def train_destin(destin_train_iterations, train_only_layer=-1):
# only enable one layer if specified
if train_only_layer != -1:
for i in xrange(layers):
dn.setLayerIsTraining(i, False)
dn.setLayerIsTraining(train_only_layer, True)
for i in range(destin_train_iterations):
if i % 100 == 0:
print "Training DeSTIN iteration: " + str(i)
#chart.update([dn.getQuality(top_layer), dn.getVar(top_layer), dn.getSep(top_layer)])
#chart.update([dn.getVar(top_layer), dn.getSep(top_layer)])
#report_layer = 0
#variance = dn.getVar(report_layer)
#seperation = dn.getSep(report_layer)
#quality = dn.getQuality(report_layer)
#qual_moving_average = moving_average(quality)
#chart.update([variance, seperation])
chart.update(dn.getLayersQualities())
if i%200 == 0:
chart.draw()
# print "Qual: %f, Variance: %f, seperation: %f, average: %f" % (quality, variance, seperation, qual_moving_average)
#find an image of an enabled class
cs.findNextImage()
dn.doDestin(getCifarFloatImage(cs))
dn.save( experiment_save_dir+"/network_"+run_id+".dst")
def train():
for i in xrange(iterations):
if i % 10 == 0:
variances = dn.getLayersVariances()
separations = dn.getLayersSeparations()
chart.update([
separations[draw_layer] - variances[draw_layer],
variances[draw_layer], separations[draw_layer]
])
chart.draw()
if (i % 300) == 0:
print "#####################################"
print "Iteration " + str(i)
print ""
for j in range(len(centroids)):
print "Layer: " + str(j)
print "Variance: " + str(variances[j])
print "Separation: " + str(separations[j])
print "Quality: " + str(separations[j] - variances[j])
print "Centroids: " + str(dn.getBeliefsPerNode(j))
print ""
sys.stdout.flush()
ims.findNextImage()
#dn.clearBeliefs()
for j in range(1):
f = ims.getGrayImageFloat()
dn.doDestin(f)
def train_destin(destin_train_iterations, train_only_layer=-1):
# only enable one layer if specified
if train_only_layer != -1:
for i in xrange(layers):
dn.setLayerIsTraining(i, False)
dn.setLayerIsTraining(train_only_layer, True)
for i in range(destin_train_iterations):
if i % 100 == 0:
print "Training DeSTIN iteration: " + str(i)
#chart.update([dn.getQuality(top_layer), dn.getVar(top_layer), dn.getSep(top_layer)])
#chart.update([dn.getVar(top_layer), dn.getSep(top_layer)])
#report_layer = 0
#variance = dn.getVar(report_layer)
#seperation = dn.getSep(report_layer)
#quality = dn.getQuality(report_layer)
#qual_moving_average = moving_average(quality)
#chart.update([variance, seperation])
chart.update(dn.getLayersQualities())
if i % 200 == 0:
chart.draw()
# print "Qual: %f, Variance: %f, seperation: %f, average: %f" % (quality, variance, seperation, qual_moving_average)
#find an image of an enabled class
cs.findNextImage()
dn.doDestin(getCifarFloatImage(cs))
dn.save(experiment_save_dir + "/network_" + run_id + ".dst")
def train():
for i in xrange(iterations):
if i % 10 == 0:
variances = dn.getLayersVariances();
separations = dn.getLayersSeparations();
chart.update([separations[draw_layer] - variances[draw_layer], variances[draw_layer], separations[draw_layer]])
chart.draw()
if (i % 300) == 0:
print "#####################################";
print "Iteration " + str(i)
print ""
for j in range(len(centroids)):
print "Layer: " + str(j)
print "Variance: " + str(variances[j])
print "Separation: " + str(separations[j])
print "Quality: " + str(separations[j] - variances[j])
print "Centroids: " + str(dn.getBeliefsPerNode(j))
print ""
sys.stdout.flush()
ims.findNextImage()
#dn.clearBeliefs()
for j in range(1):
f = ims.getGrayImageFloat()
dn.doDestin(f)
def train():
for i in xrange(iterations):
if i % 10 == 0:
variances = dn.getLayersVariances();
separations = dn.getLayersSeparations();
chart.update([separations[draw_layer] - variances[draw_layer], variances[draw_layer], separations[draw_layer]])
chart.draw()
if (i % 300) == 0:
print "#####################################";
print "Iteration " + str(i)
print ""
for j in range(len(centroids)):
print "Layer: " + str(j)
print "Variance: " + str(variances[j])
print "Separation: " + str(separations[j])
print "Quality: " + str(separations[j] - variances[j])
print "Centroids: " + str(dn.getBeliefsPerNode(j))
print ""
sys.stdout.flush()
ims.findNextImage()
#dn.clearBeliefs()
for j in range(1):
if image_mode == pd.DST_IMG_MODE_GRAYSCALE:
f = ims.getGrayImageFloat()
elif image_mode == pd.DST_IMG_MODE_RGB:
f = ims.getRGBImageFloat()
else:
raise Exception("unsupported image mode")
dn.doDestin(f)
def train():
for i in xrange(iterations):
if i % 10 == 0:
variances = dn.getLayersVariances()
separations = dn.getLayersSeparations()
chart.update([
separations[draw_layer] - variances[draw_layer],
variances[draw_layer], separations[draw_layer]
])
chart.draw()
if (i % 300) == 0:
print "#####################################"
print "Iteration " + str(i)
print ""
for j in range(len(centroids)):
print "Layer: " + str(j)
print "Variance: " + str(variances[j])
print "Separation: " + str(separations[j])
print "Quality: " + str(separations[j] - variances[j])
print "Centroids: " + str(dn.getBeliefsPerNode(j))
print ""
sys.stdout.flush()
ims.findNextImage()
#dn.clearBeliefs()
for j in range(1):
if image_mode == pd.DST_IMG_MODE_GRAYSCALE:
f = ims.getGrayImageFloat()
elif image_mode == pd.DST_IMG_MODE_RGB:
f = ims.getRGBImageFloat()
else:
raise Exception("unsupported image mode")
dn.doDestin(f)
def train():
for i in xrange(4000):
if i % 10 == 0:
print "Iteration " + str(i)
var = dn.getVar(top_layer)
print "Variance: " +str(var)
sep = dn.getSep(top_layer)
print "Sep: " + str(sep)
qual = dn.getQuality(top_layer)
print "Quality: " + str(qual)
chart.update([qual, var, sep])
chart.draw()
#code.interact(local=locals())
ims.findNextImage()
#dn.clearBeliefs()
for j in range(1):
f = ims.getGrayImageFloat()
dn.doDestin(f)
def run(self):
chart.draw()
def run(self):
chart.draw()
