def trainNetwork(net, networkFile="mnist_net.nta"):
# Some stuff we will need later
sensor = net.regions['sensor']
sp = net.regions["SP"]
pysp = sp.getSelf()
classifier = net.regions['classifier']
dutyCycles = numpy.zeros(DEFAULT_SP_PARAMS['columnCount'], dtype=GetNTAReal())
print "============= Loading training images ================="
t1 = time.time()
sensor.executeCommand(["loadMultipleImages", "mnist/training"])
numTrainingImages = sensor.getParameter('numImages')
start = time.time()
print "Load time for training images:",start-t1
print "Number of training images",numTrainingImages
# First train just the SP
print "============= SP training ================="
classifier.setParameter('inferenceMode', 0)
classifier.setParameter('learningMode', 0)
sp.setParameter('learningMode', 0)
sp.setParameter('inferenceMode', 1)
nTrainingIterations = numTrainingImages
for i in range(nTrainingIterations):
net.run(1)
dutyCycles += pysp._spatialPoolerOutput
if i%(nTrainingIterations/100)== 0:
print "Iteration",i,"Category:",sensor.getOutputData('categoryOut')
# Now train just the classifier sequentially on all training images
print "============= Classifier training ================="
sensor.setParameter('explorer','Flash')
classifier.setParameter('inferenceMode', 0)
classifier.setParameter('learningMode', 1)
sp.setParameter('learningMode', 0)
sp.setParameter('inferenceMode', 1)
for i in range(numTrainingImages):
net.run(1)
if i%(numTrainingImages/100)== 0:
print "Iteration",i,"Category:",sensor.getOutputData('categoryOut')
# Save the trained network
net.save(networkFile)
# Print various statistics
print "============= Training statistics ================="
print "Training time:",time.time() - start
tenPct= nTrainingIterations/10
print "My duty cycles:",fdrutilities.numpyStr(dutyCycles, format="%g")
print "Number of nonzero duty cycles:",len(dutyCycles.nonzero()[0])
print "Mean/Max duty cycles:",dutyCycles.mean(), dutyCycles.max()
print "Number of columns that won for > 10% patterns",\
(dutyCycles>tenPct).sum()
print "Number of columns that won for > 20% patterns",\
(dutyCycles>2*tenPct).sum()
print "Num categories learned",classifier.getParameter('categoryCount')
print "Number of patterns stored",classifier.getParameter('patternCount')
return net
def trainNetwork(net):
# Some stuff we will need later
sensor = net.regions['sensor']
sp = net.regions["SP"]
pysp = sp.getSelf()
classifier = net.regions['classifier']
dutyCycles = numpy.zeros(DEFAULT_SP_PARAMS['columnCount'], dtype=GetNTAReal())
print "============= Loading training images ================="
t1 = time.time()
sensor.executeCommand(["loadMultipleImages", "mnist/training"])
numTrainingImages = sensor.getParameter('numImages')
start = time.time()
print "Load time for training images:",start-t1
print "Number of training images",numTrainingImages
# First train just the SP
print "============= SP training ================="
classifier.setParameter('inferenceMode', 0)
classifier.setParameter('learningMode', 0)
sp.setParameter('learningMode', 0)
sp.setParameter('inferenceMode', 1)
nTrainingIterations = numTrainingImages
for i in range(nTrainingIterations):
net.run(1)
dutyCycles += pysp._spatialPoolerOutput
if i%(nTrainingIterations/100)== 0:
print "Iteration",i,"Category:",sensor.getOutputData('categoryOut')
# Now train just the classifier sequentially on all training images
print "============= Classifier training ================="
sensor.setParameter('explorer','Flash')
classifier.setParameter('inferenceMode', 0)
classifier.setParameter('learningMode', 1)
sp.setParameter('learningMode', 0)
sp.setParameter('inferenceMode', 1)
for i in range(numTrainingImages):
net.run(1)
if i%(numTrainingImages/100)== 0:
print "Iteration",i,"Category:",sensor.getOutputData('categoryOut')
# Save the trained network
net.save("mnist_net.nta")
# Print various statistics
print "============= Training statistics ================="
print "Training time:",time.time() - start
tenPct= nTrainingIterations/10
print "My duty cycles:",fdrutilities.numpyStr(dutyCycles, format="%g")
print "Number of nonzero duty cycles:",len(dutyCycles.nonzero()[0])
print "Mean/Max duty cycles:",dutyCycles.mean(), dutyCycles.max()
print "Number of columns that won for > 10% patterns",\
(dutyCycles>tenPct).sum()
print "Number of columns that won for > 20% patterns",\
(dutyCycles>2*tenPct).sum()
print "Num categories learned",classifier.getParameter('categoryCount')
print "Number of patterns stored",classifier.getParameter('patternCount')
return net