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hybrid_neural_net_classifier.h
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1 /**********************************************************************
2 * File: conv_net_classifier.h
3 * Description: Declaration of Convolutional-NeuralNet Character Classifier
4 * Author: Ahmad Abdulkader
5 * Created: 2007
6 *
7 * (C) Copyright 2008, Google Inc.
8 ** Licensed under the Apache License, Version 2.0 (the "License");
9 ** you may not use this file except in compliance with the License.
10 ** You may obtain a copy of the License at
11 ** http://www.apache.org/licenses/LICENSE-2.0
12 ** Unless required by applicable law or agreed to in writing, software
13 ** distributed under the License is distributed on an "AS IS" BASIS,
14 ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15 ** See the License for the specific language governing permissions and
16 ** limitations under the License.
17 *
18 **********************************************************************/
19
20 #ifndef HYBRID_NEURAL_NET_CLASSIFIER_H
21 #define HYBRID_NEURAL_NET_CLASSIFIER_H
22
23 #include <string>
24 #include <vector>
25
26 #include "char_samp.h"
27 #include "char_altlist.h"
28 #include "char_set.h"
29 #include "classifier_base.h"
30 #include "feature_base.h"
31 #include "lang_model.h"
32 #include "neural_net.h"
33 #include "tuning_params.h"
34
35 namespace tesseract {
36
37 // Folding Ratio is the ratio of the max-activation of members of a folding
38 // set that is used to compute the min-activation of the rest of the set
39 // static const float kFoldingRatio = 0.75; // see conv_net_classifier.h
40
41 class HybridNeuralNetCharClassifier : public CharClassifier {
42 public:
43 HybridNeuralNetCharClassifier(CharSet *char_set, TuningParams *params,
44 FeatureBase *feat_extract);
45 virtual ~HybridNeuralNetCharClassifier();
46 // The main training function. Given a sample and a class ID the classifier
47 // updates its parameters according to its learning algorithm. This function
48 // is currently not implemented. TODO(ahmadab): implement end-2-end training
49 virtual bool Train(CharSamp *char_samp, int ClassID);
50 // A secondary function needed for training. Allows the trainer to set the
51 // value of any train-time paramter. This function is currently not
52 // implemented. TODO(ahmadab): implement end-2-end training
53 virtual bool SetLearnParam(char *var_name, float val);
54 // Externally sets the Neural Net used by the classifier. Used for training
55 void SetNet(tesseract::NeuralNet *net);
56
57 // Classifies an input charsamp and return a CharAltList object containing
58 // the possible candidates and corresponding scores
59 virtual CharAltList *Classify(CharSamp *char_samp);
60 // Computes the cost of a specific charsamp being a character (versus a
61 // non-character: part-of-a-character OR more-than-one-character)
62 virtual int CharCost(CharSamp *char_samp);
63
64 private:
65 // Neural Net object used for classification
66 vector<tesseract::NeuralNet *> nets_;
67 vector<float> net_wgts_;
68
69 // data buffers used to hold Neural Net inputs and outputs
70 float *net_input_;
71 float *net_output_;
72
73 // Init the classifier provided a data-path and a language string
74 virtual bool Init(const string &data_file_path, const string &lang,
75 LangModel *lang_mod);
76 // Loads the NeuralNets needed for the classifier
77 bool LoadNets(const string &data_file_path, const string &lang);
78 // Load folding sets
79 // This function returns true on success or if the file can't be read,
80 // returns false if an error is encountered.
81 virtual bool LoadFoldingSets(const string &data_file_path,
82 const string &lang,
83 LangModel *lang_mod);
84 // Folds the output of the NeuralNet using the loaded folding sets
85 virtual void Fold();
86 // Scales the input char_samp and feeds it to the NeuralNet as input
87 bool RunNets(CharSamp *char_samp);
88 };
89 }
90 #endif // HYBRID_NEURAL_NET_CLASSIFIER_H