def test_dropout(self):
import theano, numpy
inp = ML.Input(100,
'inp',
decorators=[MD.BinomialDropout(dropoutRatio=0.8)])
model = inp.network
model.init()
data = numpy.random.randn(1, 100).astype(theano.config.floatX) + 1
out = model["inp"].propagate["train"]({
"inp.inputs": data
})["inp.propagate.train"]
self.assertTrue(sum(out[0] != 0) < sum(data[0] != 0))
def MLP(ls, cost):
i = ML.Input(28 * 28, name='inp')
h = ML.Hidden(500,
activation=MA.Tanh(),
decorators=[MD.BinomialDropout(0.2)],
initializations=[MI.GlorotTanhInit()],
regularizations=[MR.L1(0), MR.L2(0.0001)],
name="hid")
o = ML.SoftmaxClassifier(10,
initializations=[MI.ZerosWeights()],
learningScenario=ls,
costObject=cost,
name="out",
regularizations=[MR.L1(0),
MR.L2(0.0001)])
mlp = i > h > o
return mlp
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
name = "conv3" ) c2 = MCONV.Convolution2D( nbFilters = 15, filterHeight = 3, filterWidth = 3, activation = MA.Max_norm(), pooler = maxPool, name = "conv2" ) fa = MCONV.Flatten(name="flata") fb = MCONV.Flatten(name="flatb") f = MCONV.Flatten(name = "flat") h = ML.Hidden(2048, activation = MA.Max_norm(), decorators = [MD.BinomialDropout(0.75)], regularizations = [], name = "hid" ) passa = ML.Hidden(1500, activation = MA.Pass(), decorators = [MD.BinomialDropout(0.5)], regularizations = [], name = "pass1" ) passb = ML.Hidden(1500, activation = MA.Pass(), decorators = [MD.BinomialDropout(0.5)], regularizations = [], name = "pass2" ) h2 = ML.Hidden(2048, activation = MA.Max_norm(), decorators = [MD.BinomialDropout(0.75)], regularizations = [], name = "hid2" ) o = ML.SoftmaxClassifier(2, decorators = [], learningScenario = ls, costObject = cost, name = "out", regularizations = [] ) model = i > c1 > c3 > c2 > f > h > h2 > o c1 > fa > passa > h > h2 > o c2 > fb > passb >h > h2 > o tscore = [] vscore = [] tdata = load_data(trainfile) vdata = load_data(validfile) vdata = (center(vdata[0]),vdata[1]) test = load_data(testfile)