def __init__(self, dimensions=0, k_neighbors=5, use_kernel=False):
'''
Constructor for FaceRecognizer.
Args (optional):
k_rank (int): How many principal components to keep.
k_neighbors (int): How many neighbors to compare against in the
kNN classifier.
'''
self.pca_model = PCAModel(dimensions=dimensions, use_kernel=use_kernel)
self.knn_classifier = KNNClassifier(neighbors=k_neighbors)
self.instances = None
def __init__(self, dimensions=0, k_neighbors=5, use_kernel=False):
'''
Constructor for FaceRecognizer.
Args (optional):
k_rank (int): How many principal components to keep.
k_neighbors (int): How many neighbors to compare against in the
kNN classifier.
'''
self.pca_model = PCAModel(dimensions=dimensions, use_kernel=use_kernel)
self.knn_classifier = KNNClassifier(neighbors=k_neighbors)
self.instances = None
def main():
print("learnTicTacToeWithComparisonNet.py main()")
authority = tictactoe.Authority()
positionTensorShape = authority.PositionTensorShape()
moveTensorShape = authority.MoveTensorShape()
playerList = authority.PlayersList()
if args.startWithNeuralNetwork is not None:
raise NotImplementedError(
"main(): Start with a neural network is not implemented...")
else:
if args.startWithAutoencoder is not None:
autoencoderNet = autoencoder.position.Net()
autoencoderNet.Load(args.startWithAutoencoder)
decoderClassifier = ComparisonNet.BuildADecoderClassifierFromAnAutoencoder(
autoencoderNet, dropoutRatio=0.25)
else:
raise NotImplementedError(
"main(): Starting without an autoencoder is not implemented..."
)
# Create the optimizer
logging.debug(decoderClassifier)
for name, param in decoderClassifier.named_parameters():
if 'decoding' in name:
param.requires_grad = True
else:
param.requires_grad = False
print("name = {}; param.requires_grad = {}".format(
name, param.requires_grad))
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
decoderClassifier.parameters()),
lr=args.learningRate,
betas=(0.5, 0.999))
# Loss function
loss = torch.nn.CrossEntropyLoss(
) # The neural network is a binary classifier
# Initial learning rate
learningRate = args.learningRate
# Output monitoring file
epochLossFile = open(os.path.join(args.outputDirectory, 'epochLoss.csv'),
"w",
buffering=1) # Flush the buffer at each line
epochLossFile.write(
"epoch,trainingLoss,validationLoss,validationAccuracy,averageReward,winRate,drawRate,lossRate\n"
)
# First game with a random player, before any training
decoderClassifier.eval()
(numberOfWinsForComparator, numberOfWinsForRandomPlayer,
numberOfDraws) = Comparison.SimulateGamesAgainstARandomPlayer(
decoderClassifier, authority, args.numberOfGamesAgainstARandomPlayer)
print(
"(numberOfWinsForComparator, numberOfWinsForRandomPlayer, numberOfDraws) = ({}, {}, {})"
.format(numberOfWinsForComparator, numberOfWinsForRandomPlayer,
numberOfDraws))
winRate = numberOfWinsForComparator / (numberOfWinsForComparator +
numberOfWinsForRandomPlayer +
numberOfDraws)
lossRate = numberOfWinsForRandomPlayer / (numberOfWinsForComparator +
numberOfWinsForRandomPlayer +
numberOfDraws)
drawRate = numberOfDraws / (numberOfWinsForComparator +
numberOfWinsForRandomPlayer + numberOfDraws)
logging.info(
"Against a random player, winRate = {}; drawRate = {}; lossRate = {}".
format(winRate, drawRate, lossRate))
epochLossFile.write('0' + ',' + '-' + ',' + '-' + ',' + '-,' +
str(winRate - lossRate) + ',' + str(winRate) + ',' +
str(drawRate) + ',' + str(lossRate) + '\n')
latentRepresentationFile = open(os.path.join(args.outputDirectory,
'latentRepresentation.csv'),
"w",
buffering=1)
#playerToEpsilonDict = {playerList[0]: args.epsilon, playerList[1]: args.epsilon}
epsilon = args.epsilon
for epoch in range(1, args.numberOfEpochs + 1):
logging.info("Epoch {}".format(epoch))
decoderClassifier.train()
if epoch % 100 == -1:
learningRate = learningRate / 2
for param_group in optimizer.param_groups:
param_group['lr'] = learningRate
#epsilon = epsilon/2
"""if (epoch // 25) %2 == 0: # Optimize decoding
for name, param in decoderClassifier.named_parameters():
if 'decoding' in name:
param.requires_grad = True
else:
param.requires_grad = False
logging.info("Optimizing decoding layers")
else: # Optimize encoding
for name, param in decoderClassifier.named_parameters():
if 'decoding' in name:
param.requires_grad = False
else:
param.requires_grad = True
logging.info("Optimizing encoding layers")
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, decoderClassifier.parameters()),
lr=learningRate,
betas=(0.5, 0.999))
"""
if epoch > 1 and epoch % 200 == 1:
epsilon = epsilon / 2
# Generate positions
if epoch % recomputingPeriod == 1:
minimumNumberOfMovesForInitialPositions = MinimumNumberOfMovesForInitialPositions(
epoch)
maximumNumberOfMovesForInitialPositions = args.maximumNumberOfMovesForInitialPositions
logging.info("Generating positions...")
