def __init__(self,
nNeurons,
mNeurons,
learningrate=0.01,
neighbourdecay=0.9999):
# KohonenMap of IMG_SIZE dimensions and a 2x2 grid. (try 4x1 later)
self.som = KohonenMap(IMG_SIZE, nNeurons, mNeurons)
self.som.learningrate = learningrate
self.som.neighbourdecay = neighbourdecay
self.emo_clusters = [[-1] * mNeurons for x in range(nNeurons)]
def __init__(self, nNeurons, mNeurons, learningrate=0.01, neighbourdecay=0.9999):
# KohonenMap of IMG_SIZE dimensions and a 2x2 grid. (try 4x1 later)
self.som = KohonenMap(IMG_SIZE, nNeurons, mNeurons);
self.som.learningrate = learningrate
self.som.neighbourdecay = neighbourdecay
self.emo_clusters = [ [-1]*mNeurons for x in range(nNeurons)]
class UnsupervisedFacialClassifier:
som = None
emo_clusters = []
def __init__(self, nNeurons, mNeurons, learningrate=0.01, neighbourdecay=0.9999):
# KohonenMap of IMG_SIZE dimensions and a 2x2 grid. (try 4x1 later)
self.som = KohonenMap(IMG_SIZE, nNeurons, mNeurons);
self.som.learningrate = learningrate
self.som.neighbourdecay = neighbourdecay
self.emo_clusters = [ [-1]*mNeurons for x in range(nNeurons)]
def train(self, inputData, epochs, test_proportion=0.25, verbose=True):
# Sort the known emotions into image piles, to assosciate a cluster with an
# emotion after training, store that assosciation in emo_cluster
cluster_identification_images = {
Emotion.SAD: [],
Emotion.SMILING: [],
Emotion.CALM: [],
Emotion.ASTONISHED: []
}
# Dictionary to return with performance metrics etc.
train_perf = {}
train_faces = []
test_faces = []
for entry in inputData:
if (random() > test_proportion):
# Build training set with 75% of inputData
(emotion, img) = entry
cluster_identification_images[emotion].append(img)
train_faces.append(img)
else:
# Build test set with the other 25%
test_faces.append(entry)
i = 0
#while (self.som.neighbours > 0.5 or i < epochs):
while (i < epochs):
for img in train_faces:
# Not sure if this can be done with som.activateOnDataset()
self.som.activate(img)
self.som.backward()
i += 1
if verbose:
if not (i % 20):
print "SOM neighbors: %f" % self.som.neighbours
print "SOM winner err: %f" % self.som.winner_error
# Finished training SOM
train_perf['epochs'] = i
train_perf['final_som_winner_err'] = self.som.winner_error
# Correlate N SOM clusters with N emotions
training_error = []
for emotion in cluster_identification_images.keys():
emo_count = zeros((self.som.nNeurons, self.som.mNeurons))
for img in cluster_identification_images[emotion]:
self.som.activate(img)
emo_count[self.som.winner[0]][self.som.winner[1]] += 1
dominant_node = maximum_position(emo_count)
training_error.append( 1.0 -
(1.0*emo_count[dominant_node[0]][dominant_node[1]]/
len(cluster_identification_images[emotion]))
)
self.emo_clusters[dominant_node[0]][dominant_node[1]] = emotion
# Record training error
train_perf['training_error'] = training_error
train_perf['avg_training_error'] = mean(training_error)
train_perf['emo_clusters'] = self.emo_clusters
# Start the testing set
if verbose: print "Testing:"
error_count = 0
for entry in test_faces:
(expectd_emo, img) = entry
determined_emo = self.classify(img, verbose=False)
if (expectd_emo != determined_emo):
error_count += 1
if verbose: print "{>_<} Expected %s, got %s" % \
(Emotion.to_s[determined_emo], Emotion.to_s[expectd_emo])
else:
if verbose: print "{^-^} Classified a %s face correctly." % \
Emotion.to_s[determined_emo]
train_perf['avg_testing_error'] = (1.0*error_count / len(test_faces))
if verbose: print train_perf
return train_perf
def classify(self, facialData, verbose=True ):
self.som.activate(facialData)
emotion = self.emo_clusters[self.som.winner[0]][self.som.winner[1]]
if verbose:
print "This face looks %s to me" % Emotion.to_s[emotion]
return emotion
from pylab import ion, ioff, figure, draw, contourf, clf, show, hold, plot
from scipy import diag, arange, meshgrid, where
from numpy.random import multivariate_normal
from numpy import zeros
cluster_data = []
#means = [(-1,0),(2,4),(3,1)]
means = [(-10,10),(10,10),(0,0),(10,-10)]
cov = [diag([1,1]), diag([0.5,1.2]), diag([1.5,0.7]), diag([1.5,0.7])]
for n in xrange(400):
for klass in range(len(means)):
cluster_data.append(multivariate_normal(means[klass],cov[klass]))
