def main():
train, val = getData()
train_errs = []
val_errs = []
for hiddensize in range(10, 100, 5):
net = buildNetwork(39,
hiddensize,
1,
hiddenclass=SigmoidLayer,
outclass=SigmoidLayer)
trainer = BackpropTrainer(net, train)
for i in range(20):
trainer.train()
verr = ModuleValidator.MSE(net, val)
print hiddensize, ": ", verr
val_errs.append(verr)
train_errs.append(trainer.train())
return (train_errs, val_errs)
trainer = ReinforcedTrainer(module=nn, rewarder=rewardFunc)
from pybrain.tools.validation import ModuleValidator
import thread
def userthread():
from IPython.Shell import IPShellEmbed
ipshell = IPShellEmbed()
ipshell()
#thread.start_new_thread(userthread, ())
# carry out the training
while True:
trndata = generateData(nseq=20, ratevarlimit=random.uniform(0.0, 0.3))
tstdata = generateData(nseq=20)
trainer.setData(trndata)
trainer.train()
trnresult = 100. * (ModuleValidator.MSE(nn, trndata))
tstresult = 100. * (ModuleValidator.MSE(nn, tstdata))
print "train error: %5.2f%%" % trnresult, ", test error: %5.2f%%" % tstresult
s = getRandomSeq(100, ratevarlimit=random.uniform(0.0, 1.0))
print " real:", seqStr(s)
print " nn:", getSeqOutputFromNN(nn, s)
starttime = time.time()
print("Training Network 6")
trainer6 = backprop.BackpropTrainer(net6, dataset=train)
score6 = trainer6.train()
trainerrorlist.append(score6)
timeTaken.append(time.time() - starttime)
print("BackPropTrainer 1 has an error rate of " + score1.__str__())
print("BackPropTrainer 2 has an error rate of " + score2.__str__())
print("BackPropTrainer 3 has an error rate of " + score3.__str__())
print("BackPropTrainer 4 has an error rate of " + score4.__str__())
print("BackPropTrainer 5 has an error rate of " + score5.__str__())
print("BackPropTrainer 6 has an error rate of " + score6.__str__())
testErrorArray = []
score1a = ModuleValidator.MSE(net1, test)
testErrorArray.append(score1a)
score2a = ModuleValidator.MSE(net2, test)
testErrorArray.append(score2a)
score3a = ModuleValidator.MSE(net3, test)
testErrorArray.append(score3a)
score4a = ModuleValidator.MSE(net4, test)
testErrorArray.append(score4a)
score5a = ModuleValidator.MSE(net5, test)
testErrorArray.append(score5a)
score6a = ModuleValidator.MSE(net6, test)
testErrorArray.append(score6a)
print("Printing mean square error values")
print("BackPropTrainer 1 has a test error rate of " + score1a.__str__())
print("BackPropTrainer 2 has a test error rate of " + score2a.__str__())
numeroDeAcessos = 10
numeroDeEpocasPorAcesso = 50
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.axis([0, 2 * math.pi, -1.5, 1.5])
fig1.hold()
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.axis([-50, numeroDeAcessos * numeroDeEpocasPorAcesso + 50, 0.00001, 4])
ax2.set_yscale('log')
fig2.hold()
meansq = ModuleValidator()
erro2 = meansq.MSE(treinadorSupervisionado.module, dados)
print erro2
ax2.plot([0], [erro2], 'bo')
tempoPausa = 1
for i in range(numeroDeAcessos):
treinadorSupervisionado.trainEpochs(numeroDeEpocasPorAcesso)
meansq = ModuleValidator()
erro2 = meansq.MSE(treinadorSupervisionado.module, dados)
print erro2
ax1.plot(dados['input'],
dados['target'],
'bo',
markersize=7,
markeredgewidth=0)
ax1.plot(dados['input'],
timeSpentBuilding.append(end - start)
print("Trainer created")
start = time.clock()
trainer = BackpropTrainer(LSTMNetwork, dataset=train)
error = trainer.train()
end = time.clock()
print str(end - start) + " seconds for the training the network"
print("LSTMNetwork" + str(LSTMNetworkNumber) + " has an error rate of: " +
str(error))
overallError.append(error)
timeSpentTraining.append(end - start)
print("Validating the trained network to the testing one")
start = time.clock()
validation = ModuleValidator.MSE(LSTMNetwork, test)
end = time.clock()
print str(end - start) + " seconds for testing the network"
print("Backpropagation Trainer " + str(LSTMNetworkNumber) +
" has test error rate of: " + str(validation))
testComparison.append(validation)
timeSpentTesting.append(end - start)
LSTMNetworkNumber += 1
# Values for OverallError and TestComparison
print str(overallError)
print str(testComparison)
# Plot of network's error after training
plt.plot([1, 2, 3, 4], overallError)
def eval_nn(params):
global nn, trndata
nn.reset()
nn.params[:] = params
return ModuleValidator.MSE(nn, trndata)
