def trainNetwork():
global nnet
trainX = []
trainY = []
file = open('finaldata.txt')
for line in file:
data = []
for d in line.split(','):
if d != '\n':
data.append(float(d))
trainX.append(data)
file.close()
file = open('finalout.txt')
for line in file:
data = []
for d in line.split(','):
if d != '\n':
if float(d) == 2:
data.append(0)
else:
data.append(float(d))
trainY.append(data)
file.close()
trainX, trainY = np.array(trainX), np.array(trainY)
#CREATE NETWORK
nnet = cb.Network()
#CREATE LAYERS
Lin = cb.Layer(12)
Lhidden = cb.Layer(12, cb.LogisticNeuron)
Lout = cb.Layer(1, cb.LogisticNeuron)
bias = cb.Layer(1, cb.BiasUnit)
#ADD LAYERS TO NETWORK
nnet.addInputLayer(Lin)
nnet.addLayer(Lhidden)
nnet.addOutputLayer(Lout)
nnet.addAutoInputLayer(bias)
#CONNECT LAYERS
Lin.connectTo(Lhidden)
Lhidden.connectTo(Lout)
bias.connectTo(Lhidden)
bias.connectTo(Lout)
#CREATE BATCH TRAINER
rate = 0.1
batch = cb.Trainer(nnet, trainX, trainY, rate)
#TRAIN
# t1 = time()
batch.epochs(100)
Y = [[0.0]] X.append([1.0, 0.0]) Y.append([1.0]) X.append([0.0, 1.0]) Y.append([1.0]) X.append([1.0, 1.0]) Y.append([0.0]) #CONVERT DATA TO NUMPY ARRAY X, Y = np.array(X), np.array(Y) #CREATE NETWORK nnet = cb.Network() #CREATE LAYERS Lin = cb.Layer(2, names=['a', 'b']) Lhidden = cb.Layer(2, cb.LogisticNeuron, names=['c', 'd']) Lout = cb.Layer(1, cb.LogisticNeuron, names=['e']) bias = cb.Layer(1, cb.BiasUnit, names=['bias']) #ADD LAYERS TO NETWORK nnet.addInputLayer(Lin) nnet.addLayer(Lhidden) nnet.addOutputLayer(Lout) nnet.addAutoInputLayer(bias) #CONNECT LAYERS Lin.connectTo(Lhidden) Lhidden.connectTo(Lout) bias.connectTo(Lhidden) bias.connectTo(Lout)