def init(img_width,
centroids=[2, 4, 16, 32, 64, 32, 16, 8],
video_file=czm.homeFld + "/Dropbox/destin/moving_square.mov",
temperature=2.0,
learn_rate=0.1,
layer_widths=None):
global network, layers, video_source, layerMask, top_layer
layers = len(centroids)
top_layer = layers - 1
network = pd.DestinNetworkAlt(img_width, layers, centroids, True, 1,
layer_widths)
temps = []
for i in xrange(layers):
temps.append(temperature)
pd.SetLearningStrat(network.getNetwork(), pd.CLS_FIXED)
network.setTemperatures(temps)
network.setFixedLearnRate(learn_rate)
network.setBeliefTransform(pd.DST_BT_P_NORM)
layerMask = pd.SWIG_UInt_Array_frompointer(network.getNetwork().layerMask)
video_source = pd.VideoSource(False, video_file)
video_source.setSize(img_width, img_width)
def test_DestinNetworkAlt(self):
layer_widths=[32, 16, 8, 7, 6, 5, 4, 3, 2, 1]
centroids= [2, 8, 32,32,64,16,16,16,16,4]
network = pd.DestinNetworkAlt(pd.W512,len(layer_widths), centroids, True, 1, layer_widths)
node = pd.GetNodeFromDestin(network.getNetwork(), 0, 1, 1)
self.assertEqual(1, node.nParents)
node = pd.GetNodeFromDestin(network.getNetwork(), 2, 1, 1)
self.assertEqual(4, node.nParents)
def init_destin(siw=pd.W512,
nLayer=8,
centroids=[4, 8, 16, 32, 64, 32, 16, 8],
isUniform=True,
imageMode=pd.DST_IMG_MODE_GRAYSCALE):
temp_network = pd.DestinNetworkAlt(siw, nLayer, centroids, isUniform,
imageMode)
#temp_network.setBeliefTransform(pd.DST_BT_NONE)
return temp_network
ims.addImage(czm.homeFld +
"/Downloads/destin_toshare/train images/%s.png" % l)
if not batch_mode:
centroids = [4, 8, 16, 32, 64, 32, 16, len(letters)]
else:
centroids = [start_centroids for i in range(7)]
centroids.append(len(letters))
layers = len(centroids)
top_layer = layers - 1
draw_layer = top_layer
iterations = 3000
#image_mode = pd.DST_IMG_MODE_RGB
image_mode = pd.DST_IMG_MODE_GRAYSCALE
dn = pd.DestinNetworkAlt(pd.W512, layers, centroids, True, None, image_mode)
dn.setFixedLearnRate(.1)
dn.setBeliefTransform(pd.DST_BT_NONE)
pd.SetLearningStrat(dn.getNetwork(), pd.CLS_FIXED)
if batch_mode:
dn.setFrequencyCoefficients(freq_coeff, freq_treshold, add_coeff)
dn.setStarvationCoefficient(starv_coeff)
dn.setMaximumCentroidCounts(max_centroids)
#dn.setBeliefTransform(pd.DST_BT_P_NORM)
#ut=1.5
#dn.setTemperatures([ut,ut,ut,ut,ut,ut,ut,ut])
weight_exponent = 4
cifar_batch = 1 #which CIFAR batch to use from 1 to 5 cs = pd.CifarSource(cifar_dir, cifar_batch) #must have 4 layers because the cifar data is 32x32 layers = 4 centroids = [7, 5, 5, 5] # How many CIFAR images to train destin with. If larger than # If this this is larger than the number of possible CIFAR images then some # images will be repeated training_iterations = 20000 som_train_iterations = 10000 is_uniform = True # uniform DeSTIN or not dn = pd.DestinNetworkAlt(pd.W32, layers, centroids, is_uniform) # I turned off using previous beliefs in DeSTIN because I dont # think they would be useful in evaluating static images. dn.setParentBeliefDamping(0) dn.setPreviousBeliefDamping(0) # The som trains on concatenated beliefs starting from this layer to the top layer. # If bottom_belief_layer = 0 then it will use all the beliefs from all the layers. # If bottom_belief_layer = 3 then only the top layer's beliefs will be used. bottom_belief_layer = 2 # BeliefExporter - picks which beliefs from destin to show to the SOM be = pd.BeliefExporter(dn, bottom_belief_layer) # How many times at once an individual CIAR image should be shown to destin in one training iteration
