def main():
Renascence.setStreamPath("./")
Renascence.setLibPath("./")
producer = Renascence.init(["func.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('TrBmp', "input.jpg")
x1 = producer.load('TrBmp', "input_sharp.jpg")
x2 = producer.load('TrBmp', "output.jpg")
filterMatrix = producer.build("FR(x0, x1)").run(producer.merge(x2, x1))
print filterMatrix.dump()
[Trainner, BestValue] = producer.train("S(ADF(Treator,x0,x1))",
producer.merge(x0, x1),
5,
cacheFile='temp.txt',
postFormula='FIT(x0, x1)',
postExtraInput=x2)
formula = Trainner.ADF("")
print 'Formula: ', formula
print 'Parameters: ', Trainner.parameters()
print BestValue
predictor = producer.build(formula, Trainner.parameters())
y_p = predictor.run(producer.merge(x0, x1))
y_p.save("output/output_trained.jpg")
def getPieceProducer():
producer = Renascence.init(["./libAbstract_learning.xml"])
print producer
p_producer = Renascence.PieceFunctionProducer(producer, ['./parallel.xml'],
['Map-Reduce.xml'])
print p_producer
sub_p_producer = Renascence.PieceFunctionProducerParallel(
p_producer, 'server')
return sub_p_producer
def main():
producer = Renascence.init(["./libAbstract_learning.xml"])
gd = producer.load('ALGradientMethod', './res/cnn/lenet.json')
#gd = producer.load('ALGradientMethod', './res/cnn/softmax.json')
trainMerge = producer.load('ALFloatMatrix',
'../../machine_exam/handset/train.txt')
trainX = producer.build(
'MatrixLinear(MatrixCrop(x0, x1, x2), x3, x4)').run(
producer.merge(trainMerge, 1.0, -1.0, 1.0 / 255.0, 0.0))
trainX.save('output/handset.trainX.txt')
trainY = producer.build('MatrixCrop(x0, x1, x2)').run(
producer.merge(trainMerge, 0.0, 0.0))
trainY.save('output/handset.trainY.txt')
p = producer.build('ParameterInit(x0)').run(gd)
print trainX, trainY, gd, p
mergeM = producer.build('GDMatrixPrepare(x0, x1, x2)').run(
producer.merge(trainX, trainY, gd))
mergeM.save('output/handset.m')
p = producer.build('GDCompute(x0, x1, x2)').run(
producer.merge(mergeM, gd, p))
p.save('output/handset.p')
#p = producer.load('ALFloatMatrix', 'output/pieces/parameters/ALFloatMatrix_0')
#print p
predictX = producer.load('ALFloatMatrix',
'../../machine_exam/handset/test.txt')
print predictX
predictY = producer.build(
'Classify(GDPredictorLoad(x0, x1), MatrixLinear(x2,x3,x4))').run(
producer.merge(gd, p, predictX, 1.0 / 255.0, 0.0))
predictY.save('output/handset.y')
def main():
Renascence.setStreamPath("../../")
Renascence.setLibPath("../../")
producer = Renascence.init(["func.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('TrBmp', "input.jpg")
x1 = producer.load('TrBmp', "input_sharp.jpg")
x2 = producer.load('TrBmp', "output.jpg")
x0x1x2 = producer.merge(x0, x1, x2)
[Trainner, BestValue] = producer.train("FIT(ADF(Treator,x0,x1), x2)", x0x1x2, 500, cacheFile='temp.txt')
formula = Trainner.ADF("Treator")
print 'Formula: ', formula
print 'Parameters: ', Trainner.parameters()
print BestValue
predictor = producer.build(formula)
x0x1 = producer.merge(x0, x1)
y_p = predictor.run(x0x1)
u = producer.content(9.0)
y_p.save("output/output_trained.jpg")
def main():
Renascence.setStreamPath("./")
Renascence.setLibPath("./")
producer = Renascence.init(["func.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('TrBmp', "input.jpg")
x1 = producer.load('TrBmp', "input_sharp.jpg")
x2 = producer.load('TrBmp', "output.jpg")
filterMatrix = producer.build("FR(x0, x1)").run(producer.merge(x2, x1))
print filterMatrix.dump()
[Trainner, BestValue] = producer.train("S(ADF(Treator,x0,x1))", producer.merge(x0, x1), 5, cacheFile='temp.txt', postFormula='FIT(x0, x1)', postExtraInput=x2)
formula = Trainner.ADF("")
print 'Formula: ', formula
print 'Parameters: ', Trainner.parameters()
print BestValue
predictor = producer.build(formula, Trainner.parameters())
y_p = predictor.run(producer.merge(x0,x1))
y_p.save("output/output_trained.jpg")
def main():
Renascence.setStreamPath("../../")
Renascence.setLibPath("../../")
producer = Renascence.init(["func.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('TrBmp', "input.jpg")
x1 = producer.load('TrBmp', "input_sharp.jpg")
x2 = producer.load('TrBmp', "output.jpg")
x0x1x2 = producer.merge(x0, x1, x2)
[Trainner, BestValue] = producer.train("FIT(ADF(Treator,x0,x1), x2)",
x0x1x2,
500,
cacheFile='temp.txt')
formula = Trainner.ADF("Treator")
print 'Formula: ', formula
print 'Parameters: ', Trainner.parameters()
print BestValue
predictor = producer.build(formula)
x0x1 = producer.merge(x0, x1)
y_p = predictor.run(x0x1)
u = producer.content(9.0)
y_p.save("output/output_trained.jpg")
def main():
Renascence.setStreamPath("./")
Renascence.setLibPath("./")
producer = Renascence.init(["func.xml"])
p_producer = Renascence.PieceFunctionProducer(producer, ['parallel.xml'], ['Map-Reduce.xml'])
print p_producer.listType()
sub_p_producer = Renascence.PieceFunctionProducerParallel(p_producer, 'server')
print "sub_p_producer created"
pfunction = sub_p_producer.createFunction('C(S(x0))', 'TrBmp')
inputPieces = sub_p_producer.createInput('deps/Renascence/res/pictures/', 'TrBmp', [5])
tempPiece = pfunction.run([inputPieces])
outputPieces = sub_p_producer.createOutput('output/pythonTestParallel')
sub_p_producer.copyPiece(tempPiece, outputPieces)
def main():
Renascence.setStreamPath("./")
Renascence.setLibPath("./")
producer = Renascence.init(["func.xml"])
p_producer = Renascence.PieceFunctionProducer(producer, ['mgpfunc.xml'], ['Map-Reduce.xml'])
print p_producer.listType()
sub_p_producer = Renascence.PieceFunctionProducerParallel(p_producer, 'thread')
pfunction = sub_p_producer.createFunction('C(S(x0))', 'TrBmp')
inputPieces = sub_p_producer.createInput('res/pictures/', 'TrBmp', [5])
outputPieces = sub_p_producer.createOutput('output/pythonTestParallel')
tempPiece = pfunction.run([inputPieces])
sub_p_producer.copyPiece(tempPiece, outputPieces)
input1 = inputPieces.read('0')
input1.save('output/pythonTestParallel/input.jpg')
tempPiece.write(input1, '0')
print input1
#!/usr/bin/python
import Renascence
producer = Renascence.init(["./libAbstract_learning.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('ALFloatMatrix', './a1a.tra')
#formula = 'CrossValidate(ADF(GodTrain), Labled(x0, x1))'
#formula = 'C45Tree(x0)'
formula = 'RandomForest(x0)'
result = producer.build(formula).run(x0)
result.save('./.tree')
