class COTONN:
def __init__(self):
self.version = "0.5.4"
self.utils = Utilities()
self.save_path = "./controllers/nn/" + self.utils.getTimestamp() + "/"
self.importer = Importer()
self.exporter = Exporter(self.version)
self.exporter.setSaveLocation(self.save_path)
self.nnm = NeuralNetworkManager()
self.nnm.setSaveLocation(self.save_path)
self.staticController = StaticController()
self.debug_mode = False
self.importer.setDebugMode(False)
self.nnm.setDebugMode(self.debug_mode)
print("COTONN v" + self.version + "\n")
# Clean memory function
def cleanMemory(self):
del self.nnm.nn
del self.nnm
del self.staticController
del self.exporter
del self.importer
# Generate MLP from fullset
def fullSetMLP(self,
filename,
layer_width,
layer_height,
learning_rate,
dropout_rate,
fitness_threshold,
batch_size,
display_step,
save_option=True):
self.staticController = self.importer.readStaticController(filename)
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.formatToBinary()
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunction(NNActivationFunction.Sigmoid)
self.nnm.setDataSet(fullSet)
self.nnm.setDropoutRate(dropout_rate)
self.nnm.rectangularHiddenLayers(layer_width, layer_height)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size,
display_step, -1, 5000)
self.nnm.getDataSize()
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
if (save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.nnm.close()
self.cleanMemory()
# Generate MLP from subset
def subSetMLP(self,
filename,
percentage,
layer_width,
layer_height,
learning_rate,
dropout_rate,
fitness_threshold,
batch_size,
display_step,
save_option=True):
self.staticController = self.importer.readStaticController(filename)
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.formatToBinary()
subSet = DataSet()
subSet.readSubsetFromController(self.staticController, percentage)
subSet.formatToBinary()
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunction(NNActivationFunction.Sigmoid)
self.nnm.setDataSet(subSet)
self.nnm.setDropoutRate(dropout_rate)
self.nnm.rectangularHiddenLayers(layer_width, layer_height)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size,
display_step, -1, 5000)
self.nnm.getDataSize()
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
if (save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.nnm.close()
self.cleanMemory()
# Scout learningrate convergence
def scoutLearningRateConvergence(self, filename, layer_width, layer_height,
epoch_threshold, rates, batch_size,
display_step):
self.staticController = self.importer.readStaticController(filename)
dataSet = DataSet()
dataSet.readSetFromController(self.staticController)
dataSet.formatToBinary()
self.nnm.setDebugMode(False)
fitnesses = []
for r in rates:
print("\nLearning rate: " + str(r))
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunction(NNActivationFunction.Sigmoid)
self.nnm.setDataSet(dataSet)
self.nnm.rectangularHiddenLayers(layer_width, layer_height)
self.nnm.initializeNeuralNetwork()
self.nnm.initializeTraining(r, 1.0, batch_size, display_step,
epoch_threshold)
self.nnm.train()
fitness, wrong_states = self.nnm.checkFitness(dataSet)
self.fitnesses.append(fitness)
self.nnm.close()
# Plot
plt.semilogx(rates, fitnesses, 'r-')
plt.xlabel("Rates")
plt.ylabel("Fitness")
plt.grid()
(x1, x2, y1, y2) = plt.axis()
plt.axis((min(rates), max(rates), 0.0, y2 + 0.1))
plt.show()
self.cleanMemory()
# Generate MLP from fullset
def importMLP(self,
import_path,
filename,
layer_width,
layer_height,
learning_rate,
dropout_rate,
fitness_threshold,
batch_size,
display_step,
save_option=True):
self.staticController = self.importer.readStaticController(filename)
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.formatToBinary()
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunction(NNActivationFunction.Sigmoid)
self.nnm.setDataSet(fullSet)
# Option to adjust parameters for new training session
self.nnm.setDropoutRate(dropout_rate)
self.nnm.rectangularHiddenLayers(layer_width, layer_height)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size,
display_step)
self.nnm.getDataSize()
# Restore Network from saved file:
self.importer.restoreNetwork(self.nnm, import_path)
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
# Save Network or Variables
if (save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.nnm.close()
# Generate MLP from fullset
def customFullSetMLP(self,
filename,
layer,
learning_rate,
dropout_rate,
fitness_threshold,
batch_size,
display_step,
save_option=True):
self.staticController = self.importer.readStaticController(filename)
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.formatToBinary()
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunction(NNActivationFunction.Sigmoid)
self.nnm.setDataSet(fullSet)
self.nnm.setDropoutRate(dropout_rate)
self.nnm.customHiddenLayers(layer)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size,
display_step, -1, 5000)
