def getModel(inpSize, filterWidth) :
ls = MS.GradientDescent(lr = 0.5)
cost = MC.NegativeLogLikelihood()
pooler = MCONV.MaxPooling2D(1, 2)
i = ML.Input(inpSize, name = 'inp')
ichan = MCONV.InputChanneler(1, inpSize, name = 'inpChan')
c1 = MCONV.Convolution2D(
nbFilters = 5,
filterHeight = 1,
filterWidth = filterWidth,
activation = MA.ReLU(),
pooler = pooler,
name = "conv1"
)
c2 = MCONV.Convolution2D(
nbFilters = 10,
filterHeight = 1,
filterWidth = filterWidth,
activation = MA.ReLU(),
pooler = pooler,
name = "conv2"
)
f = MCONV.Flatten(name = "flat")
h = ML.Hidden(5, activation = MA.ReLU(), decorators = [], regularizations = [ ], name = "hid" )
o = ML.SoftmaxClassifier(2, decorators = [], learningScenario = ls, costObject = cost, name = "out", regularizations = [ ] )
model = i > ichan > c1 > c2 > f > h > o
return model
def __init__(self, ls, cost):
maxPool = MCONV.MaxPooling2D(2, 2)
#The input channeler will take regular layers and arrange them into several channels
i = MCONV.Input(nbChannels=3, height=256, width=256, name='inp')
#ichan = MCONV.InputChanneler(256, 256, name = 'inpChan')
c1 = MCONV.Convolution2D(nbFilters=3,
filterHeight=5,
filterWidth=5,
activation=MA.Tanh(),
pooler=maxPool,
name="conv1")
c2 = MCONV.Convolution2D(nbFilters=3,
filterHeight=5,
filterWidth=5,
activation=MA.Tanh(),
pooler=maxPool,
name="conv2")
f = MCONV.Flatten(name="flat")
h = ML.Hidden(5,
activation=MA.Tanh(),
decorators=[MD.GlorotTanhInit()],
regularizations=[MR.L1(0), MR.L2(0.0001)],
name="hid")
o = ML.SoftmaxClassifier(2,
decorators=[],
learningScenario=ls,
costObject=cost,
name="out",
regularizations=[])
self.model = i > c1 > c2 > f > h > o
def __init__(self, ls, cost):
maxPool = MCONV.MaxPooling2D(2, 2)
i = MCONV.Input(nbChannels=1, height=28, width=28, name='inp')
c1 = MCONV.Convolution2D(nbFilters=20,
filterHeight=5,
filterWidth=5,
activation=MA.Tanh(),
pooler=maxPool,
name="conv1")
c2 = MCONV.Convolution2D(nbFilters=50,
filterHeight=5,
filterWidth=5,
activation=MA.Tanh(),
pooler=maxPool,
name="conv2")
#needed for the transition to a fully connected layer
f = MCONV.Flatten(name="flat")
h = ML.Hidden(500,
activation=MA.Tanh(),
decorators=[],
regularizations=[],
name="hid")
o = ML.SoftmaxClassifier(10,
decorators=[],
learningScenario=ls,
costObject=cost,
name="out",
regularizations=[])
self.model = i > c1 > c2 > f > h > o
print self.model
def __init__(self, ls, cost):
maxPool = MCONV.MaxPooling2D(3, 3)
i = MCONV.Input(nbChannels=1, height=100, width=100, name='inp')
c1 = MCONV.Convolution2D(nbFilters=10,
filterHeight=3,
filterWidth=3,
activation=MA.Max_norm(),
pooler=maxPool,
name="conv1")
c3 = MCONV.Convolution2D(nbFilters=20,
filterHeight=3,
filterWidth=3,
activation=MA.Max_norm(),
pooler=maxPool,
name="conv3")
c2 = MCONV.Convolution2D(nbFilters=10,
filterHeight=3,
filterWidth=3,
activation=MA.Max_norm(),
pooler=maxPool,
name="conv2")
f = MCONV.Flatten(name="flat")
h = ML.Hidden(2048,
activation=MA.Max_norm(),
decorators=[MD.BinomialDropout(0.7)],
regularizations=[],
name="hid")
o = ML.SoftmaxClassifier(2,
decorators=[],
learningScenario=ls,
costObject=cost,
name="out",
regularizations=[])
self.model = i > c1 > c3 > c2 > f > h > o
def __init__(self, inputSize, dictSize, patternSize, embSize, ls, cost):
# pooler = MCONV.NoPooling()
pooler = MCONV.MaxPooling2D(1, 2)
emb = MCONV.Embedding(size=inputSize,
nbDimentions=embSize,
dictSize=dictSize,
name='Emb')
c1 = MCONV.Convolution2D(nbFilters=1,
filterHeight=1,
filterWidth=patternSize / 2,
activation=MA.ReLU(),
pooler=pooler,
name="conv1")
c2 = MCONV.Convolution2D(nbFilters=4,
filterHeight=1,
filterWidth=patternSize / 2,
activation=MA.ReLU(),
pooler=MCONV.NoPooling(),
name="conv2")
f = MCONV.Flatten(name="flat")
h = ML.Hidden(5,
activation=MA.ReLU(),
decorators=[],
regularizations=[],
name="hid")
o = ML.SoftmaxClassifier(2,
decorators=[],
learningScenario=ls,
costObject=cost,
name="out",
regularizations=[])
self.model = emb > c1 > c2 > f > h > o
def __init__(self, inputSize, patternSize, ls, cost):
# pooler = MCONV.NoPooling()
pooler = MCONV.MaxPooling2D(1, 2)
#The input channeler will take regular layers and arrange them into several channels
i = ML.Input(inputSize, name='inp')
ichan = MCONV.InputChanneler(1, inputSize, name='inpChan')
c1 = MCONV.Convolution2D(nbFilters=5,
filterHeight=1,
filterWidth=patternSize / 2,
activation=MA.ReLU(),
pooler=pooler,
name="conv1")
c2 = MCONV.Convolution2D(nbFilters=10,
filterHeight=1,
filterWidth=patternSize / 2,
activation=MA.ReLU(),
pooler=MCONV.NoPooling(),
name="conv2")
f = MCONV.Flatten(name="flat")
h = ML.Hidden(5,
activation=MA.ReLU(),
decorators=[],
regularizations=[],
name="hid")
o = ML.SoftmaxClassifier(2,
decorators=[],
learningScenario=ls,
costObject=cost,
name="out",
regularizations=[])
self.model = i > ichan > c1 > c2 > f > h > o
def load_data(picklefile):
tbl = pickle.load(open(picklefile,"rb"))
return tbl
def center(batch):
mean = numpy.mean(batch)
sd = numpy.std(batch)
return (batch-mean)/sd
def classes(targets):
x = [numpy.abs(numpy.argmax(i)-1) for i in targets[0]]
return x
maxPool = MCONV.MaxPooling2D(2,2)
ls = MS.MomentumGradientDescent(lr = 1e-1, momentum = 0.95)
cost = MC.NegativeLogLikelihood()
miniBatchSize = 100
trainfile = "../flip_grey_train_dataset.p"
validfile = "../flip_grey_valid_dataset.p"
testfile = "../test_set.p"
runprefix = "HD2"
i = MCONV.Input(nbChannels = 1, height = 100, width = 100, name = 'inp')
c1 = MCONV.Convolution2D(
nbFilters = 15,
filterHeight = 3,
filterWidth = 3,