def trainSGD(netConfig, x, y, lmb):
th1, th2 = SimpleNN2.initRandomThetas(netConfig)
alpha = 0.1
costs = []
numSamples = x.shape[0]
miniBatchSize = 200
for i in range((numSamples-2)//miniBatchSize):
fr = i*miniBatchSize
to = (i+1)*miniBatchSize
xi = x[fr:to,:]
yi = y[fr:to]
costBefore = 0.0
if len(costs) > 0:
costBefore = costs[-1]
else:
costBefore = SimpleNN2.computeCost(netConfig, th1, th2, xi, yi, lmb)
grad1, grad2 = SimpleNN2.computeGrad(netConfig, th1, th2, xi, yi, lmb)
alpha = findOptimalAlpha(netConfig, th1, th2, xi, yi, lmb, grad1, grad2, alpha/2, alpha*2)
th1p = th1 - alpha*grad1
th2p = th2 - alpha*grad2
costAfter = SimpleNN2.computeCost(netConfig, th1p, th2p, xi, yi, lmb)
if costAfter <= costBefore:
costs.append(costAfter)
th1 = th1p
th2 = th2p
#else:
# # Find optimal alpha in a wide range
# alpha = findOptimalAlpha(netConfig, th1, th2, xi, yi, lmb, grad1, grad2, alpha/50, alpha)
# th1p = th1 - alpha*grad1
# th2p = th2 - alpha*grad2
# costAfter = SimpleNN2.computeCost(netConfig, th1p, th2p, xi, yi, lmb)
# if costAfter <= costBefore:
# costs.append(costAfter)
# th1 = th1p
# th2 = th2p
if len(costs) > 0 and len(costs) % 10 == 0:
print('Epoch', len(costs), 'with cost', costs[-1], 'and alpha', alpha)
return th1, th2
def trainSciPy2(netConfig, x, y, lmb):
th1, th2 = SimpleNN2.initRandomThetas(netConfig)
combinedTheta = SimpleNN2.combineThetas(th1, th2)
optimizedTheta = minimize(
fun = lambda p: SimpleNN2.computeCostComb(netConfig, p, x, y, lmb) ,
x0 = combinedTheta,
method = 'L-BFGS-B',
jac = lambda p: SimpleNN2.computeGradComb(netConfig, p, x, y, lmb),
#callback = lambda xk: print("Iteration complete!"),
options={'disp': False}) #'maxiter' : 5, 'eps' : 1e-10, 'gtol' : 1e-10
return SimpleNN2.splitThetas(netConfig, optimizedTheta.x)
def trainGradientDescent2(netConfig, x, y, lmb):
th1, th2 = SimpleNN2.initRandomThetas(netConfig)
alpha = 2.0
costs = []
while True:
costBefore = SimpleNN2.computeCost(netConfig, th1, th2, x, y, lmb)
grad1, grad2 = SimpleNN2.computeGrad(netConfig, th1, th2, x, y, lmb)
th1p = th1 - alpha*grad1
th2p = th2 - alpha*grad2
costAfter = SimpleNN2.computeCost(netConfig, th1p, th2p, x, y, lmb)
skipUpdate = False
if costAfter > costBefore:
alpha = alpha / 1.01
skipUpdate = True
print("Decrease alpha due to cyclic behaviour")
if not skipUpdate:
costs.append(costAfter)
th1 = th1p
th2 = th2p
if len(costs) > 0 and len(costs) % 10 == 0:
print('Epoch', len(costs), 'with cost', costs[-1], 'and alpha', alpha)
if len(costs) > 2 and abs(costs[-2] - costs[-1]) < 0.00001:
if alpha < 0.02:
break
else:
print("Decrease alpha due to close costs")
alpha = alpha / 1.5
return th1, th2
