def train(self):
""" Store a copy of every image in the iterator. """
Experiment.train(self)
start = time.time()
num_images = len(self.image_iterator)
print "Num images:", num_images
assert num_images > 0
gabor = self.gabor_region = GaborRegion((144, 192), rotations=3,
initial_wavelength=3,
num_wavelengths=2)
kmeans = KmeansRegion(max(1,int(float(num_images) * self.compression)),
self.window_sampler)
and_region = AndOrRegion(kmeans.image_shape, num_images)
# Regions = [ GaborRegion, Kmeans, AndRegion, OrRegion (classifier) ]
self.network = AndOrNetwork([gabor, kmeans, and_region])
classifier = self.network.get_classifier()
self.categories = []
print "Extracting windows..."
i = 0
while self.image_iterator.has_next():
image, category, img_idx = self.image_iterator.next()
self.categories.append(category)
gabor.do_inference(numpy.array(image))
kmeans.do_learning()
i += 1
print "Training K-means..."
kmeans.prepare_for_inference()
# Send all of the images through the feature learner to save the image
# activations.
print "Storing classifier features..."
self.image_iterator.reset()
while self.image_iterator.has_next():
image, category, img_idx = self.image_iterator.next()
gabor.do_inference(numpy.array(image))
kmeans.do_inference()
cxns = kmeans.get_active_nodes()
pos = and_region.create_node((0,0), cxns = cxns)
classifier.create_node(category, pos)
print "Preparing for inference..."
self.network.prepare_for_inference(2)