startingPositionsList = Comparison.SimulateRandomGames(
authority,
minimumNumberOfMovesForInitialPositions,
maximumNumberOfMovesForInitialPositions,
args.numberOfPositionsForTraining,
swapIfOddNumberOfMoves=True)
numberOfMajorityX, numberOfMajorityO, numberOfEqualities = Majority(
startingPositionsList)
#print ("numberOfMajorityX = {}; numberOfMajorityO = {}; numberOfEqualities = {}".format(numberOfMajorityX, numberOfMajorityO, numberOfEqualities))
#print ("main(): startingPositionsList = {}".format(startingPositionsList))
startingPositionsTensor, augmentedStartingPositionsList = StartingPositionsInPairsOfPossibleOptions(
startingPositionsList, authority)
#print ("main(): augmentedStartingPositionsList = {}".format(augmentedStartingPositionsList))
#print ("main(): startingPositionsTensor.shape = {}".format(startingPositionsTensor.shape))
#print ("main(): startingPositionsTensor = {}".format(startingPositionsTensor))
logging.info("Comparing starting position pairs...")
decoderClassifier.eval()
pairWinnerIndexList = Comparison.ComparePositionPairs(
authority,
decoderClassifier,
augmentedStartingPositionsList,
args.numberOfSimulations,
epsilon=0,
playerToEpsilonDict={
playerList[0]: epsilon,
playerList[1]: epsilon
})
#print ("pairWinnerIndexList = {}".format(pairWinnerIndexList))
pairWinnerIndexTsr = PairWinnerIndexTensor(pairWinnerIndexList)
decoderClassifier.train()
# Since the samples are generated dynamically, there is no need for minibatches: all samples are always new
optimizer.zero_grad()
# Forward pass
outputTensor = decoderClassifier(startingPositionsTensor)
# Calculate the error and backpropagate
trainingLoss = loss(outputTensor, pairWinnerIndexTsr)
logging.info("trainingLoss.item() = {}".format(trainingLoss.item()))
trainingLoss.backward()
# Move in the gradient descent direction
optimizer.step()
gradient0AbsMean = decoderClassifier.Gradient0AbsMean()
logging.debug("gradient0AbsMean = {}".format(gradient0AbsMean))
# ****************** Validation ******************
decoderClassifier.eval()
if epoch % 200 == 1:
logging.info("Generating validation positions...")
validationStartingPositionsList = Comparison.SimulateRandomGames(
authority,
minimumNumberOfMovesForInitialPositions,
maximumNumberOfMovesForInitialPositions,
args.numberOfPositionsForValidation,
swapIfOddNumberOfMoves=True)
"""for validationStartingPositionNdx in range(len(validationStartingPositionsList)):
if numpy.random.random() >= 0.5:
swappedPosition = authority.SwapPositions(validationStartingPositionsList[validationStartingPositionNdx], playerList[0], playerList[1])
validationStartingPositionsList[validationStartingPositionNdx] = swappedPosition
"""
# print ("main(): startingPositionsList = {}".format(startingPositionsList))
validationStartingPositionsTensor, validationAugmentedStartingPositionsList = \
StartingPositionsInPairsOfPossibleOptions(validationStartingPositionsList, authority)
logging.info("Comparing validation starting position pairs...")
