def ae1(data):
'''Using a regression layer. This layer needs an explicit target'''
miniBatchSize = 2
ls = MS.GradientDescent(lr=0.1)
cost = MC.MeanSquaredError()
i = ML.Input(8, name='inp')
h = ML.Hidden(3,
activation=MA.ReLU(),
initializations=[MI.SmallUniformWeights(),
MI.ZeroBias()],
name="hid")
o = ML.Regression(
8,
activation=MA.ReLU(),
initializations=[MI.SmallUniformWeights(),
MI.ZeroBias()],
learningScenario=ls,
costObject=cost,
name="out")
ae = i > h > o
for e in xrange(1000):
for i in xrange(0, len(data), miniBatchSize):
ae.train(o,
inp=data[i:i + miniBatchSize],
targets=data[i:i + miniBatchSize])
return ae, o
def ae2(data):
"""This one uses an Autoencode layer. This layer is a part of the graph and does not need a specific traget"""
miniBatchSize = 1
ls = MS.GradientDescent(lr=0.1)
cost = MC.MeanSquaredError()
i = ML.Input(8, name='inp')
h = ML.Hidden(3,
activation=MA.ReLU(),
initializations=[MI.SmallUniformWeights(),
MI.ZeroBias()],
name="hid")
o = ML.Autoencode(
i.name,
activation=MA.ReLU(),
initializations=[MI.SmallUniformWeights(),
MI.ZeroBias()],
learningScenario=ls,
costObject=cost,
name="out")
ae = i > h > o
# ae.init()
# o.train.printGraph()
for e in xrange(2000):
for i in xrange(0, len(data), miniBatchSize):
ae.train(o, inp=data[i:i + miniBatchSize])
return ae, o
def test_ae_reg(self) :
data = []
for i in xrange(8) :
zeros = numpy.zeros(8)
zeros[i] = 1
data.append(zeros)
ls = MS.GradientDescent(lr = 0.1)
cost = MC.MeanSquaredError()
i = ML.Input(8, name = 'inp')
h = ML.Hidden(3, activation = MA.ReLU(), initializations=[MI.SmallUniformWeights(), MI.ZeroBias()], name = "hid")
o = ML.Regression(8, activation = MA.ReLU(), initializations=[MI.SmallUniformWeights(), MI.ZeroBias()], learningScenario = ls, costObject = cost, name = "out" )
ae = i > h > o
miniBatchSize = 1
for e in xrange(2000) :
for i in xrange(0, len(data), miniBatchSize) :
ae.train(o, inp = data[i:i+miniBatchSize], targets = data[i:i+miniBatchSize] )
res = ae.propagate(o, inp = data)["outputs"]
for i in xrange(len(res)) :
self.assertEqual( numpy.argmax(data[i]), numpy.argmax(res[i]))
def __init__(self, WInitialization=MI.SmallUniformWeights(), bInitialization=MI.ZeroBias(), epsilon=1e-6) : Decorator_ABC.__init__(self) self.epsilon = epsilon self.WInitialization = WInitialization self.bInitialization = bInitialization self.W = None self.b = None self.paramShape = None
def __init__(self,
size,
layerTypes,
initializations=[MI.SmallUniformWeights(),
MI.ZeroBias()],
**kwargs):
super(WeightBias_ABC, self).__init__(size,
layerTypes=layerTypes,
initializations=initializations,
**kwargs)
self.testInputs = None
self.parameters = {"W": None, "b": None}
def __init__(self,
size,
layerType,
initializations=[MI.SmallUniformWeights(),
MI.ZerosBias()],
**kwargs):
super(WeightBias_ABC, self).__init__(size,
layerType=layerType,
initializations=initializations,
**kwargs)
self.W = None
self.b = None
def __init__(self,
WInitialization=MI.SmallUniformWeights(),
bInitialization=MI.ZeroBias(),
epsilon=1e-6,
onTrain=True,
onTest=True):
Decorator_ABC.__init__(self)
self.epsilon = epsilon
self.WInitialization = WInitialization
self.bInitialization = bInitialization
self.W = None
self.b = None
self.paramShape = None
self.onTrain = onTrain
self.onTest = onTest
self.hyperParameters.extend(["onTrain", "onTest", "epsilon"])