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
* automatically saves the model if the training halts because of an error or if the process is killed
* saves a log if the process dies unexpectedly
* training results and hyper parameters values are recorded to a file
* allows you to define custom stop criteria
* training info is printed at each epoch, including best scores and at which epoch they were achieved
"""
if __name__ == "__main__":
# Let's define the network
ls = MS.GradientDescent(lr=0.01)
cost = MC.NegativeLogLikelihood()
i = ML.Input(28 * 28, name='inp')
h = ML.Hidden(500, activation=MA.Tanh(), initializations=[MI.GlorotTanhInit(), MI.ZeroBias()], regularizations=[MR.L1(0), MR.L2(0.0001)], name="hid")
o = ML.SoftmaxClassifier(10, learningScenario=ls, costObject=cost, name="out", regularizations=[MR.L1(0), MR.L2(0.0001)])
mlp = i > h > o
mlp.saveDOT("mnist_mlp")
mlp.saveHTML("mnist_mlp")
# And then map sets to the inputs and outputs of our network
train_set, validation_set, test_set = load_mnist()
trainData = MDM.Series(images=train_set[0], numbers=train_set[1])
trainMaps = MDM.DatasetMapper("train", miniBatchSize=500)
trainMaps.mapInput(i, trainData.images)
trainMaps.mapOutput(o, trainData.numbers)
testData = MDM.Series(images=test_set[0], numbers=test_set[1])
* training results and hyper parameters values are recorded to a file
* allows you to define custom stop criteria
* training info is printed at each epoch, including best scores and at which epoch they were achieved
"""
if __name__ == "__main__":
# Let's define the network
ls = MS.GradientDescent(lr=0.01)
cost = MC.NegativeLogLikelihood()
i = ML.Input(28 * 28, name='inp')
h = ML.Hidden(500,
activation=MA.Tanh(),
initializations=[MI.GlorotTanhInit()],
regularizations=[MR.L1(0), MR.L2(0.0001)],
name="hid")
o = ML.SoftmaxClassifier(10,
learningScenario=ls,
costObject=cost,
name="out",
regularizations=[MR.L1(0),
MR.L2(0.0001)])
mlp = i > h > o
mlp.saveDOT("mnist_mlp")
mlp.saveHTML("mnist_mlp")
# And then map sets to the inputs and outputs of our network
train_set, validation_set, test_set = load_mnist()