/usr/include/tesseract/neuron.h is in libtesseract-dev 3.02.01-6.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | // Copyright 2008 Google Inc.
// All Rights Reserved.
// Author: ahmadab@google.com (Ahmad Abdulkader)
//
// neuron.h: Declarations of a class for an object that
// represents a single neuron in a neural network
//
#ifndef NEURON_H
#define NEURON_H
#include <math.h>
#include <vector>
#ifdef USE_STD_NAMESPACE
using std::vector;
#endif
namespace tesseract {
// Input Node bias values
static const float kInputNodeBias = 0.0f;
class Neuron {
public:
// Types of nodes
enum NeuronTypes {
Unknown = 0,
Input,
Hidden,
Output
};
Neuron();
~Neuron();
// set the forward dirty flag indicating that the
// activation of the net is not fresh
void Clear() {
frwd_dirty_ = true;
}
// Read a binary representation of the neuron info from
// an input buffer.
template <class BuffType> bool ReadBinary(BuffType *input_buff) {
float val;
if (input_buff->Read(&val, sizeof(val)) != sizeof(val)) {
return false;
}
// input nodes should have no biases
if (node_type_ == Input) {
bias_ = kInputNodeBias;
} else {
bias_ = val;
}
// read fanin count
int fan_in_cnt;
if (input_buff->Read(&fan_in_cnt, sizeof(fan_in_cnt)) !=
sizeof(fan_in_cnt)) {
return false;
}
// validate fan-in cnt
if (fan_in_cnt != fan_in_.size()) {
return false;
}
// read the weights
for (int in = 0; in < fan_in_cnt; in++) {
if (input_buff->Read(&val, sizeof(val)) != sizeof(val)) {
return false;
}
*(fan_in_weights_[in]) = val;
}
return true;
}
// Add a new connection from this neuron *From*
// a target neuron using specfied params
// Note that what is actually copied in this function are pointers to the
// specified Neurons and weights and not the actualt values. This is by
// design to centralize the alloction of neurons and weights and so
// increase the locality of reference and improve cache-hits resulting
// in a faster net. This technique resulted in a 2X-10X speedup
// (depending on network size and processor)
void AddFromConnection(Neuron *neuron_vec,
float *wts_offset,
int from_cnt);
// Set the type of a neuron
void set_node_type(NeuronTypes type);
// Computes the output of the node by
// "pulling" the output of the fan-in nodes
void FeedForward();
// fast computation of sigmoid function using a lookup table
// defined in sigmoid_table.cpp
static float Sigmoid(float activation);
// Accessor functions
float output() const {
return output_;
}
void set_output(float out_val) {
output_ = out_val;
}
int id() const {
return id_;
}
int fan_in_cnt() const {
return fan_in_.size();
}
Neuron * fan_in(int idx) const {
return fan_in_[idx];
}
float fan_in_wts(int idx) const {
return *(fan_in_weights_[idx]);
}
void set_id(int id) {
id_ = id;
}
float bias() const {
return bias_;
}
Neuron::NeuronTypes node_type() const {
return node_type_;
}
protected:
// Type of Neuron
NeuronTypes node_type_;
// unqique id of the neuron
int id_;
// node bias
float bias_;
// node net activation
float activation_;
// node output
float output_;
// pointers to fanin nodes
vector<Neuron *> fan_in_;
// pointers to fanin weights
vector<float *> fan_in_weights_;
// Sigmoid function lookup table used for fast computation
// of sigmoid function
static const float kSigmoidTable[];
// flag determining if the activation of the node
// is fresh or not (dirty)
bool frwd_dirty_;
// Initializer
void Init();
};
}
#endif // NEURON_H__
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