validationPairWinnerIndexList = Comparison.ComparePositionPairs(
authority,
decoderClassifier,
validationAugmentedStartingPositionsList,
args.numberOfSimulations,
epsilon=0,
playerToEpsilonDict={
playerList[0]: epsilon,
playerList[1]: epsilon
}) # Start with purely random games (epsilon = 1)
validationPairWinnerIndexTsr = PairWinnerIndexTensor(
validationPairWinnerIndexList)
# Forward pass
validationOutputTensor = decoderClassifier(
validationStartingPositionsTensor)
# Calculate the validation error
validationLoss = loss(validationOutputTensor,
validationPairWinnerIndexTsr)
# Calculate the validation accuracy
validationAccuracy = Accuracy(validationOutputTensor,
validationPairWinnerIndexTsr)
logging.info(
"validationLoss.item() = {}; validationAccuracy = {}".format(
validationLoss.item(), validationAccuracy))
# Check if latent representation of pairs are the same
validationLatentRepresentationTsr = decoderClassifier.DecodingLatentRepresentation(
decodingLayerNdx=1, inputTsr=validationStartingPositionsTensor)
print(
"validationStartingPositionsTensor.shape = {}; validationLatentRepresentationTsr.shape = {}"
.format(validationStartingPositionsTensor.shape,
validationLatentRepresentationTsr.shape))
for pairNdx in range(validationLatentRepresentationTsr.shape[0] // 2):
if torch.max(
torch.abs(validationLatentRepresentationTsr[2 * pairNdx] -
validationLatentRepresentationTsr[2 * pairNdx +
1])
).item() < dead_neuron_zero_threshold:
logging.warning(
"Pair {} and {} have an identical latent representation".
format(2 * pairNdx, 2 * pairNdx + 1))
print("validationStartingPositionsTensor[2 * pairNdx] = {}".
format(validationStartingPositionsTensor[2 * pairNdx]))
print(
"validationStartingPositionsTensor[2 * pairNdx + 1] = {}".
format(validationStartingPositionsTensor[2 * pairNdx + 1]))
logging.info("Play against a random player...")
# Play against a random player
(numberOfWinsForEvaluator, numberOfWinsForRandomPlayer,
numberOfDraws) = Comparison.SimulateGamesAgainstARandomPlayer(
decoderClassifier, authority,
args.numberOfGamesAgainstARandomPlayer)
winRate = numberOfWinsForEvaluator / (numberOfWinsForEvaluator +
numberOfWinsForRandomPlayer +
numberOfDraws)
lossRate = numberOfWinsForRandomPlayer / (numberOfWinsForEvaluator +
numberOfWinsForRandomPlayer +
numberOfDraws)
drawRate = numberOfDraws / (numberOfWinsForEvaluator +
numberOfWinsForRandomPlayer +
numberOfDraws)
logging.info(
"Against a random player, winRate = {}; drawRate = {}; lossRate = {}"
.format(winRate, drawRate, lossRate))
epochLossFile.write(
str(epoch) + ',' + str(trainingLoss.item()) + ',' +
str(validationLoss.item()) + ',' + str(validationAccuracy) + ',' +
str(winRate - lossRate) + ',' + str(winRate) + ',' +
str(drawRate) + ',' + str(lossRate) + '\n')
# Write validation latent representations
logging.info("Validation latent representation...")
validationLatentRepresentationTsr = decoderClassifier.DecodingLatentRepresentation(
decodingLayerNdx=7, inputTsr=validationStartingPositionsTensor)
# validationLatentRepresentation1Tsr.shape = torch.Size([ 2 * numberOfPositionsForValidation, decoderClassifier.decodingIntermediateNumberOfNeurons ])
validationLatentRepresentationArr = validationLatentRepresentationTsr.detach(
).numpy()
pcaModel = PCAModel.PCAModel(validationLatentRepresentationArr,
pca_zero_threshold)
pcaModel.TruncateModel(2)
validationProjections = pcaModel.Project(
validationLatentRepresentationArr)
validationProjectionsTsr = torch.from_numpy(validationProjections)
validationProjectionsTsr = torch.cat(
(validationProjectionsTsr,
torch.argmax(validationOutputTensor,
dim=1).unsqueeze(1).double()), 1)
validationProjectionsTsr = torch.cat(
(validationProjectionsTsr,
validationPairWinnerIndexTsr.unsqueeze(1).double()), 1)
numpy.savetxt(latentRepresentationFile,
[validationProjectionsTsr.numpy().flatten()],
delimiter=',')
if epoch % 10 == 0:
filepath = os.path.join(args.outputDirectory,
'tictactoe_' + str(epoch) + '.bin')
decoderClassifier.Save(filepath)
epsilon0GamePositionsList, epsilon0GameWinner = Comparison.SimulateAGame(
decoderClassifier, authority)
for position in epsilon0GamePositionsList:
authority.Display(position)
print(".............\n")
# Reinitialize for dead neurons
ReinitializeDeadNeurons(decoderClassifier, startingPositionsTensor)
class FaceRecognizer:
def __init__(self, dimensions=0, k_neighbors=5, use_kernel=False):
'''
Constructor for FaceRecognizer.
