def testImage(image, faceCoordinates, emotion_classifier):
"""Classifies the emotions in the input image according to the face coordinates
detected by a face detection engine.
First calls preprocess and then uses the given emotion_classifier to detect
emotions in the processed image.
"""
testImg = preprocess(image, faceCoordinates, return_vector=True)
# IMPORTANT: scale the image for it to be testable
test = common.scale(testImg.reshape(1, len(testImg)))
probs, emotion = emotion_classifier.classify(test)
# classify returns a vector, as it is made to classify multiple test instances
# at the same time.
# We check if emotion is iterable before getting the first element, in case
# someone uses an api in which a vector is not returned.
if hasattr(emotion, '__iter__'):
emotion = emotion[0]
print "probs"
print probs
print "label"
print emotion
return emotion
def testImage(image, faceCoordinates, emotion_classifier):
"""Classifies the emotions in the input image according to the face coordinates
detected by a face detection engine.
First calls preprocess and then uses the given emotion_classifier to detect
emotions in the processed image.
"""
testImg = preprocess(image, faceCoordinates, return_vector=True)
# IMPORTANT: scale the image for it to be testable
test = common.scale(testImg.reshape(1, len(testImg)))
probs, emotion = emotion_classifier.classify(test)
# classify returns a vector, as it is made to classify multiple test instances
# at the same time.
# We check if emotion is iterable before getting the first element, in case
# someone uses an api in which a vector is not returned.
if hasattr(emotion, '__iter__'):
emotion = emotion[0]
print "probs"
print probs
print "label"
print emotion
return emotion
def trainNetWithAllData():
unsupervisedData, data, labels = createTrainingSet()
print "data.shape"
print data.shape
print "labels.shape"
print labels.shape
data = common.scale(data)
unsupervisedData = None
activationFunction = activationfunctions.Rectified()
rbmActivationFunctionVisible = activationfunctions.Identity()
rbmActivationFunctionHidden = activationfunctions.RectifiedNoisy()
unsupervisedLearningRate = 0.0001
supervisedLearningRate = 0.001
momentumMax = 0.99
print "This is input data shape", data.shape
print labels.shape
net = db.DBN(4, [1200, 1500, 1000, len(args.emotions)],
binary=False,
activationFunction=activationFunction,
rbmActivationFunctionVisible=rbmActivationFunctionVisible,
rbmActivationFunctionHidden=rbmActivationFunctionHidden,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
nesterovMomentum=True,
rbmNesterovMomentum=True,
rmsprop=True,
miniBatchSize=20,
hiddenDropout=0.5,
visibleDropout=0.8,
momentumFactorForLearningRateRBM=False,
firstRBMheuristic=False,
rbmVisibleDropout=1.0,
rbmHiddenDropout=1.0,
preTrainEpochs=10,
sparsityConstraintRbm=False,
sparsityRegularizationRbm=0.001,
sparsityTragetRbm=0.01)
net.train(data,
labels,
maxEpochs=200,
validation=False,
unsupervisedData=unsupervisedData)
with open(args.net_file, "wb") as f:
pickle.dump(net, f)
return net
def trainNetWithAllData():
unsupervisedData, data, labels = createTrainingSet()
print "data.shape"
print data.shape
print "labels.shape"
print labels.shape
data = common.scale(data)
unsupervisedData = None
activationFunction = activationfunctions.Rectified()
rbmActivationFunctionVisible = activationfunctions.Identity()
rbmActivationFunctionHidden = activationfunctions.RectifiedNoisy()
unsupervisedLearningRate = 0.0001
supervisedLearningRate = 0.001
momentumMax = 0.99
