/usr/include/tesseract/neural_net.h is in libtesseract-dev 3.02.01-2.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 | // Copyright 2008 Google Inc.
// All Rights Reserved.
// Author: ahmadab@google.com (Ahmad Abdulkader)
//
// neural_net.h: Declarations of a class for an object that
// represents an arbitrary network of neurons
//
#ifndef NEURAL_NET_H
#define NEURAL_NET_H
#include <string>
#include <vector>
#include "neuron.h"
#include "input_file_buffer.h"
namespace tesseract {
// Minimum input range below which we set the input weight to zero
static const float kMinInputRange = 1e-6f;
class NeuralNet {
public:
NeuralNet();
virtual ~NeuralNet();
// create a net object from a file. Uses stdio
static NeuralNet *FromFile(const string file_name);
// create a net object from an input buffer
static NeuralNet *FromInputBuffer(InputFileBuffer *ib);
// Different flavors of feed forward function
template <typename Type> bool FeedForward(const Type *inputs,
Type *outputs);
// Compute the output of a specific output node.
// This function is useful for application that are interested in a single
// output of the net and do not want to waste time on the rest
template <typename Type> bool GetNetOutput(const Type *inputs,
int output_id,
Type *output);
// Accessor functions
int in_cnt() const { return in_cnt_; }
int out_cnt() const { return out_cnt_; }
protected:
struct Node;
// A node-weight pair
struct WeightedNode {
Node *input_node;
float input_weight;
};
// node struct used for fast feedforward in
// Read only nets
struct Node {
float out;
float bias;
int fan_in_cnt;
WeightedNode *inputs;
};
// Read-Only flag (no training: On by default)
// will presumeably be set to false by
// the inherting TrainableNeuralNet class
bool read_only_;
// input count
int in_cnt_;
// output count
int out_cnt_;
// Total neuron count (including inputs)
int neuron_cnt_;
// count of unique weights
int wts_cnt_;
// Neuron vector
Neuron *neurons_;
// size of allocated weight chunk (in weights)
// This is basically the size of the biggest network
// that I have trained. However, the class will allow
// a bigger sized net if desired
static const int kWgtChunkSize = 0x10000;
// Magic number expected at the beginning of the NN
// binary file
static const unsigned int kNetSignature = 0xFEFEABD0;
// count of allocated wgts in the last chunk
int alloc_wgt_cnt_;
// vector of weights buffers
vector<vector<float> *>wts_vec_;
// Is the net an auto-encoder type
bool auto_encoder_;
// vector of input max values
vector<float> inputs_max_;
// vector of input min values
vector<float> inputs_min_;
// vector of input mean values
vector<float> inputs_mean_;
// vector of input standard deviation values
vector<float> inputs_std_dev_;
// vector of input offsets used by fast read-only
// feedforward function
vector<Node> fast_nodes_;
// Network Initialization function
void Init();
// Clears all neurons
void Clear() {
for (int node = 0; node < neuron_cnt_; node++) {
neurons_[node].Clear();
}
}
// Reads the net from an input buffer
template<class ReadBuffType> bool ReadBinary(ReadBuffType *input_buff) {
// Init vars
Init();
// is this an autoencoder
unsigned int read_val;
unsigned int auto_encode;
// read and verify signature
if (input_buff->Read(&read_val, sizeof(read_val)) != sizeof(read_val)) {
return false;
}
if (read_val != kNetSignature) {
return false;
}
if (input_buff->Read(&auto_encode, sizeof(auto_encode)) !=
sizeof(auto_encode)) {
return false;
}
auto_encoder_ = auto_encode;
// read and validate total # of nodes
if (input_buff->Read(&read_val, sizeof(read_val)) != sizeof(read_val)) {
return false;
}
neuron_cnt_ = read_val;
if (neuron_cnt_ <= 0) {
return false;
}
// set the size of the neurons vector
neurons_ = new Neuron[neuron_cnt_];
if (neurons_ == NULL) {
return false;
}
// read & validate inputs
if (input_buff->Read(&read_val, sizeof(read_val)) != sizeof(read_val)) {
return false;
}
in_cnt_ = read_val;
if (in_cnt_ <= 0) {
return false;
}
// read outputs
if (input_buff->Read(&read_val, sizeof(read_val)) != sizeof(read_val)) {
return false;
}
out_cnt_ = read_val;
if (out_cnt_ <= 0) {
return false;
}
// set neuron ids and types
for (int idx = 0; idx < neuron_cnt_; idx++) {
neurons_[idx].set_id(idx);
// input type
if (idx < in_cnt_) {
neurons_[idx].set_node_type(Neuron::Input);
} else if (idx >= (neuron_cnt_ - out_cnt_)) {
neurons_[idx].set_node_type(Neuron::Output);
} else {
neurons_[idx].set_node_type(Neuron::Hidden);
}
}
// read the connections
for (int node_idx = 0; node_idx < neuron_cnt_; node_idx++) {
// read fanout
if (input_buff->Read(&read_val, sizeof(read_val)) != sizeof(read_val)) {
return false;
}
// read the neuron's info
int fan_out_cnt = read_val;
for (int fan_out_idx = 0; fan_out_idx < fan_out_cnt; fan_out_idx++) {
// read the neuron id
if (input_buff->Read(&read_val, sizeof(read_val)) != sizeof(read_val)) {
return false;
}
// create the connection
if (!SetConnection(node_idx, read_val)) {
return false;
}
}
}
// read all the neurons' fan-in connections
for (int node_idx = 0; node_idx < neuron_cnt_; node_idx++) {
// read
if (!neurons_[node_idx].ReadBinary(input_buff)) {
return false;
}
}
// size input stats vector to expected input size
inputs_mean_.resize(in_cnt_);
inputs_std_dev_.resize(in_cnt_);
inputs_min_.resize(in_cnt_);
inputs_max_.resize(in_cnt_);
// read stats
if (input_buff->Read(&(inputs_mean_.front()),
sizeof(inputs_mean_[0]) * in_cnt_) !=
sizeof(inputs_mean_[0]) * in_cnt_) {
return false;
}
if (input_buff->Read(&(inputs_std_dev_.front()),
sizeof(inputs_std_dev_[0]) * in_cnt_) !=
sizeof(inputs_std_dev_[0]) * in_cnt_) {
return false;
}
if (input_buff->Read(&(inputs_min_.front()),
sizeof(inputs_min_[0]) * in_cnt_) !=
sizeof(inputs_min_[0]) * in_cnt_) {
return false;
}
if (input_buff->Read(&(inputs_max_.front()),
sizeof(inputs_max_[0]) * in_cnt_) !=
sizeof(inputs_max_[0]) * in_cnt_) {
return false;
}
// create a readonly version for fast feedforward
if (read_only_) {
return CreateFastNet();
}
return true;
}
// creates a connection between two nodes
bool SetConnection(int from, int to);
// Create a read only version of the net that
// has faster feedforward performance
bool CreateFastNet();
// internal function to allocate a new set of weights
// Centralized weight allocation attempts to increase
// weights locality of reference making it more cache friendly
float *AllocWgt(int wgt_cnt);
// different flavors read-only feedforward function
template <typename Type> bool FastFeedForward(const Type *inputs,
Type *outputs);
// Compute the output of a specific output node.
// This function is useful for application that are interested in a single
// output of the net and do not want to waste time on the rest
// This is the fast-read-only version of this function
template <typename Type> bool FastGetNetOutput(const Type *inputs,
int output_id,
Type *output);
};
}
#endif // NEURAL_NET_H__
|