self.nnm.getDataSize()
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
if (save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.nnm.close()
self.cleanMemory()
class COTONN:
def __init__(self):
self.version = "2.0"
self.utils = Utilities()
self.save_path = "./nn/" + self.utils.getTimestamp() + "/"
self.importer = Importer()
self.exporter = Exporter(self.version)
self.exporter.setSaveLocation(self.save_path)
self.nnm = NeuralNetworkManager()
self.nnm.setSaveLocation(self.save_path)
self.staticController = StaticController()
self.debug_mode = False
self.importer.setDebugMode(False)
self.nnm.setDebugMode(self.debug_mode)
print("COTONN v" + self.version + "\n")
self.encode = EncodeTypes.Boolean
self.var_order = Ordering.PerCoordinate
self.filename = ""
# Clean memory function
def cleanMemory(self):
del self.nnm.nn
del self.nnm.controller
del self.nnm
del self.staticController
del self.exporter
del self.importer
# Generate MLP from fullset
def deterministicMLP(self, filename, layer, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = True
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.addAllGridPointDeterministic(self.staticController, self.encode, self.var_order)
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Sigmoid)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
self.nnm.setDropoutRate(dropout_rate)
self.nnm.customHiddenLayers(layer)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step, -1, 5000)
self.nnm.getDataSize()
# self.nnm.setEpochThreshold(172)
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
# fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()
# Generate MLP from fullset
def importDeterministicMLP(self, import_path, filename, layer, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = True
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
# fullSet.formatToBinary()
fullSet.addAllGridPointDeterministic(self.staticController, self.encode, self.var_order)
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
# self.nnm.setActivationFunction(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Sigmoid)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
# Option to adjust parameters for new training session
self.nnm.setDropoutRate(dropout_rate)
self.nnm.customHiddenLayers(layer)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step)
self.nnm.getDataSize()
# Restore Network from saved file:
self.importer.restoreNetwork(self.nnm, import_path)
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
self.nnm.randomCheck(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()
# Generate MLP from fullset
def deterministicRectMLP(self, filename, layer_width, layer_height, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = True
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.addAllGridPointDeterministic(self.staticController, self.encode, self.var_order)
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Sigmoid)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
self.nnm.setDropoutRate(dropout_rate)
self.nnm.rectangularHiddenLayers(layer_width, layer_height)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step, -1, 5000)
self.nnm.getDataSize()
# self.nnm.setEpochThreshold(172)
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
# fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()
# Generate MLP from fullset
def importDeterministicRectMLP(self, import_path, filename, layer_width, layer_height, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = True
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
# fullSet.formatToBinary()
fullSet.addAllGridPointDeterministic(self.staticController, self.encode, self.var_order)
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
# self.nnm.setActivationFunction(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Sigmoid)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
# Option to adjust parameters for new training session
self.nnm.setDropoutRate(dropout_rate)
self.nnm.rectangularHiddenLayers(layer_width, layer_height)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step)
self.nnm.getDataSize()
# Restore Network from saved file:
self.importer.restoreNetwork(self.nnm, import_path)
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
self.nnm.randomCheck(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()
# Generate MLP from fullset
def determinizingMLP(self, filename, layer, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = False
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.addAllGridPointDeterminizing(self.staticController, self.var_order)
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Softmax)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
self.nnm.setDropoutRate(dropout_rate)
self.nnm.customHiddenLayers(layer)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step, -1, 5000)
self.nnm.getDataSize()
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
# fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()
# Generate MLP from fullset
def importDeterminizingMLP(self, import_path, filename, layer, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = False
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.addAllGridPointDeterminizing(self.staticController, self.var_order)
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Softmax)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
self.nnm.setDropoutRate(dropout_rate)
self.nnm.customHiddenLayers(layer)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step, -1, 5000)
self.nnm.getDataSize()
# Restore Network from saved file:
self.importer.restoreNetwork(self.nnm, import_path)
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
# fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()
# Generate MLP from fullset
def determinizingRectMLP(self, filename, layer_width, layer_height, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = False
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.addAllGridPointDeterminizing(self.staticController, self.var_order)
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Softmax)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
self.nnm.setDropoutRate(dropout_rate)
self.nnm.rectangularHiddenLayers(layer_width, layer_height)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step, -1, 5000)
self.nnm.getDataSize()
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
# fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()
# Generate MLP from fullset
def importDeterminizingRectMLP(self, import_path, filename, layer_width, layer_height, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = False
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.addAllGridPointDeterminizing(self.staticController, self.var_order)
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Softmax)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
self.nnm.setDropoutRate(dropout_rate)
self.nnm.rectangularHiddenLayers(layer_width, layer_height)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step, -1, 5000)
self.nnm.getDataSize()
# Restore Network from saved file:
self.importer.restoreNetwork(self.nnm, import_path)
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
# fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()
# Generate MLP from fullset
def nonDeterministicMLP(self, filename, input_file, layer, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = False
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.addAllGridPointNonDeterministic(input_file, self.staticController, self.var_order)
self.nnm.non_det = True
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Sigmoid)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
self.nnm.setDropoutRate(dropout_rate)
self.nnm.customHiddenLayers(layer)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step, -1, 5000)
self.nnm.getDataSize()
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
# fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()
# Generate MLP from fullset
def importNonDeterministicMLP(self, import_path, input_file, filename, layer, encode = 0, var_order = 0, learning_rate = 0.01, dropout_rate = 0.0, fitness_threshold = 1.0, batch_size = 1024, display_step = 50, save_option=True):
self.importer.det = False
self.staticController = self.importer.readStaticController(filename)
self.filename = filename
if(encode == 0):
self.encode = EncodeTypes.Boolean
if(var_order == 0):
self.var_order = Ordering.Original
else:
self.var_order = Ordering.PerCoordinate
fullSet = DataSet()
fullSet.readSetFromController(self.staticController)
fullSet.addAllGridPointNonDeterministic(input_file, self.staticController, self.var_order)
self.nnm.setDebugMode(True)
self.nnm.setType(NNTypes.MLP)
self.nnm.setTrainingMethod(NNOptimizer.Adam)
self.nnm.setActivationFunctionHidden(NNActivationFunction.Sigmoid)
self.nnm.setActivationFunctionOutput(NNActivationFunction.Sigmoid)
self.nnm.setEncodeTypes(self.encode)
self.nnm.setDataSet(fullSet)
self.nnm.controller = self.staticController
self.nnm.setDropoutRate(dropout_rate)
self.nnm.customHiddenLayers(layer)
self.nnm.initialize(learning_rate, fitness_threshold, batch_size, display_step, -1, 5000)
self.nnm.getDataSize()
# Restore Network from saved file:
self.importer.restoreNetwork(self.nnm, import_path)
# Train model and visualize performance
self.nnm.train()
self.nnm.plot()
# fitness, wrong_states = self.nnm.checkFitness(fullSet)
self.nnm.randomCheck(fullSet)
fitness, wrong_states = self.nnm.checkFitnessAllGridPoint(fullSet)
loosing_states = self.nnm.createLoosingPoints(wrong_states)
if(save_option):
self.exporter.saveNetwork(self.nnm)
self.exporter.saveWrongStates(wrong_states)
self.exporter.saveMatlabMLP(self.staticController, self.nnm)
self.exporter.saveBinary(self.nnm)
self.exporter.saveTrainingData(self.nnm)
self.exporter.saveUpdatedAvoid(self.filename, wrong_states, loosing_states)
self.nnm.close()
self.cleanMemory()