work on MLP sample in letter_recog.py (in progress...)

samples/python2/letter_recog.py

@@ -7,12 +7,28 @@ def load_base(fn):
     return samples, responses
 
 class LetterStatModel(object):
+    class_n = 26
     train_ratio = 0.5
+
     def load(self, fn):
         self.model.load(fn)
     def save(self, fn):
         self.model.save(fn)
     
+    def unroll_samples(self, samples):
+        sample_n, var_n = samples.shape
+        new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
+        new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)
+        new_samples[:,-1] = np.tile(np.arange(self.class_n), sample_n)
+        return new_samples
+    
+    def unroll_responses(self, responses):
+        sample_n = len(responses)
+        new_responses = np.zeros(sample_n*self.class_n, np.int32)
+        resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
+        new_responses[resp_idx] = 1
+        return new_responses
+
 class RTrees(LetterStatModel):
     def __init__(self):
         self.model = cv2.RTrees()
@@ -43,7 +59,6 @@ class KNearest(LetterStatModel):
 class Boost(LetterStatModel):
     def __init__(self):
         self.model = cv2.Boost()
-        self.class_n = 26
     
     def train(self, samples, responses):
         sample_n, var_n = samples.shape
@@ -60,20 +75,6 @@ class Boost(LetterStatModel):
         pred = pred.reshape(-1, self.class_n).argmax(1)
         return pred
 
-    def unroll_samples(self, samples):
-        sample_n, var_n = samples.shape
-        new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
-        new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)
-        new_samples[:,-1] = np.tile(np.arange(self.class_n), sample_n)
-        return new_samples
-    
-    def unroll_responses(self, responses):
-        sample_n = len(responses)
-        new_responses = np.zeros(sample_n*self.class_n, np.int32)
-        resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
-        new_responses[resp_idx] = 1
-        return new_responses
-
 
 class SVM(LetterStatModel):
     train_ratio = 0.1
@@ -89,12 +90,36 @@ class SVM(LetterStatModel):
     def predict(self, samples):
         return np.float32( [self.model.predict(s) for s in samples] )
 
+class MLP(LetterStatModel):
+    def __init__(self):
+        self.model = cv2.ANN_MLP()
+
+    def train(self, samples, responses):
+        sample_n, var_n = samples.shape
+        new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)
+
+        layer_sizes = np.int32([var_n, 100, 100, self.class_n])
+        self.model.create(layer_sizes)
+        
+        # CvANN_MLP_TrainParams::BACKPROP,0.001
+        params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
+                       train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP, 
+                       bp_dw_scale = 0.001,
+                       bp_moment_scale = 0.0 )
+        self.model.train(samples, np.float32(new_responses), None, params = params)
+
+    def predict(self, samples):
+        pass
+        #return np.float32( [self.model.predict(s) for s in samples] )
+
+
+
 
 if __name__ == '__main__':
     import getopt
     import sys
 
-    models = [RTrees, KNearest, Boost, SVM] # MLP, NBayes
+    models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
     models = dict( [(cls.__name__.lower(), cls) for cls in models] )
 
     print 'USAGE: letter_recog.py [--model <model>] [--data <data fn>] [--load <model fn>] [--save <model fn>]'