g.write("tree #%i:\n%s\n" % (t, tm.getMinedTreeStructureAsString(t)))
f.close()
g.close()
shutil.copy(dst_save_file, out_dir + "network_save.bin")
for i, l in enumerate(letters):
shutil.copy("%s%s.png" % (img_path, l),
out_dir + "input_img_%i.png" % (i))
## Init destin
centroids = [2, 2, 4, 8, 32, 16, 8, 3]
layers = len(centroids)
dn = pd.DestinNetworkAlt(pd.W512, layers, centroids, True)
dn.setBeliefTransform(pd.DST_BT_P_NORM)
#dn.setBeliefTransform(pd.DST_BT_NONE)
#dn.setBeliefTransform(pd.DST_BT_BOLTZ)
uniform_temp = 2
temperatures = []
for i in range(8):
temperatures.append(uniform_temp)
#temperatures = [5, 5, 10, 20, 40, 20, 16, 6]
dn.setTemperatures(temperatures)
dn.setIsPOSTraining(True)
dn.setCentImgWeightExponent(4)
letters = "ABCDE"
for l in letters:
ims.addImage(czm.homeFld +
"/Downloads/destin_toshare/train images/%s.png" % l)
if not batch_mode:
centroids = [4, 8, 16, 32, 64, 32, 16, len(letters)]
else:
centroids = [start_centroids for i in range(7)]
centroids.append(len(letters))
layers = len(centroids)
top_layer = layers - 1
draw_layer = top_layer
iterations = 3000
dn = pd.DestinNetworkAlt(pd.W512, 8, centroids, True)
dn.setFixedLearnRate(.1)
dn.setBeliefTransform(pd.DST_BT_NONE)
pd.SetLearningStrat(dn.getNetwork(), pd.CLS_FIXED)
if batch_mode:
dn.setFrequencyCoefficients(freq_coeff, freq_treshold, add_coeff)
dn.setStarvationCoefficient(starv_coeff)
dn.setMaximumCentroidCounts(max_centroids)
#dn.setBeliefTransform(pd.DST_BT_P_NORM)
#ut=1.5
#dn.setTemperatures([ut,ut,ut,ut,ut,ut,ut,ut])
weight_exponent = 4
def test_tree_sizes(self):
layer_widths=[32, 16, 8, 7, 6, 5, 4, 3, 2, 1]
centroids= [2, 8, 32,32,64,16,16,16,16,4]
network = pd.DestinNetworkAlt(pd.W512,len(layer_widths), centroids, True, 1, layer_widths)
dtm = pd.DestinTreeManager(network, 0)
print "Size is:" + str(dtm.getWinningCentroidTreeSize())
layers = len(centroids)
top_layer = layers - 1
layers_to_enum = {
1: pd.W4,
2: pd.W8,
3: pd.W16,
4: pd.W32,
5: pd.W64,
6: pd.W128,
7: pd.W256,
8: pd.W512}
img_width = layers_to_enum[layers]
dn = pd.DestinNetworkAlt(img_width, layers, centroids, True)
dn.setBeliefTransform(pd.DST_BT_P_NORM)
ct=2.0
dn.setTemperatures([ct,ct,ct,ct,ct,ct,ct,ct])
dn.setFixedLearnRate(.1)
top_node = dn.getNode(top_layer, 0, 0)
vs = pd.VideoSource(False, "hand.m4v")
vs.setSize(img_width, img_width)
vs.enableDisplayWindow()
def init_destin(siw=pd.W512, nLayer=8, centroids=[4,8,16,32,64,32,16,8],
isUniform=True, extRatio=1):
temp_network = pd.DestinNetworkAlt(siw, nLayer, centroids, isUniform, extRatio)
#temp_network.setBeliefTransform(pd.DST_BT_NONE)
return temp_network