# net = db.DBN(4, [1200, 1500, 1000, len(args.emotions)],
# binary=False,
# activationFunction=activationFunction,
# rbmActivationFunctionVisible=rbmActivationFunctionVisible,
# rbmActivationFunctionHidden=rbmActivationFunctionHidden,
# unsupervisedLearningRate=unsupervisedLearningRate,
# supervisedLearningRate=supervisedLearningRate,
# momentumMax=momentumMax,
# nesterovMomentum=True,
# rbmNesterovMomentum=True,
# rmsprop=True,
# miniBatchSize=20,
# hiddenDropout=0.5,
# visibleDropout=0.8,
# momentumFactorForLearningRateRBM=False,
# firstRBMheuristic=False,
# rbmVisibleDropout=1.0,
# rbmHiddenDropout=1.0,
# preTrainEpochs=10,
# sparsityConstraintRbm=False,
# sparsityRegularizationRbm=0.001,
# sparsityTragetRbm=0.01)
#
# net.train(data, labels, maxEpochs=200,
# validation=False,
# unsupervisedData=unsupervisedData)
net = cnn.CNN(width=30, height=40, classes=len(args.emotions))
net.train(data, labels)
with open(args.net_file, "wb") as f:
pickle.dump(net, f)
return net
def trainNetWithAllData():
unsupervisedData, data, labels = createTrainingSet()
print "data.shape"
print data.shape
print "labels.shape"
print labels.shape
data = common.scale(data)
unsupervisedData = None
activationFunction = activationfunctions.Rectified()
rbmActivationFunctionVisible = activationfunctions.Identity()
rbmActivationFunctionHidden = activationfunctions.RectifiedNoisy()
unsupervisedLearningRate = 0.0001
supervisedLearningRate = 0.001
momentumMax = 0.99
net = db.DBN(4, [1200, 1500, 1000, len(args.emotions)],
binary=False,
activationFunction=activationFunction,
rbmActivationFunctionVisible=rbmActivationFunctionVisible,
rbmActivationFunctionHidden=rbmActivationFunctionHidden,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
nesterovMomentum=True,
rbmNesterovMomentum=True,
rmsprop=True,
miniBatchSize=20,
hiddenDropout=0.5,
visibleDropout=0.8,
momentumFactorForLearningRateRBM=False,
firstRBMheuristic=False,
rbmVisibleDropout=1.0,
rbmHiddenDropout=1.0,
preTrainEpochs=10,
sparsityConstraintRbm=False,
sparsityRegularizationRbm=0.001,
sparsityTragetRbm=0.01)
net.train(data, labels, maxEpochs=200,
validation=False,
unsupervisedData=unsupervisedData)
with open(args.net_file, "wb") as f:
pickle.dump(net, f)
return net
def getHyperParamsAndBestNet():
unsupervisedData, data, labels = createTrainingSet()
print np.unique(np.argmax(labels, axis=1))
print "data.shape"
print data.shape
print "labels.shape"
print labels.shape
print data
data = common.scale(data)
unsupervisedData = None
activationFunction = activationfunctions.Rectified()
rbmActivationFunctionVisible = activationfunctions.Identity()
rbmActivationFunctionHidden = activationfunctions.RectifiedNoisy()
tried_params = []
percentages = []
best_index = 0
index = 0
best_correct = 0
# Random data for training and testing
kf = cross_validation.KFold(n=len(data), n_folds=10)
for train, test in kf:
unsupervisedLearningRate = random.uniform(0.0001, 0.2)
supervisedLearningRate = random.uniform(0.0001, 0.2)
momentumMax = random.uniform(0.7, 1)
tried_params += [{'unsupervisedLearningRate': unsupervisedLearningRate,
'supervisedLearningRate': supervisedLearningRate,
'momentumMax': momentumMax}]
trainData = data[train]
trainLabels = labels[train]
net = db.DBN(4, [1200, 1500, 1000, len(args.emotions)],
binary=False,
activationFunction=activationFunction,
rbmActivationFunctionVisible=rbmActivationFunctionVisible,
rbmActivationFunctionHidden=rbmActivationFunctionHidden,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
nesterovMomentum=True,
rbmNesterovMomentum=True,
rmsprop=True,
miniBatchSize=20,
hiddenDropout=0.5,
visibleDropout=0.8,
momentumFactorForLearningRateRBM=False,
firstRBMheuristic=False,
rbmVisibleDropout=1.0,
rbmHiddenDropout=1.0,
preTrainEpochs=10,
sparsityConstraintRbm=False,
sparsityRegularizationRbm=0.001,
sparsityTragetRbm=0.01)