som = KohonenMap(2, 9, 9)
pylab.ion()
p = pylab.plot(som.neurons[:,:,0].flatten(), som.neurons[:,:,1].flatten(), 's')
pylab.axis([-15,15,-15,15])
i = 0
for j in range(40):
print j
for data in cluster_data:
i += 1
# one forward and one backward (training) pass
som.activate(data)
som.backward()
# plot every 100th step
from pylab import ion, ioff, figure, draw, contourf, clf, show, hold, plot
from scipy import diag, arange, meshgrid, where
from numpy.random import multivariate_normal
from numpy import zeros
cluster_data = []
#means = [(-1,0),(2,4),(3,1)]
means = [(-10, 10), (10, 10), (0, 0), (10, -10)]
cov = [diag([1, 1]), diag([0.5, 1.2]), diag([1.5, 0.7]), diag([1.5, 0.7])]
for n in xrange(400):
for klass in range(len(means)):
cluster_data.append(multivariate_normal(means[klass], cov[klass]))
som = KohonenMap(2, 9, 9)
pylab.ion()
p = pylab.plot(som.neurons[:, :, 0].flatten(), som.neurons[:, :, 1].flatten(),
's')
pylab.axis([-15, 15, -15, 15])
i = 0
for j in range(40):
print j
for data in cluster_data:
i += 1
# one forward and one backward (training) pass
som.activate(data)
som.backward()
class UnsupervisedFacialClassifier:
som = None
emo_clusters = []
def __init__(self,
nNeurons,
mNeurons,
learningrate=0.01,
neighbourdecay=0.9999):
# KohonenMap of IMG_SIZE dimensions and a 2x2 grid. (try 4x1 later)
self.som = KohonenMap(IMG_SIZE, nNeurons, mNeurons)
self.som.learningrate = learningrate
self.som.neighbourdecay = neighbourdecay
self.emo_clusters = [[-1] * mNeurons for x in range(nNeurons)]
def train(self, inputData, epochs, test_proportion=0.25, verbose=True):
# Sort the known emotions into image piles, to assosciate a cluster with an
# emotion after training, store that assosciation in emo_cluster
cluster_identification_images = {
Emotion.SAD: [],
Emotion.SMILING: [],
Emotion.CALM: [],
Emotion.ASTONISHED: []
}
# Dictionary to return with performance metrics etc.
train_perf = {}
train_faces = []
test_faces = []
for entry in inputData:
if (random() > test_proportion):
# Build training set with 75% of inputData
(emotion, img) = entry
cluster_identification_images[emotion].append(img)
train_faces.append(img)
else:
# Build test set with the other 25%
test_faces.append(entry)
i = 0
#while (self.som.neighbours > 0.5 or i < epochs):
while (i < epochs):
for img in train_faces:
# Not sure if this can be done with som.activateOnDataset()
self.som.activate(img)
self.som.backward()
i += 1
if verbose:
if not (i % 20):
print "SOM neighbors: %f" % self.som.neighbours
print "SOM winner err: %f" % self.som.winner_error
# Finished training SOM
train_perf['epochs'] = i
train_perf['final_som_winner_err'] = self.som.winner_error
# Correlate N SOM clusters with N emotions
training_error = []
for emotion in cluster_identification_images.keys():
emo_count = zeros((self.som.nNeurons, self.som.mNeurons))
for img in cluster_identification_images[emotion]:
self.som.activate(img)
emo_count[self.som.winner[0]][self.som.winner[1]] += 1
dominant_node = maximum_position(emo_count)
training_error.append(
1.0 - (1.0 * emo_count[dominant_node[0]][dominant_node[1]] /
len(cluster_identification_images[emotion])))
self.emo_clusters[dominant_node[0]][dominant_node[1]] = emotion
# Record training error
train_perf['training_error'] = training_error
train_perf['avg_training_error'] = mean(training_error)
train_perf['emo_clusters'] = self.emo_clusters
# Start the testing set
if verbose: print "Testing:"
error_count = 0
for entry in test_faces:
(expectd_emo, img) = entry
determined_emo = self.classify(img, verbose=False)
if (expectd_emo != determined_emo):
error_count += 1
if verbose: print "{>_<} Expected %s, got %s" % \
(Emotion.to_s[determined_emo], Emotion.to_s[expectd_emo])
else:
if verbose: print "{^-^} Classified a %s face correctly." % \
Emotion.to_s[determined_emo]
train_perf['avg_testing_error'] = (1.0 * error_count / len(test_faces))
if verbose: print train_perf
return train_perf
def classify(self, facialData, verbose=True):
self.som.activate(facialData)
emotion = self.emo_clusters[self.som.winner[0]][self.som.winner[1]]
if verbose:
print "This face looks %s to me" % Emotion.to_s[emotion]
return emotion
