ m_rounding_bias = i; }

◆ set_rounding_relu()

void set_rounding_relu ( const std::vector< int > & i)
inline

Definition at line 105 of file GRLMLP.h.

105{ m_rounding_relu = i; }

◆ set_rounding_weight()

void set_rounding_weight ( const std::vector< int > & i)
inline

Definition at line 100 of file GRLMLP.h.

100{ m_rounding_weight = i; }

◆ set_saturation()

void set_saturation ( const std::vector< int > & i)
inline

Definition at line 111 of file GRLMLP.h.

111{ m_saturation = i; }

◆ set_saturation_accum()

void set_saturation_accum ( const std::vector< int > & i)
inline

Definition at line 96 of file GRLMLP.h.

96{ m_saturation_accum = i; }

◆ set_saturation_bias()

void set_saturation_bias ( const std::vector< int > & i)
inline

Definition at line 91 of file GRLMLP.h.

91{ m_saturation_bias = i; }

◆ set_saturation_relu()

void set_saturation_relu ( const std::vector< int > & i)
inline

Definition at line 106 of file GRLMLP.h.

106{ m_saturation_relu = i; }

◆ set_saturation_weight()

void set_saturation_weight ( const std::vector< int > & i)
inline

Definition at line 101 of file GRLMLP.h.

101{ m_saturation_weight = i; }

◆ set_total_bit()

void set_total_bit ( const std::vector< int > & i)
inline

Definition at line 107 of file GRLMLP.h.

107{ m_total_bit = i; }

◆ set_total_bit_accum()

void set_total_bit_accum ( const std::vector< int > & i)
inline

Definition at line 92 of file GRLMLP.h.

92{ m_total_bit_accum = i; }

◆ set_total_bit_bias()

void set_total_bit_bias ( const std::vector< int > & i)
inline

set bit width etc.

constant in each node

Definition at line 87 of file GRLMLP.h.

87{ m_total_bit_bias = i; }

◆ set_total_bit_relu()

void set_total_bit_relu ( const std::vector< int > & i)
inline

Definition at line 102 of file GRLMLP.h.

102{ m_total_bit_relu = i; }

◆ set_total_bit_weight()

void set_total_bit_weight ( const std::vector< int > & i)
inline

Definition at line 97 of file GRLMLP.h.

97{ m_total_bit_weight = i; }

◆ set_W_input()

void set_W_input ( const std::vector< std::vector< int > > & i)
inline

Definition at line 112 of file GRLMLP.h.

112{ m_W_input = i; }

◆ set_weights()

void set_weights ( std::vector< float > & weights)
inline

set weights vector

Definition at line 53 of file GRLMLP.h.

53{ m_weights = weights; }

◆ Trained()

void Trained ( bool trained)
inline

check if weights are default values or set by some trainer

Definition at line 116 of file GRLMLP.h.

116{ m_trained = trained; }

Friends And Related Symbol Documentation

◆ GRLNeuroTrainerModule

friend class GRLNeuroTrainerModule
friend

Definition at line 24 of file GRLMLP.h.

Member Data Documentation

◆ m_bias

std::vector<float> m_bias
private

bias of the network.

Definition at line 124 of file GRLMLP.h.

◆ m_I_input

std::vector<std::vector<int> > m_I_input
private

Definition at line 152 of file GRLMLP.h.

◆ m_int_bit

std::vector<int> m_int_bit
private

Definition at line 147 of file GRLMLP.h.

◆ m_int_bit_accum

std::vector<int> m_int_bit_accum
private

Definition at line 132 of file GRLMLP.h.

◆ m_int_bit_bias

std::vector<int> m_int_bit_bias
private

Definition at line 127 of file GRLMLP.h.

◆ m_int_bit_relu

std::vector<int> m_int_bit_relu
private

Definition at line 142 of file GRLMLP.h.

◆ m_int_bit_weight

std::vector<int> m_int_bit_weight
private

Definition at line 137 of file GRLMLP.h.

◆ m_is_signed

std::vector<bool> m_is_signed
private

Definition at line 148 of file GRLMLP.h.

◆ m_is_signed_accum

std::vector<bool> m_is_signed_accum
private

Definition at line 133 of file GRLMLP.h.

◆ m_is_signed_bias

std::vector<bool> m_is_signed_bias
private

Definition at line 128 of file GRLMLP.h.

◆ m_is_signed_relu

std::vector<bool> m_is_signed_relu
private

Definition at line 143 of file GRLMLP.h.

◆ m_is_signed_weight

std::vector<bool> m_is_signed_weight
private

Definition at line 138 of file GRLMLP.h.

◆ m_n_nodes

std::vector<unsigned short> m_n_nodes
private

Number of nodes in each layer, not including bias nodes.

Definition at line 120 of file GRLMLP.h.

◆ m_output_scale

std::vector<float> m_output_scale
private

Output[i] of the MLP is scaled from [-1, 1] to [outputScale[2i], outputScale[2i+1]].

Definition at line 162 of file GRLMLP.h.

◆ m_rounding

std::vector<int> m_rounding
private

Definition at line 149 of file GRLMLP.h.

◆ m_rounding_accum

std::vector<int> m_rounding_accum
private

Definition at line 134 of file GRLMLP.h.

◆ m_rounding_bias

std::vector<int> m_rounding_bias
private

Definition at line 129 of file GRLMLP.h.

◆ m_rounding_relu

std::vector<int> m_rounding_relu
private

Definition at line 144 of file GRLMLP.h.

◆ m_rounding_weight

std::vector<int> m_rounding_weight
private

Definition at line 139 of file GRLMLP.h.

◆ m_saturation

std::vector<int> m_saturation
private

Definition at line 150 of file GRLMLP.h.

◆ m_saturation_accum

std::vector<int> m_saturation_accum
private

Definition at line 135 of file GRLMLP.h.

◆ m_saturation_bias

std::vector<int> m_saturation_bias
private

Definition at line 130 of file GRLMLP.h.

◆ m_saturation_relu

std::vector<int> m_saturation_relu
private

Definition at line 145 of file GRLMLP.h.

◆ m_saturation_weight

std::vector<int> m_saturation_weight
private

Definition at line 140 of file GRLMLP.h.

◆ m_target_vars

unsigned short m_target_vars
private

output variables: 1: z, 2: theta, 3: (z, theta)

Definition at line 159 of file GRLMLP.h.

◆ m_total_bit

std::vector<int> m_total_bit
private

Definition at line 146 of file GRLMLP.h.

◆ m_total_bit_accum

std::vector<int> m_total_bit_accum
private

Definition at line 131 of file GRLMLP.h.

◆ m_total_bit_bias

std::vector<int> m_total_bit_bias
private

bit width etc.

constant in each node

Definition at line 126 of file GRLMLP.h.

◆ m_total_bit_relu

std::vector<int> m_total_bit_relu
private

Definition at line 141 of file GRLMLP.h.

◆ m_total_bit_weight

std::vector<int> m_total_bit_weight
private

Definition at line 136 of file GRLMLP.h.

◆ m_trained

bool m_trained
private

Indicator whether the weights are just default values or have been set by some trainer (set to true when setWeights() is first called).

Definition at line 156 of file GRLMLP.h.

◆ m_W_input

std::vector<std::vector<int> > m_W_input
private

Definition at line 151 of file GRLMLP.h.

◆ m_weights

std::vector<float> m_weights
private

Weights of the network.

Definition at line 122 of file GRLMLP.h.

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