def train(self):
""" Store a copy of every image in the iterator. """
Experiment.train(self)
start = time.time()
gabor = GaborRegion((144, 192), rotations=3,
initial_wavelength=3,
num_wavelengths=2)
# Regions = [ GaborRegion, AndRegion, OrRegion (classifier) ]
self.network = AndOrNetwork((144,192), num_regions=1,
input_region = gabor)
and_region = self.network.regions[1]
classifier = self.network.get_classifier()
i = 0
while self.image_iterator.has_next():
image, category, img_idx = self.image_iterator.next()
gabor.do_inference(numpy.array(image))
active_nodes = gabor.get_active_nodes()
pos = and_region.create_node((0,0), cxns = active_nodes)
classifier.create_node(category, pos)
i += 1
if i % self.PRINT_INCR == 0: print "Iter:", i
and_region.prepare_for_inference()
classifier.prepare_for_inference()
num_cxns = and_region.get_num_cxns() + classifier.get_num_cxns()
print "Number of connections:", num_cxns
elapsed = (time.time() - start)
print "Training time:", elapsed
print "Time per category:", (elapsed / i)
print colored("Training complete", "green")
