hyperparameters["numFeatures"] = len(variables)
data = loadTrainingData(variables, rng, valPart=0.3, verbose=True)
optAlgs = ["RMSProp", "adaDelta", "vSGDfd"]
patience = 12000
validationFrequency = 1
batchSize = int(data[0].get_value(borrow=True).shape[0] / 12.0)
# totNumSamples = 67553
numTrainBatches = data[0].get_value(borrow=True).shape[0] / batchSize
numEpochs = int(patience / numTrainBatches) * 15
hyperparameters["optAlg"] = optAlgs[1]
errorDict = {}
AUCDict = {}
mlp = initalizeModel(**hyperparameters)
score = trainWithEarlyStopping(
mlp,
numEpochs,
patience,
validationFrequency,
data,
variables,
batchSize,
errorDict,
AUCDict,
name=modelName,
visualize=True,
)
print "Score:", score
