from scipy import optimize from network import Network nt = Network() nn=nt.create([1, 1000, 1]) lamb=0.3 cost=1 alf = 0.2 xTrain = [[0], [1], [1.9], [2], [3], [3.31], [4], [4.7], [5], [5.1], [6], [7], [8], [9]] yTrain = [[0], [0], [0], [0], [0], [0], [0], [0], [1], [1], [1], [1], [1], [1]] xTest= [[0.4], [1.51], [2.6], [3.23], [4.87], [5.78], [6.334], [7.667], [8.22], [9.1]] yTest = [[0], [0], [0], [0], [0], [1], [1], [1], [1], [1]] theta = nt.unroll(nn['theta']) print(nt.runAll(nn, xTest)) theta = optimize.fmin_cg(nt.costTotal, fprime=nt.backpropagation, x0=theta, args=(nn, xTrain, yTrain, lamb), maxiter=200) print(nt.runAll(nn, xTest))