def __init__(self,
activation=MA.Pass(),
regularizations=[],
initializations=[],
learningScenari=[],
decorators=[],
name=None,
maxInConnections=1,
streams=["train", "test"]):
super(Layer_ABC, self).__init__(initializations=[], streams=streams)
self.maxInConnections = maxInConnections
#a unique tag associated to the layer
self.appelido = str(uuid.uuid1())
if name is not None:
self.name = name
else:
self.name = "%s_%s" % (self.__class__.__name__, self.appelido)
self.inputs = MTYPES.Variable(streams=self.streams)
self.outputs = MTYPES.Variable(streams=self.streams)
self.outputs_preactivation = None
self.abstractions = {
"activation": [activation],
"regularizations": regularizations,
"decorators": decorators,
"initializations": initializations,
"learningScenari": learningScenari,
}
self._inputRegistrations = set()
self._resetNetwork()
def __init__(self,
size,
layerTypes,
activation=MA.Pass(),
regularizations=[],
initializations=[],
learningScenario=None,
decorators=[],
name=None):
self.isLayer = True
#a unique tag associated to the layer
self.appelido = numpy.random.random()
if name is not None:
self.name = name
else:
self.name = "%s_%s" % (self.__class__.__name__, self.appelido)
self.types = layerTypes
self.nbInputs = None
self.inputs = None
self.nbOutputs = size
self.outputs = None # this is a symbolic var
self.testOutputs = None # this is a symbolic var
self.preactivation_outputs = None
self.preactivation_testOutputs = None
self.activation = activation
self.regularizationObjects = regularizations
self.regularizations = []
self.decorators = decorators
self.initializations = initializations
self.learningScenario = learningScenario
self.network = MNET.Network()
self.network._addLayer(self)
self._inputRegistrations = set()
self._mustInit = True
self._mustReset = True
self._decorating = False
self.parameters = {}
) 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) test = center(test)