tf = open('novelty_plant.txt','r')
first = True
for line in tf.readlines():
if (not first):
data = [float(x) for x in line.strip().split('\t') if x != '']
# indata = tuple(data[:6])
# outdata = tuple(data[6:])
ds.addSample(data)
first = False
n = buildNetwork(ds.dim,8,8,1,recurrent=True)
t = DeepBeliefTrainer(n,ds, epochs=50)
t.trainEpochs(1)
t.testOnData(ds, verbose= True)
ds.addSample((0, 0), (0,))
ds.addSample((0, 1), (1,))
ds.addSample((1, 0), (1,))
ds.addSample((1, 1), (0,))
for input, target in ds:
print(input, target)
#net = buildNetwork(2, 3, 1, bias=True, hiddenclass=TanhLayer)#1000
# net = buildNetwork(2, 6, 1, bias=True) # 3000
net = buildNetwork(2, 3, 1, bias=True)
trainer = BackpropTrainer(net, ds)
net.addConnection(FullConnection(bias, h1))
net.addConnection(FullConnection(bias, h2))
net.addConnection(FullConnection(bias, out))
net.sortModules()
return net
if __name__ == "__main__":
import GwData
data = GwData.GwData()
xs = get_binary_data(data)
ys = data.labels_for("50")
sdataset = SupervisedDataSet(xs.shape[1], 1)
udataset = UnsupervisedDataSet(xs.shape[1])
for i, x in enumerate(xs):
sdataset.addSample(x, ys[i])
udataset.addSample(x)
epochs = 100
layerDims = [xs.shape[1], 300, 100, 2]
#net = buildNetwork(*layerDims)
net = custom_build_network(layerDims)
trainer = DeepBeliefTrainer(net, dataset=udataset)
#trainer = DeepBeliefTrainer(net, dataset=sdataset)
trainer.trainEpochs(epochs)
net.addConnection(FullConnection(bias, h1))
net.addConnection(FullConnection(bias, h2))
net.addConnection(FullConnection(bias, out))
net.sortModules()
return net
if __name__ == "__main__":
import GwData
data = GwData.GwData()
xs = get_binary_data(data)
ys = data.labels_for("50")
sdataset = SupervisedDataSet(xs.shape[1], 1)
udataset = UnsupervisedDataSet(xs.shape[1])
for i,x in enumerate(xs):
sdataset.addSample(x, ys[i])
udataset.addSample(x)
epochs = 100
layerDims = [xs.shape[1], 300, 100, 2]
#net = buildNetwork(*layerDims)
net = custom_build_network(layerDims)
trainer = DeepBeliefTrainer(net, dataset=udataset)
#trainer = DeepBeliefTrainer(net, dataset=sdataset)
trainer.trainEpochs(epochs)
