self.weights.append(randomInitialWeight())
self.weightDeltas.append(0.0)
# Output Format
def __str__(self):
out = "{" + str(round(self.input,2)) + "["
if self.layer.layerType == NetLayerType.Output:
for w in self.weights:
out = out + str(round(w,2)) + ","
elif self.layer.layerType == NetLayerType.Hidden:
for c in self.center:
out = out + str(round(c,2)) + ","
out = out + "]" + str(round(self.output,2)) + "} "
return out
#Main
if __name__=="__main__":
trainPercentage = 0.8
#p = PatternSet('data/optdigits/optdigits-orig.json', trainPercentage) # 32x32
#p = PatternSet('data/letter/letter-recognition.json', trainPercentage) # 20000 @ 1x16 # Try 1 center per attribute, and allow outputs to combine them
#p = PatternSet('data/pendigits/pendigits.json', trainPercentage) # 10992 @ 1x16 # same as above
#p = PatternSet('data/semeion/semeion.json', trainPercentage) # 1593 @ 16x16 # Training set is very limited
p = PatternSet('data/optdigits/optdigits.json', trainPercentage) # 5620 @ 8x8
n = Net(p)
n.run(PatternType.Train, 0, int(p.count*trainPercentage))
n.run(PatternType.Test, int(p.count*trainPercentage), p.count)
p.printConfusionMatrix()
print("Done")
# strategies = [TS.TrainingStrategyType.EvolutionStrategy, TS.TrainingStrategyType.GeneticAlgorithm]
# Single:
strategies = [TS.TrainingStrategyType.EvolutionStrategy]
# strategies = [TS.TrainingStrategyType.GeneticAlgorithm]
# strategies = [TS.TrainingStrategyType.DifferentialGA]
trainPercentage = 0.8
maxGenerations = 30
populationSize = 20
runsPerDataSet = 5 # 10
# hiddenArchitecture = [14] # each hidden layer is a new index in this list, it's value = number of neurons in that layer
for dataSet in allDataTypes:
for strat in strategies:
p = PatternSet(dataSet) # this is here simply to init the confusion matrix
for run in range(runsPerDataSet):
p = PatternSet(dataSet)
print(
"\nData Set: ("
+ str(dataSet)
+ ") Run: "
+ str(run)
+ " Strategy: "
+ str(TS.TrainingStrategyType.desc(strat))
)
if run == 0:
p.initCombinedConfusionMatrix()
hiddenArchitecture = [
2 * len(p.patterns[0]["p"])
# 'data/zoo/zoo.json',
# 'data/iris/iris.json']
# Single:
# allDataTypes = ['data/ionosphere/ionosphere.json']
# allDataTypes = ['data/block/pageblocks.json']
# allDataTypes = ['data/heart/heart.json']
# allDataTypes = ['data/glass/glass.json']
# allDataTypes = ['data/car/car.json']
# allDataTypes = ['data/seeds/seeds.json']
allDataTypes = ['data/wine/wine.json']
# allDataTypes = ['data/yeast/yeast.json']
# allDataTypes = ['data/zoo/zoo.json']
# allDataTypes = ['data/iris/iris.json']
trainPercentage = 0.8
runsPerDataSet = 10
for dataset in allDataTypes:
p = PatternSet(dataset)
for run in range(runsPerDataSet):
if run == 0:
p.initCombinedConfusionMatrix()
n = Network(p)
n.run(PatternType.Train, 0, int(p.count*trainPercentage))
saveWeights(n)
n.run(PatternType.Test, int(p.count*trainPercentage), p.count)
p.printStats()
p.saveConfusionMatrix()
print("Done")
