Tesseract 3.02

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Classes | Public Member Functions | Static Public Member Functions | Protected Member Functions | Protected Attributes | Static Protected Attributes
tesseract::NeuralNet Class Reference

`#include <neural_net.h>`

List of all members.

## Classes

struct | Node
struct | WeightedNode

## Public Member Functions

| NeuralNet ()
virtual | ~NeuralNet ()
template<typename Type >
bool | FeedForward (const Type *inputs, Type *outputs)
template<typename Type >
bool | GetNetOutput (const Type *inputs, int output_id, Type *output)
int | in_cnt () const
int | out_cnt () const

## Static Public Member Functions

static NeuralNet * | FromFile (const string file_name)
static NeuralNet * | FromInputBuffer (InputFileBuffer *ib)

## Protected Member Functions

void | Init ()
void | Clear ()
template<class ReadBuffType >
bool | ReadBinary (ReadBuffType *input_buff)
bool | SetConnection (int from, int to)
bool | CreateFastNet ()
float * | AllocWgt (int wgt_cnt)
template<typename Type >
bool | FastFeedForward (const Type *inputs, Type *outputs)
template<typename Type >
bool | FastGetNetOutput (const Type *inputs, int output_id, Type *output)

## Protected Attributes

bool | read_only_
int | in_cnt_
int | out_cnt_
int | neuron_cnt_
int | wts_cnt_
Neuron * | neurons_
int | alloc_wgt_cnt_
vector< vector< float > * > | wts_vec_
bool | auto_encoder_
vector< float > | inputs_max_
vector< float > | inputs_min_
vector< float > | inputs_mean_
vector< float > | inputs_std_dev_
vector< Node > | fast_nodes_

## Static Protected Attributes

static const int | kWgtChunkSize = 0x10000
static const unsigned int | kNetSignature = 0xFEFEABD0

## Detailed Description

Definition at line 22 of file neural_net.h.

## Constructor & Destructor Documentation

tesseract::NeuralNet::NeuralNet | ( |  | ) |

Definition at line 15 of file neural_net.cpp.

{
Init();
}

tesseract::NeuralNet::~NeuralNet | ( |  | ) |

virtual

Definition at line 19 of file neural_net.cpp.

{
// clean up the wts chunks vector
for(int vec = 0; vec < wts_vec_.size(); vec++) {
delete wts_vec_[vec];
}
// clean up neurons
delete []neurons_;
// clean up nodes
for (int node_idx = 0; node_idx < neuron_cnt_; node_idx++) {
delete []fast_nodes_[node_idx].inputs;
}
}

## Member Function Documentation

float * tesseract::NeuralNet::AllocWgt | ( | int | wgt_cnt | ) |

protected

Definition at line 189 of file neural_net.cpp.

{
// see if need to allocate a new chunk of wts
if (wts_vec_.size() == 0 || (alloc_wgt_cnt_ + wgt_cnt) > kWgtChunkSize) {
// add the new chunck to the wts_chunks vector
wts_vec_.push_back(new vector<float> (kWgtChunkSize));
alloc_wgt_cnt_ = 0;
}
float *ret_ptr = &((*wts_vec_.back())[alloc_wgt_cnt_]);
// incr usage counts
alloc_wgt_cnt_ += wgt_cnt;
wts_cnt_ += wgt_cnt;
return ret_ptr;
}

void tesseract::NeuralNet::Clear | ( |  | ) |

inline protected

Definition at line 100 of file neural_net.h.

{
for (int node = 0; node < neuron_cnt_; node++) {
neurons_[node].Clear();
}
}

bool tesseract::NeuralNet::CreateFastNet | ( |  | ) |

protected

Definition at line 124 of file neural_net.cpp.

{
fast_nodes_.resize(neuron_cnt_);
// build the node structures
int wts_cnt = 0;
for (int node_idx = 0; node_idx < neuron_cnt_; node_idx++) {
Node *node = &fast_nodes_[node_idx];
if (neurons_[node_idx].node_type() == Neuron::Input) {
// Input neurons have no fan-in
node->fan_in_cnt = 0;
node->inputs = NULL;
// Input bias is the normalization offset computed from
// training input stats
if (fabs(inputs_max_[node_idx] - inputs_min_[node_idx]) <
kMinInputRange) {
// if the range approaches zero, the stdev is not defined,
// this indicates that this input does not change.
// Set the bias to zero
node->bias = 0.0f;
} else {
node->bias = inputs_min_[node_idx] + (inputs_mean_[node_idx] *
(inputs_max_[node_idx] - inputs_min_[node_idx]));
}
} else {
node->bias = neurons_[node_idx].bias();
node->fan_in_cnt = neurons_[node_idx].fan_in_cnt();
// allocate memory for fan-in nodes
node->inputs = new WeightedNode[node->fan_in_cnt];
if (node->inputs == NULL) {
return false;
}
for (int fan_in = 0; fan_in < node->fan_in_cnt; fan_in++) {
// identify fan-in neuron
const int id = neurons_[node_idx].fan_in(fan_in)->id();
// Feedback connections are not allowed and should never happen
if (id >= node_idx) {
return false;
}
// add the the fan-in neuron and its wgt
node->inputs[fan_in].input_node = &fast_nodes_[id];
float wgt_val = neurons_[node_idx].fan_in_wts(fan_in);
// for input neurons normalize the wgt by the input scaling
// values to save time during feedforward
if (neurons_[node_idx].fan_in(fan_in)->node_type() == Neuron::Input) {
// if the range approaches zero, the stdev is not defined,
// this indicates that this input does not change.
// Set the weight to zero
if (fabs(inputs_max_[id] - inputs_min_[id]) < kMinInputRange) {
wgt_val = 0.0f;
} else {
wgt_val /= ((inputs_max_[id] - inputs_min_[id]) *
inputs_std_dev_[id]);
}
}
node->inputs[fan_in].input_weight = wgt_val;
}
// incr wgt count to validate against at the end
wts_cnt += node->fan_in_cnt;
}
}
// sanity check
return wts_cnt_ == wts_cnt;
}
template<typename Type >

