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=[]
tst_class_accu=[]
trnErrorPath='44sigmoid/trn_error'
tstErrorPath='44sigmoid/tst_error'
trnClassErrorPath='44sigmoid/trn_ClassAccu'
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()
# LSTMClassificationNet = buildNetwork(trndata.indim,20,trndata.outdim, hiddenclass=LSTMLayer,
# outclass=SoftmaxLayer, bias=True, recurrent=True, outputbias=False)
print "Total Number of weights:",LSTMClassificationNet.paramdim
trainer = RPropMinusTrainer(LSTMClassificationNet, dataset=trndata, verbose=True, weightdecay=0.01)
tstErrorCount=0
oldtstError=0
trn_error=[]
tst_error=[]
trn_class_accu=[]
tst_class_accu=[]
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 = [] tst_class_accu = [] trnErrorPath = "44sigmoid/trn_error" tstErrorPath = "44sigmoid/tst_error" trnClassErrorPath = "44sigmoid/trn_ClassAccu"
