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

This commit is contained in:
Alexander Mordvintsev 2011-08-14 02:26:47 +00:00
parent 638f3d31cf
commit 622bd42224

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@ -7,11 +7,27 @@ 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):
@ -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>]'