net.train(trainData, trainLabels, maxEpochs=200,
validation=False,
unsupervisedData=unsupervisedData)
probs, predicted = net.classify(data[test])
actualLabels = labels[test]
correct = 0
for i in xrange(len(test)):
actual = actualLabels[i]
print probs[i]
if predicted[i] == np.argmax(actual):
correct += 1
percentage_correct = correct * 1.0 / len(test)
print "percentage correct"
print percentage_correct
if percentage_correct > best_correct:
best_index = index
best_correct = percentage_correct
with open(args.net_file, "wb") as f:
pickle.dump(net, f)
percentages += [percentage_correct]
index += 1
print 'best params'
print tried_params[best_index]
print 'precision'
print best_correct
def trainAndTestNet():
unsupervisedData, data, labels = createTrainingSet()
print np.unique(np.argmax(labels, axis=1))
print "data.shape"
print data.shape
print "labels.shape"
print labels.shape
# Random data for training and testing
kf = cross_validation.KFold(n=len(data), k=5)
for train, test in kf:
break
print data
data = common.scale(data)
unsupervisedData = None
activationFunction = activationfunctions.Rectified()
rbmActivationFunctionVisible = activationfunctions.Identity()
rbmActivationFunctionHidden = activationfunctions.RectifiedNoisy()
unsupervisedLearningRate = 0.0001
supervisedLearningRate = 0.001
momentumMax = 0.99
trainData = data[train]
trainLabels = labels[train]
net = db.DBN(4, [1200, 1500, 1000, len(args.emotions)],
binary=False,
activationFunction=activationFunction,
rbmActivationFunctionVisible=rbmActivationFunctionVisible,
rbmActivationFunctionHidden=rbmActivationFunctionHidden,
unsupervisedLearningRate=unsupervisedLearningRate,
supervisedLearningRate=supervisedLearningRate,
momentumMax=momentumMax,
nesterovMomentum=True,
rbmNesterovMomentum=True,
rmsprop=True,
miniBatchSize=20,
hiddenDropout=0.5,
visibleDropout=0.8,
momentumFactorForLearningRateRBM=False,
firstRBMheuristic=False,
rbmVisibleDropout=1.0,
rbmHiddenDropout=1.0,
preTrainEpochs=10,
sparsityConstraintRbm=False,
sparsityRegularizationRbm=0.001,
sparsityTragetRbm=0.01)
net.train(trainData, trainLabels, maxEpochs=200,
validation=False,
unsupervisedData=unsupervisedData)
probs, predicted = net.classify(data[test])
actualLabels = labels[test]
correct = 0
errorCases = []
for i in xrange(len(test)):
actual = actualLabels[i]
print probs[i]
if predicted[i] == np.argmax(actual):
correct += 1
else:
errorCases.append(i)
print "correct"
print correct
print "percentage correct"
print correct * 1.0 / len(test)
confMatrix = confusion_matrix(np.argmax(actualLabels, axis=1), predicted)
print "confusion matrix"
print confMatrix
with open(args.net_file, "wb") as f:
pickle.dump(net, f)
return net
def getHyperParamsAndBestNet():
unsupervisedData, data, labels = createTrainingSet()
print np.unique(np.argmax(labels, axis=1))
print "data.shape"
print data.shape
print "labels.shape"
print labels.shape
print data
data = common.scale(data)
unsupervisedData = None
activationFunction = activationfunctions.Rectified()
rbmActivationFunctionVisible = activationfunctions.Identity()
rbmActivationFunctionHidden = activationfunctions.RectifiedNoisy()
tried_params = []
percentages = []
best_index = 0
index = 0
best_correct = 0
# Random data for training and testing
kf = cross_validation.KFold(n=len(data), n_folds=10)
for train, test in kf:
unsupervisedLearningRate = random.uniform(0.0001, 0.2)
supervisedLearningRate = random.uniform(0.0001, 0.2)
momentumMax = random.uniform(0.7, 1)
tried_params += [{
'unsupervisedLearningRate': unsupervisedLearningRate,
'supervisedLearningRate': supervisedLearningRate,
'momentumMax': momentumMax
}]
trainData = data[train]
trainLabels = labels[train]
