def main():
"""Run this experiment"""
training_ints = initialize_instances('./../data/wine_train.csv')
testing_ints = initialize_instances('./../data/wine_test.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
acti = LogisticSigmoid()
rule = RPROPUpdateRule()
# oa_names = ["Backprop"]
classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1, OUTPUT_LAYER],acti)
train(BatchBackPropagationTrainer(data_set,classification_network,measure,rule), classification_network, 'Backprop', training_ints,testing_ints, measure, TRAINING_ITERATIONS, OUTFILE)
def main():
"""Run this experiment"""
training_ints = initialize_instances('./../data/bank_train.csv')
testing_ints = initialize_instances('./../data/bank_test.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
acti = LogisticSigmoid()
rule = RPROPUpdateRule()
classification_network = factory.createClassificationNetwork(
[INPUT_LAYER, HIDDEN_LAYER1, OUTPUT_LAYER], acti)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network,
measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, 'RHC', training_ints, testing_ints,
measure, TRAINING_ITERATIONS, OUTFILE)
def main(T, CE):
"""Run this experiment"""
training_ints = initialize_instances('./../data/wine_train.csv')
testing_ints = initialize_instances('./../data/wine_test.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
acti = LogisticSigmoid()
rule = RPROPUpdateRule()
oa_name = "SA_{}_{}".format(T, CE)
with open(OUTFILE,'w') as f:
f.write('{},{},{},{},{},{}\n'.format('iteration','MSE_trg','MSE_tst','acc_trg','acc_tst','elapsed'))
classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1, OUTPUT_LAYER],acti)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network, measure)
oa = SimulatedAnnealing(T, CE, nnop)
train(oa, classification_network, oa_name, training_ints, testing_ints, measure, TRAINING_ITERATIONS, OUTFILE)