def main(P, mate, mutate, layers, training_iterations, test_data_file, train_data_file, validate_data_file):
"""Run this experiment"""
training_ints = base.initialize_instances(train_data_file)
testing_ints = base.initialize_instances(test_data_file)
validation_ints = base.initialize_instances(validate_data_file)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
# relu = RELU()
relu = LogisticSigmoid()
# 50 and 0.000001 are the defaults from RPROPUpdateRule.java
rule = RPROPUpdateRule(0.064, 50, 0.000001)
oa_name = "GA_{}_{}_{}".format(P, mate, mutate)
with open(OUTFILE.format(oa_name), 'w') as f:
f.write('{},{},{},{},{},{},{},{},{},{},{}\n'.format('iteration', 'MSE_trg', 'MSE_val', 'MSE_tst', 'acc_trg',
'acc_val', 'acc_tst', 'f1_trg', 'f1_val', 'f1_tst',
'elapsed'))
classification_network = factory.createClassificationNetwork(
layers, relu)
nnop = NeuralNetworkOptimizationProblem(
data_set, classification_network, measure)
oa = StandardGeneticAlgorithm(P, mate, mutate, nnop)
base.train(oa, classification_network, oa_name, training_ints, validation_ints, testing_ints, measure,
training_iterations, OUTFILE.format(oa_name))
return
def main(CE, layers, training_iterations, test_data_file, train_data_file,
validate_data_file, data_name):
"""Run this experiment"""
training_ints = base.initialize_instances(train_data_file)
testing_ints = base.initialize_instances(test_data_file)
validation_ints = base.initialize_instances(validate_data_file)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
# 50 and 0.000001 are the defaults from RPROPUpdateRule.java
rule = RPROPUpdateRule(0.064, 50, 0.000001)
oa_name = "SA_{}".format(CE)
classification_network = factory.createClassificationNetwork(layers, relu)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network,
measure)
oa = SimulatedAnnealing(1E10, CE, nnop)
with open(OUTFILE.format(data_name, oa_name), 'a+') as f:
content = f.read()
if "MSE_trg" not in content:
f.write('{},{},{},{},{},{},{},{},{},{},{}\n'.format(
'iteration', 'MSE_trg', 'MSE_val', 'MSE_tst', 'acc_trg',
'acc_val', 'acc_tst', 'f1_trg', 'f1_val', 'f1_tst', 'elapsed'))
base.train(oa, classification_network, oa_name, training_ints,
validation_ints, testing_ints, measure, training_iterations,
OUTFILE.format(data_name, oa_name))
return
def main(layers, training_iterations, test_data_file, train_data_file, validate_data_file, data_name):
"""Run this experiment"""
training_ints = base.initialize_instances(train_data_file)
testing_ints = base.initialize_instances(test_data_file)
validation_ints = base.initialize_instances(validate_data_file)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
# 50 and 0.000001 are the defaults from RPROPUpdateRule.java
rule = RPROPUpdateRule(0.064, 50, 0.000001)
oa_names = ["RHC"]
classification_network = factory.createClassificationNetwork(layers, relu)
nnop = NeuralNetworkOptimizationProblem(
data_set, classification_network, measure)
oa = RandomizedHillClimbing(nnop)
base.train(oa, classification_network, 'RHC', training_ints, validation_ints, testing_ints, measure,
training_iterations, OUTFILE.format(data_name, 'RHC'))
return
def main(layers, training_iterations, test_data_file, train_data_file,
validate_data_file, data_name):
"""Run this experiment"""
training_ints = base.initialize_instances(train_data_file)
testing_ints = base.initialize_instances(test_data_file)
validation_ints = base.initialize_instances(validate_data_file)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
# 50 and 0.000001 are the defaults from RPROPUpdateRule.java
rule = RPROPUpdateRule(0.064, 50, 0.000001)
oa_names = ["Backprop"]
classification_network = factory.createClassificationNetwork(layers, relu)
base.train(
BatchBackPropagationTrainer(data_set, classification_network, measure,
rule), classification_network, 'Backprop',
training_ints, validation_ints, testing_ints, measure,
training_iterations, OUTFILE.format(data_name, 'Backprop'))
return
def main(layers, training_iterations, test_data_file, train_data_file, validate_data_file, data_name):
"""Run this experiment"""
training_ints = base.initialize_instances(train_data_file)
testing_ints = base.initialize_instances(test_data_file)
validation_ints = base.initialize_instances(validate_data_file)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
# 50 and 0.000001 are the defaults from RPROPUpdateRule.java
rule = RPROPUpdateRule(0.064, 50, 0.000001)
oa_names = ["Backprop"]
classification_network = factory.createClassificationNetwork(layers, relu)
with open(OUTFILE.format(data_name, 'Backprop'), 'a+') as f:
content = f.read()
if "MSE_trg" not in content:
f.write('{},{},{},{},{},{},{},{},{},{},{}\n'.format('iteration', 'MSE_trg', 'MSE_val', 'MSE_tst', 'acc_trg',
'acc_val', 'acc_tst', 'f1_trg', 'f1_val', 'f1_tst',
'elapsed'))
base.train(BatchBackPropagationTrainer(data_set, classification_network, measure, rule), classification_network,
'Backprop', training_ints, validation_ints, testing_ints, measure, training_iterations,
OUTFILE.format(data_name, 'Backprop'))
return