tstdata.newSequence()
tstdata.appendLinked(RRopeway[i,:], [7])
trndata._convertToOneOfMany()
tstdata._convertToOneOfMany()
print 'Loaded Dataset!'
#####################
#####################
print 'Building Network'
LSTMClassificationNet = RecurrentNetwork()
LSTMClassificationNet.addInputModule(LinearLayer(trndata.indim, name='in'))
LSTMClassificationNet.addModule(LSTMLayer(11, name='hidden0'))
LSTMClassificationNet.addModule(LSTMLayer(11, name='hidden1'))
LSTMClassificationNet.addOutputModule(LinearLayer(trndata.outdim, name='out'))
LSTMClassificationNet.addModule(BiasUnit(name='bias0'))
LSTMClassificationNet.addModule(BiasUnit(name='bias1'))
LSTMClassificationNet.addConnection(FullConnection(LSTMClassificationNet['in'], LSTMClassificationNet['hidden0'], name='in-to-h0'))
LSTMClassificationNet.addConnection(FullConnection(LSTMClassificationNet['in'], LSTMClassificationNet['hidden1'], name='in-to-h1'))
LSTMClassificationNet.addConnection(FullConnection(LSTMClassificationNet['hidden0'], LSTMClassificationNet['hidden1'], name='h0-to-h1'))
LSTMClassificationNet.addConnection(FullConnection(LSTMClassificationNet['hidden1'], LSTMClassificationNet['out'], name='h1-to-out'))
LSTMClassificationNet.addConnection(FullConnection(LSTMClassificationNet['bias0'], LSTMClassificationNet['hidden0'], name='bias0-to-h0'))
LSTMClassificationNet.addConnection(FullConnection(LSTMClassificationNet['bias1'], LSTMClassificationNet['hidden1'], name='bias1-to-h1'))
LSTMClassificationNet.addRecurrentConnection(FullConnection(LSTMClassificationNet['hidden0'], LSTMClassificationNet['hidden0'], name='h0-to-h0'))
LSTMClassificationNet.addRecurrentConnection(FullConnection(LSTMClassificationNet['hidden1'], LSTMClassificationNet['hidden1'], name='h1-to-h1'))
LSTMClassificationNet.sortModules()
if i%100==0:
tstdata.newSequence()
tstdata.appendLinked(RopewayJoint[i,:], [5])
trndata._convertToOneOfMany()
tstdata._convertToOneOfMany()
print 'Loaded Dataset!'
#####################
#####################
print 'Building Network'
vanillaRNN = RecurrentNetwork()
vanillaRNN.addInputModule(LinearLayer(trndata.indim, name='in'))
vanillaRNN.addModule(SigmoidLayer(44, name='hidden'))
vanillaRNN.addOutputModule(LinearLayer(trndata.outdim, name='out'))
vanillaRNN.addModule(BiasUnit(name='bias'))
vanillaRNN.addConnection(FullConnection(vanillaRNN['in'], vanillaRNN['hidden'], name='c1'))
vanillaRNN.addConnection(FullConnection(vanillaRNN['hidden'], vanillaRNN['out'], name='c2'))
vanillaRNN.addConnection(FullConnection(vanillaRNN['bias'], vanillaRNN['hidden'], name='biasConn'))
vanillaRNN.addRecurrentConnection(FullConnection(vanillaRNN['hidden'], vanillaRNN['hidden'], name='c3'))
vanillaRNN.sortModules()
print "Total Weights:",vanillaRNN.paramdim
trainer = RPropMinusTrainer(vanillaRNN, dataset=trndata, verbose=True, weightdecay=0.01)
tstErrorCount=0
oldtstError=0
trn_error=[]
tst_error=[]
trn_class_accu=[]
