net.addConnection(FullConnection(net["hidden2"], net["output"], name="c5"))
net.addRecurrentConnection(FullConnection(net["hidden1"], net["hidden1"], name="c6"))
net.sortModules()
# net = buildNetwork(n_input, 256, n_output, hiddenclass=LSTMLayer, outclass=TanhLayer, outputbias=False, recurrent=True)
# net = NetworkReader.readFrom('signal_weight.xml')
# train network
trainer = RPropMinusTrainer(net, dataset=training_dataset, verbose=True, weightdecay=0.01)
# trainer = BackpropTrainer(net, dataset=training_dataset, learningrate = 0.04, momentum = 0.96, weightdecay = 0.02, verbose = True)
for i in range(100):
# train the network for 1 epoch
trainer.trainEpochs(5)
# evaluate the result on the training and test data
trnresult = percentError(trainer.testOnClassData(), training_dataset['class'])
tstresult = percentError(trainer.testOnClassData(dataset=testing_dataset), testing_dataset['class'])
# print the result
print("epoch: %4d" % trainer.totalepochs, \
" train error: %5.2f%%" % trnresult, \
" test error: %5.2f%%" % tstresult)
if tstresult <= 0.5 :
print('Bingo !!!!!!!!!!!!!!!!!!!!!!')
break
# export network
NetworkWriter.writeToFile(net, 'signal_weight.xml')
# run test
actual_price = np.array([n[3] for n in testing_input])
