def kfoldCrossValidation(epochs):
chunks = makeChunks('car.data')
err_list = []
tp_sum = 0
falsep_sum = 0
tn_sum = 0
fn_sum = 0
for fold in range(10):
t0 = time()
# defaulting to 10 chunks
validation_set = chunks[fold]
training_set = []
for chunk in chunks[:fold] + chunks[fold+1:]:
training_set += chunk
# best determined network size
NN = NeuralNetwork(13, 3)
# best determined learning rate, upper and lower classification limits
NN.buildNetwork(0.65,0.9,0.1)
for i in range(epochs): # train the network
NN.trainTheNetwork(training_set)
ttime = time()-t0
er, tp, falsep, tn, fn = NN.testExampleData(validation_set)
err_list.append( er )
tp_sum += tp
falsep_sum += falsep
tn_sum += tn
fn_sum += fn
fp = open('NN-kfoldcrossvalidation.txt','a')
fp.write('{0} {1} {2}\n'. # layers: {0}, neurons: {1}
format(*(NN.n_layers, NN.m_neurons, 0.65)))
fp.write('{0}\n'.format(epochs)) # Number of run epochs: {0}
fp.write('error rate: {0}\n'.format(er)) # error rate: {0}
fp.write('T/F analysis: TP[{0}], FP[{1}], TN[{2}], FN[{3}]\n'.
format(tp, falsep, tn, fn))
fp.write('{0}\n\n'.format(ttime)) # Total training time: {0}
fp.close()
del(NN)
return err_list, tp_sum, falsep_sum, tn_sum, fn_sum
def findOptimaEpoch():
chunks = makeChunks("car.data")
tuning_set = chunks[0]
grow_set = []
for c in chunks[1:]:
grow_set += c
epochs = 80
g = []
r = 0.1
while r < 0.9:
g.append(r)
r += 0.1
# for lr in g: # vary the learning rate
# for blah in range(0,5): # repeat five times
# for n in range(1,16):
t0 = time()
error_rates = []
NN = NeuralNetwork(13, 3) # layers fixed at 3
NN.buildNetwork(0.65,0.9,0.1) # pass the learning rate
for i in range(epochs):
NN.trainTheNetwork(grow_set)
error_rate, tp, fp, tn, fn = NN.testExampleData(tuning_set)
error_rates.append(error_rate)
fp = open('finding-optima-epoch.txt','a')
# layers: {0}, neurons: {1}
fp.write('{0} {1} {2}\n'.format(*(NN.n_layers, NN.m_neurons, 0.65)))
# Number of run epochs: {0}\n
fp.write('{0}\n'.format(epochs))
# error rates: {0}\n
fp.write('{0}\n'.format(str(error_rates)))
# epoch with lowest error rate: {0} -> {1} (zero indexing)\n
fp.write('optima epoch: {0} @ {1}\n'.format(error_rates.index(min(error_rates)), min(error_rates)))
# Total training time: {0}\n\n
fp.write('{0}\n\n'.format(time()-t0))
fp.close()
print(error_rates.index(min(error_rates)))
del(NN)
def findOptimaEpoch():
chunks = makeChunks("car.data")
tuning_set = chunks[0]
grow_set = []
for c in chunks[1:]:
grow_set += c
epochs = 80
g = []
r = 0.1
while r < 0.9:
g.append(r)
r += 0.1
# for lr in g: # vary the learning rate
# for blah in range(0,5): # repeat five times
# for n in range(1,16):
t0 = time()
error_rates = []
NN = NeuralNetwork(13, 3) # layers fixed at 3
NN.buildNetwork(0.65, 0.9, 0.1) # pass the learning rate
for i in range(epochs):
NN.trainTheNetwork(grow_set)
error_rate, tp, fp, tn, fn = NN.testExampleData(tuning_set)
error_rates.append(error_rate)
fp = open("finding-optima-epoch.txt", "a")
# layers: {0}, neurons: {1}
fp.write("{0} {1} {2}\n".format(*(NN.n_layers, NN.m_neurons, 0.65)))
# Number of run epochs: {0}\n
fp.write("{0}\n".format(epochs))
# error rates: {0}\n
fp.write("{0}\n".format(str(error_rates)))
# epoch with lowest error rate: {0} -> {1} (zero indexing)\n
fp.write("optima epoch: {0} @ {1}\n".format(error_rates.index(min(error_rates)), min(error_rates)))
# Total training time: {0}\n\n
fp.write("{0}\n\n".format(time() - t0))
fp.close()
print(error_rates.index(min(error_rates)))
del (NN)
def kfoldCrossValidation(epochs):
chunks = makeChunks("car.data")
err_list = []
tp_sum = 0
falsep_sum = 0
tn_sum = 0
fn_sum = 0
for fold in range(10):
t0 = time()
# defaulting to 10 chunks
validation_set = chunks[fold]
training_set = []
for chunk in chunks[:fold] + chunks[fold + 1 :]:
training_set += chunk
# best determined network size
NN = NeuralNetwork(13, 3)
# best determined learning rate, upper and lower classification limits
NN.buildNetwork(0.65, 0.9, 0.1)
for i in range(epochs): # train the network
NN.trainTheNetwork(training_set)
ttime = time() - t0
er, tp, falsep, tn, fn = NN.testExampleData(validation_set)
err_list.append(er)
tp_sum += tp
falsep_sum += falsep
tn_sum += tn
fn_sum += fn
fp = open("NN-kfoldcrossvalidation.txt", "a")
fp.write("{0} {1} {2}\n".format(*(NN.n_layers, NN.m_neurons, 0.65))) # layers: {0}, neurons: {1}
fp.write("{0}\n".format(epochs)) # Number of run epochs: {0}
fp.write("error rate: {0}\n".format(er)) # error rate: {0}
fp.write("T/F analysis: TP[{0}], FP[{1}], TN[{2}], FN[{3}]\n".format(tp, falsep, tn, fn))
fp.write("{0}\n\n".format(ttime)) # Total training time: {0}
fp.close()
del (NN)
return err_list, tp_sum, falsep_sum, tn_sum, fn_sum