template bool tesseract::NeuralNet::FastFeedForward | ( | const Type * | inputs,
|  | Type * | outputs
| ) |  |

protected

Definition at line 52 of file neural_net.cpp.

{
int node_idx = 0;
Node *node = &fast_nodes_[0];
// feed inputs in and offset them by the pre-computed bias
for (node_idx = 0; node_idx < in_cnt_; node_idx++, node++) {
node->out = inputs[node_idx] - node->bias;
}
// compute nodes activations and outputs
for (;node_idx < neuron_cnt_; node_idx++, node++) {
double activation = -node->bias;
for (int fan_in_idx = 0; fan_in_idx < node->fan_in_cnt; fan_in_idx++) {
activation += (node->inputs[fan_in_idx].input_weight *
node->inputs[fan_in_idx].input_node->out);
}
node->out = Neuron::Sigmoid(activation);
}
// copy the outputs to the output buffers
node = &fast_nodes_[neuron_cnt_ - out_cnt_];
for (node_idx = 0; node_idx < out_cnt_; node_idx++, node++) {
outputs[node_idx] = node->out;
}
return true;
}
template<typename Type >

template bool tesseract::NeuralNet::FastGetNetOutput | ( | const Type * | inputs,
|  | int | output_id,
|  | Type * | output
| ) |  |

protected

Definition at line 231 of file neural_net.cpp.

{
// feed inputs in and offset them by the pre-computed bias
int node_idx = 0;
Node *node = &fast_nodes_[0];
for (node_idx = 0; node_idx < in_cnt_; node_idx++, node++) {
node->out = inputs[node_idx] - node->bias;
}
// compute nodes' activations and outputs for hidden nodes if any
int hidden_node_cnt = neuron_cnt_ - out_cnt_;
for (;node_idx < hidden_node_cnt; node_idx++, node++) {
double activation = -node->bias;
for (int fan_in_idx = 0; fan_in_idx < node->fan_in_cnt; fan_in_idx++) {
activation += (node->inputs[fan_in_idx].input_weight *
node->inputs[fan_in_idx].input_node->out);
}
node->out = Neuron::Sigmoid(activation);
}
// compute the output of the required output node
node += output_id;
double activation = -node->bias;
for (int fan_in_idx = 0; fan_in_idx < node->fan_in_cnt; fan_in_idx++) {
activation += (node->inputs[fan_in_idx].input_weight *
node->inputs[fan_in_idx].input_node->out);
}
(*output) = Neuron::Sigmoid(activation);
return true;
}
template<typename Type >

template bool tesseract::NeuralNet::FeedForward | ( | const Type * | inputs,
|  | Type * | outputs
| ) |  |

Definition at line 79 of file neural_net.cpp.

{
// call the fast version in case of readonly nets
if (read_only_) {
return FastFeedForward(inputs, outputs);
}
// clear all neurons
Clear();
// for auto encoders, apply no input normalization
if (auto_encoder_) {
for (int in = 0; in < in_cnt_; in++) {
neurons_[in].set_output(inputs[in]);
}
} else {
// Input normalization : subtract mean and divide by stddev
for (int in = 0; in < in_cnt_; in++) {
neurons_[in].set_output((inputs[in] - inputs_min_[in]) /
(inputs_max_[in] - inputs_min_[in]));
neurons_[in].set_output((neurons_[in].output() - inputs_mean_[in]) /
inputs_std_dev_[in]);
}
}
// compute the net outputs: follow a pull model each output pulls the
// outputs of its input nodes and so on
for (int out = neuron_cnt_ - out_cnt_; out < neuron_cnt_; out++) {
neurons_[out].FeedForward();
// copy the values to the output buffer
outputs[out] = neurons_[out].output();
}
return true;
}

NeuralNet * tesseract::NeuralNet::FromFile | ( | const string | file_name | ) |

static

Definition at line 204 of file neural_net.cpp.

