class FER():
def __init__(self):
#change 3rd param
"""
Usually you should start with a high learning rate and a low momentum. Then you decrease the learning rate over time
and increase the momentum. The idea is to allow more exploration at the beginning of the learning and force convergence
at the end of the learning. Usually you should look at the training error to set up your learning schedule:
if it got stuck, i.e. the error does not change, it is time to decrease your learning rate.
"""
self.neural = ANN(48*48,7,2,1200,10,0.5)
def train(self,dataset):
print "FER Train"
start = time.clock()
weights = self.neural.train(dataset,epoch=3)
result = True
timex = 0
weight_f = open("weights.txt","w")
#print "len(weights)",len(weights)
#print "len(weights[0])",len(weights[0])
#print "weights[0][1]",weights[0][1]
for layer in range(len(weights)):
weight_f.write("Layer "+str(layer)+"\n")
for e in range(len(weights[layer])):
weight_f.write("Node "+str(e)+"\n")
for f in range(len(weights[layer][e])):
weight_f.write(str(weights[layer][e][f]) + " ")
weight_f.write("\n")
weight_f.close()
end = time.clock()
timex = end - start
#train; writes the weight on the file; returns true if sucesss, false otherwise; returns time consumed
return result,timex
def test(self,dataset,weights):
start = time.clock()
correct = 0
accuracy = 0.0
result = True
timex = 0
count = 1
for row in dataset:#temporary set to 10
start = time.clock()
print 'test_data',count
prediction = self.predict(row[1],weights,flag=count)
if prediction == row[0]:
correct+=1
count += 1
end = time.clock()
timex = end - start
print 'test time',timex
accuracy = correct/len(dataset)
end = time.clock()
timex = end - start
#returns the accuracy of the algorithm in floating point format; returns true if sucess, false otherwise; returns time consumed
return accuracy,result,timex
def predict(self,image,weights,flag=-1):
#classify a single image, returns the index of result (see main for the legend index:[0,7])
if flag == 1:
self.neural.set_weight(weights)
elif flag == -1:
self.neural.set_weight(weights)
output = list(self.neural.classify(image))
predicted_output = output.index(max(output))
print "output:",output
return predicted_output