# net = db.DBN(4, [1200, 1500, 1000, len(args.emotions)],
# binary=False,
# activationFunction=activationFunction,
# rbmActivationFunctionVisible=rbmActivationFunctionVisible,
# rbmActivationFunctionHidden=rbmActivationFunctionHidden,
# unsupervisedLearningRate=unsupervisedLearningRate,
# supervisedLearningRate=supervisedLearningRate,
# momentumMax=momentumMax,
# nesterovMomentum=True,
# rbmNesterovMomentum=True,
# rmsprop=True,
# miniBatchSize=20,
# hiddenDropout=0.5,
# visibleDropout=0.8,
# momentumFactorForLearningRateRBM=False,
# firstRBMheuristic=False,
# rbmVisibleDropout=1.0,
# rbmHiddenDropout=1.0,
# preTrainEpochs=10,
# sparsityConstraintRbm=False,
# sparsityRegularizationRbm=0.001,
# sparsityTragetRbm=0.01)
#
# net.train(trainData, trainLabels, maxEpochs=200,
# validation=False,
# unsupervisedData=unsupervisedData)
#
# probs, predicted = net.classify(data[test])
net = cnn.CNN(30, 40, len(args.emotions))
net.train(trainData, trainLabels)
probs, predicted = net.classify(data[test])
actualLabels = labels[test]
correct = 0
for i in xrange(len(test)):
actual = actualLabels[i]
print probs[i]
if predicted[i] == np.argmax(actual):
correct += 1
percentage_correct = correct * 1.0 / len(test)
print "percentage correct"
print percentage_correct
if percentage_correct > best_correct:
best_index = index
best_correct = percentage_correct
with open(args.net_file, "wb") as f:
pickle.dump(net, f)
percentages += [percentage_correct]
index += 1
print 'best params'
print tried_params[best_index]
print 'precision'
print best_correct
def trainAndTestNet():
unsupervisedData, data, labels = createTrainingSet()
print np.unique(np.argmax(labels, axis=1))
print "data.shape"
print data.shape
print "labels.shape"
print labels.shape
# Random data for training and testing
kf = cross_validation.KFold(n=len(data), k=5)
for train, test in kf:
break
print data
data = common.scale(data)
unsupervisedData = None
activationFunction = activationfunctions.Rectified()
rbmActivationFunctionVisible = activationfunctions.Identity()
rbmActivationFunctionHidden = activationfunctions.RectifiedNoisy()
unsupervisedLearningRate = 0.0001
supervisedLearningRate = 0.001
momentumMax = 0.99
trainData = data[train]
trainLabels = labels[train]
# net = db.DBN(4, [1200, 1500, 1000, len(args.emotions)],
# binary=False,
# activationFunction=activationFunction,
# rbmActivationFunctionVisible=rbmActivationFunctionVisible,
# rbmActivationFunctionHidden=rbmActivationFunctionHidden,
# unsupervisedLearningRate=unsupervisedLearningRate,
# supervisedLearningRate=supervisedLearningRate,
# momentumMax=momentumMax,
# nesterovMomentum=True,
# rbmNesterovMomentum=True,
# rmsprop=True,
# miniBatchSize=20,
# hiddenDropout=0.5,
# visibleDropout=0.8,
# momentumFactorForLearningRateRBM=False,
# firstRBMheuristic=False,
# rbmVisibleDropout=1.0,
# rbmHiddenDropout=1.0,
# preTrainEpochs=10,
# sparsityConstraintRbm=False,
# sparsityRegularizationRbm=0.001,
# sparsityTragetRbm=0.01)
#
# net.train(trainData, trainLabels, maxEpochs=200,
# validation=False,
# unsupervisedData=unsupervisedData)
#
# probs, predicted = net.classify(data[test])
net = cnn.CNN(30, 40, len(args.emotions))
net.train(trainData, trainLabels)
probs, predicted = net.classify(data[test])
actualLabels = labels[test]
correct = 0
errorCases = []
for i in xrange(len(test)):
actual = actualLabels[i]
print probs[i]
if predicted[i] == np.argmax(actual):
correct += 1
else:
errorCases.append(i)
print "correct"
print correct
print "percentage correct"
print correct * 1.0 / len(test)
confMatrix = confusion_matrix(np.argmax(actualLabels, axis=1), predicted)
print "confusion matrix"
print confMatrix
with open(args.net_file, "wb") as f:
pickle.dump(net, f)
return net