{
// open the file
InputFileBuffer input_buff(file_name);
// create a new net object using input buffer
NeuralNet *net_obj = FromInputBuffer(&input_buff);
return net_obj;
}

NeuralNet * tesseract::NeuralNet::FromInputBuffer | ( | InputFileBuffer * | ib | ) |

static

Definition at line 213 of file neural_net.cpp.

{
// create a new net object
NeuralNet *net_obj = new NeuralNet();
if (net_obj == NULL) {
return NULL;
}
// load the net
if (!net_obj->ReadBinary(ib)) {
delete net_obj;
net_obj = NULL;
}
return net_obj;
}
template<typename Type >

template bool tesseract::NeuralNet::GetNetOutput | ( | const Type * | inputs,
|  | int | output_id,
|  | Type * | output
| ) |  |

Definition at line 265 of file neural_net.cpp.

{
// validate output id
if (output_id < 0 || output_id >= out_cnt_) {
return false;
}
// call the fast version in case of readonly nets
if (read_only_) {
return FastGetNetOutput(inputs, output_id, output);
}
// For the slow version, we'll just call FeedForward and return the
// appropriate output
vector<Type> outputs(out_cnt_);
if (!FeedForward(inputs, &outputs[0])) {
return false;
}
(*output) = outputs[output_id];
return true;
}

int tesseract::NeuralNet::in_cnt | ( |  | ) | const

inline

Definition at line 40 of file neural_net.h.

{ return in_cnt_; }

void tesseract::NeuralNet::Init | ( |  | ) |

protected

Definition at line 34 of file neural_net.cpp.

{
read_only_ = true;
auto_encoder_ = false;
alloc_wgt_cnt_ = 0;
wts_cnt_ = 0;
neuron_cnt_ = 0;
in_cnt_ = 0;
out_cnt_ = 0;
wts_vec_.clear();
neurons_ = NULL;
inputs_mean_.clear();
inputs_std_dev_.clear();
inputs_min_.clear();
inputs_max_.clear();
}

int tesseract::NeuralNet::out_cnt | ( |  | ) | const

inline

Definition at line 41 of file neural_net.h.

{ return out_cnt_; }
template<class ReadBuffType >

template bool tesseract::NeuralNet::ReadBinary | ( | ReadBuffType * | input_buff | ) |

inline protected

Definition at line 106 of file neural_net.h.

{
// 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;
}

bool tesseract::NeuralNet::SetConnection | ( | int | from,
|  | int | to
| ) |  |

protected

Definition at line 112 of file neural_net.cpp.

{
// allocate the wgt
float *wts = AllocWgt(1);
if (wts == NULL) {
return false;
}
// register the connection
neurons_[to].AddFromConnection(neurons_ + from, wts, 1);
return true;
}

## Member Data Documentation

int tesseract::NeuralNet::alloc_wgt_cnt_

protected

Definition at line 81 of file neural_net.h.

bool tesseract::NeuralNet::auto_encoder_

protected

Definition at line 85 of file neural_net.h.

vector<Node> tesseract::NeuralNet::fast_nodes_

protected

Definition at line 96 of file neural_net.h.

int tesseract::NeuralNet::in_cnt_

protected

Definition at line 63 of file neural_net.h.

vector<float> tesseract::NeuralNet::inputs_max_

protected

Definition at line 87 of file neural_net.h.

vector<float> tesseract::NeuralNet::inputs_mean_

protected

Definition at line 91 of file neural_net.h.

vector<float> tesseract::NeuralNet::inputs_min_

protected

Definition at line 89 of file neural_net.h.

vector<float> tesseract::NeuralNet::inputs_std_dev_

protected

Definition at line 93 of file neural_net.h.

const unsigned int tesseract::NeuralNet::kNetSignature = 0xFEFEABD0

static protected

Definition at line 79 of file neural_net.h.

const int tesseract::NeuralNet::kWgtChunkSize = 0x10000

static protected

Definition at line 76 of file neural_net.h.

int tesseract::NeuralNet::neuron_cnt_

protected

Definition at line 67 of file neural_net.h.

Neuron* tesseract::NeuralNet::neurons_

protected

Definition at line 71 of file neural_net.h.

int tesseract::NeuralNet::out_cnt_

protected

Definition at line 65 of file neural_net.h.

bool tesseract::NeuralNet::read_only_

protected

Definition at line 61 of file neural_net.h.

int tesseract::NeuralNet::wts_cnt_

protected

Definition at line 69 of file neural_net.h.

vector<vector<float> *> tesseract::NeuralNet::wts_vec_

protected

Definition at line 83 of file neural_net.h.

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

- /mnt/data/src/tesseract-ocr/neural_networks/runtime/neural_net.h
- /mnt/data/src/tesseract-ocr/neural_networks/runtime/neural_net.cpp