Args (optional):
k_rank (int): How many principal components to keep.
k_neighbors (int): How many neighbors to compare against in the
kNN classifier.
'''
self.pca_model = PCAModel(dimensions=dimensions, use_kernel=use_kernel)
self.knn_classifier = KNNClassifier(neighbors=k_neighbors)
self.instances = None
def train(self, instances):
'''
Trains the recognizer with a set of faces.
Args:
instances (list<tuple<int, numpy.ndarray>>): List of label/face
data pairs.
'''
self.instances = instances
# Stack all of the faces together
faces_list = list()
for instance in instances:
faces_list.append(instance[1])
faces = np.vstack(faces_list).T
# Learn principal components
self.pca_model.fit(faces)
# Add each class to the kNN classifier
for instance in instances:
label = instance[0]
face = instance[1]
t_face = self.pca_model.transform(face)
self.knn_classifier.add_sample(label, t_face)
def fit_knn(self):
'''
Fits the kNN classifier with the current instances.
'''
if self.instances is None:
raise RuntimeError('FaceRecognizer has no instances')
self.knn_classifier.reset()
for instance in self.instances:
label = instance[0]
face = instance[1]
t_face = self.pca_model.transform(face)
self.knn_classifier.add_sample(label, t_face)
def classify(self, face):
'''
Classifies a given face from the trained set.
Args:
face (numpy.ndarray): The face to classify.
Returns:
int, the class the face best belongs to.
'''
t_face = self.pca_model.transform(face)
return self.knn_classifier.classify(t_face)
def set_dimensions(self, dimensions):
'''
Sets the number of dimensions to use from PCA.
Args:
dimensions (int): The new number of dimensions.
'''
self.pca_model.dimensions = dimensions
if self.instances is not None:
self.fit_knn()
def set_k_neighbors(self, k):
'''
Sets k for kNN classifier.
Args:
k (int): The new k for the classifier.
'''
self.knn_classifier.neighbors = k
def set_kernel_variance(self, variance):
'''
Sets the variance for the RBF kernel and retrains.
Args:
variance (float): The new variance.
'''
self.pca_model.variance = variance
if self.instances is not None and self.pca_model.use_kernel:
self.train(self.instances)
class FaceRecognizer:
def __init__(self, dimensions=0, k_neighbors=5, use_kernel=False):
'''
Constructor for FaceRecognizer.
Args (optional):
k_rank (int): How many principal components to keep.
k_neighbors (int): How many neighbors to compare against in the
kNN classifier.
'''
self.pca_model = PCAModel(dimensions=dimensions, use_kernel=use_kernel)
self.knn_classifier = KNNClassifier(neighbors=k_neighbors)
self.instances = None
def train(self, instances):
'''
Trains the recognizer with a set of faces.
Args:
instances (list<tuple<int, numpy.ndarray>>): List of label/face
data pairs.
'''
self.instances = instances
# Stack all of the faces together
faces_list = list()
for instance in instances:
faces_list.append(instance[1])
faces = np.vstack(faces_list).T
# Learn principal components
self.pca_model.fit(faces)
# Add each class to the kNN classifier
for instance in instances:
label = instance[0]
face = instance[1]
t_face = self.pca_model.transform(face)
self.knn_classifier.add_sample(label, t_face)
def fit_knn(self):
'''
Fits the kNN classifier with the current instances.
'''
if self.instances is None:
raise RuntimeError('FaceRecognizer has no instances')
self.knn_classifier.reset()
for instance in self.instances:
label = instance[0]
face = instance[1]
t_face = self.pca_model.transform(face)
self.knn_classifier.add_sample(label, t_face)
def classify(self, face):
'''
Classifies a given face from the trained set.
Args:
face (numpy.ndarray): The face to classify.
Returns:
int, the class the face best belongs to.
'''
t_face = self.pca_model.transform(face)
return self.knn_classifier.classify(t_face)
def set_dimensions(self, dimensions):
'''
Sets the number of dimensions to use from PCA.
Args:
dimensions (int): The new number of dimensions.
'''
self.pca_model.dimensions = dimensions
if self.instances is not None:
self.fit_knn()
def set_k_neighbors(self, k):
'''
Sets k for kNN classifier.
Args:
k (int): The new k for the classifier.
'''
self.knn_classifier.neighbors = k
def set_kernel_variance(self, variance):
'''
Sets the variance for the RBF kernel and retrains.
Args:
variance (float): The new variance.
'''
self.pca_model.variance = variance
if self.instances is not None and self.pca_model.use_kernel:
self.train(self.instances)
