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This commit is contained in:
Jim O'Regan 2014-09-12 21:45:02 +01:00
parent 631730599d
commit 9f42f69782
1 changed files with 39 additions and 19 deletions
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58
cube/conv_net_classifier.cpp
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@ -61,22 +61,28 @@ ConvNetCharClassifier::~ConvNetCharClassifier() {
} }
} }
// The main training function. Given a sample and a class ID the classifier /**
// updates its parameters according to its learning algorithm. This function * The main training function. Given a sample and a class ID the classifier
// is currently not implemented. TODO(ahmadab): implement end-2-end training * updates its parameters according to its learning algorithm. This function
* is currently not implemented. TODO(ahmadab): implement end-2-end training
*/
bool ConvNetCharClassifier::Train(CharSamp *char_samp, int ClassID) { bool ConvNetCharClassifier::Train(CharSamp *char_samp, int ClassID) {
return false; return false;
} }
// A secondary function needed for training. Allows the trainer to set the /**
// value of any train-time paramter. This function is currently not * A secondary function needed for training. Allows the trainer to set the
// implemented. TODO(ahmadab): implement end-2-end training * value of any train-time paramter. This function is currently not
* implemented. TODO(ahmadab): implement end-2-end training
*/
bool ConvNetCharClassifier::SetLearnParam(char *var_name, float val) { bool ConvNetCharClassifier::SetLearnParam(char *var_name, float val) {
// TODO(ahmadab): implementation of parameter initializing. // TODO(ahmadab): implementation of parameter initializing.
return false; return false;
} }
// Folds the output of the NeuralNet using the loaded folding sets /**
* Folds the output of the NeuralNet using the loaded folding sets
*/
void ConvNetCharClassifier::Fold() { void ConvNetCharClassifier::Fold() {
// in case insensitive mode // in case insensitive mode
if (case_sensitive_ == false) { if (case_sensitive_ == false) {
@ -125,8 +131,10 @@ void ConvNetCharClassifier::Fold() {
} }
} }
// Compute the features of specified charsamp and feedforward the /**
// specified nets * Compute the features of specified charsamp and feedforward the
* specified nets
*/
bool ConvNetCharClassifier::RunNets(CharSamp *char_samp) { bool ConvNetCharClassifier::RunNets(CharSamp *char_samp) {
if (char_net_ == NULL) { if (char_net_ == NULL) {
fprintf(stderr, "Cube ERROR (ConvNetCharClassifier::RunNets): " fprintf(stderr, "Cube ERROR (ConvNetCharClassifier::RunNets): "
@ -173,7 +181,9 @@ bool ConvNetCharClassifier::RunNets(CharSamp *char_samp) {
return true; return true;
} }
// return the cost of being a char /**
* return the cost of being a char
*/
int ConvNetCharClassifier::CharCost(CharSamp *char_samp) { int ConvNetCharClassifier::CharCost(CharSamp *char_samp) {
if (RunNets(char_samp) == false) { if (RunNets(char_samp) == false) {
return 0; return 0;
@ -181,8 +191,10 @@ int ConvNetCharClassifier::CharCost(CharSamp *char_samp) {
return CubeUtils::Prob2Cost(1.0f - net_output_[0]); return CubeUtils::Prob2Cost(1.0f - net_output_[0]);
} }
// classifies a charsamp and returns an alternate list /**
// of chars sorted by char costs * classifies a charsamp and returns an alternate list
* of chars sorted by char costs
*/
CharAltList *ConvNetCharClassifier::Classify(CharSamp *char_samp) { CharAltList *ConvNetCharClassifier::Classify(CharSamp *char_samp) {
// run the needed nets // run the needed nets
if (RunNets(char_samp) == false) { if (RunNets(char_samp) == false) {
@ -207,7 +219,9 @@ CharAltList *ConvNetCharClassifier::Classify(CharSamp *char_samp) {
return alt_list; return alt_list;
} }
// Set an external net (for training purposes) /**
* Set an external net (for training purposes)
*/
void ConvNetCharClassifier::SetNet(tesseract::NeuralNet *char_net) { void ConvNetCharClassifier::SetNet(tesseract::NeuralNet *char_net) {
if (char_net_ != NULL) { if (char_net_ != NULL) {
delete char_net_; delete char_net_;
@ -216,8 +230,10 @@ void ConvNetCharClassifier::SetNet(tesseract::NeuralNet *char_net) {
char_net_ = char_net; char_net_ = char_net;
} }
// This function will return true if the file does not exist. /**
// But will fail if the it did not pass the sanity checks * This function will return true if the file does not exist.
* But will fail if the it did not pass the sanity checks
*/
bool ConvNetCharClassifier::LoadFoldingSets(const string &data_file_path, bool ConvNetCharClassifier::LoadFoldingSets(const string &data_file_path,
const string &lang, const string &lang,
LangModel *lang_mod) { LangModel *lang_mod) {
@ -284,7 +300,9 @@ bool ConvNetCharClassifier::LoadFoldingSets(const string &data_file_path,
return true; return true;
} }
// Init the classifier provided a data-path and a language string /**
* Init the classifier provided a data-path and a language string
*/
bool ConvNetCharClassifier::Init(const string &data_file_path, bool ConvNetCharClassifier::Init(const string &data_file_path,
const string &lang, const string &lang,
LangModel *lang_mod) { LangModel *lang_mod) {
@ -308,9 +326,11 @@ bool ConvNetCharClassifier::Init(const string &data_file_path,
return true; return true;
} }
// Load the classifier's Neural Nets /**
// This function will return true if the net file does not exist. * Load the classifier's Neural Nets
// But will fail if the net did not pass the sanity checks * This function will return true if the net file does not exist.
* But will fail if the net did not pass the sanity checks
*/
bool ConvNetCharClassifier::LoadNets(const string &data_file_path, bool ConvNetCharClassifier::LoadNets(const string &data_file_path,
const string &lang) { const string &lang) {
string char_net_file; string char_net_file;