def testNewTraining2(hidden=15, n=None):
d = XORDataSet()
if n is None:
n = buildNetwork(d.indim, hidden, d.outdim, recurrent=False)
provider = ModuleWrapper(d, n, shuffling=False)
algo = vSGDfd(provider, n.params.copy(), callback=printy)
algo.run(1000)
def testNewTraining2(hidden=15, n=None):
d = XORDataSet()
if n is None:
n = buildNetwork(d.indim, hidden, d.outdim, recurrent=False)
provider = ModuleWrapper(d, n, shuffling=False)
algo = vSGDfd(provider, n.params.copy(), callback=printy)
algo.run(1000)
def testCE(hidden=15):
""" Test when doing the classification using the cross-entropy loss."""
d = XORDataSet(True)
n = buildNetwork(d.indim, hidden, d.outdim, recurrent=False)
p0 = n.params.copy()
provider = ClassificationModuleWrapper(d, n, shuffling=False)
algo = SGD(provider, n.params.copy(), callback=printy, learning_rate=0.01, momentum=0.99)
print '\n' * 2
print 'SGD-CE'
algo.run(1000)
n._setParameters(p0)
provider = ClassificationModuleWrapper(d, n, shuffling=False)
algo = vSGDfd(provider, n.params.copy(), callback=printy)
print '\n' * 2
print 'vSGD-CE'
algo.run(1000)
def testTrain():
from algorithms.vsgd import vSGDfd
from core.datainterface import ModuleWrapper
D, net = prepare()
print 'init', rmse(D,net), net.params
print
p0 = net.params.copy()
for bs in range(1, 11):
net._setParameters(p0.copy())
provider = ModuleWrapper(D, net, shuffling=True)
algo = vSGDfd(provider, net.params.copy(),batch_size=bs,
#callback=printy,
#verbose=True,
)
# 1000 epochs
algo.run(1000 *
len(D)/bs)
#print
net._setParameters(algo.bestParameters)
print 'batch size',bs, 'RMSE', rmse(D, net), 'weights', net.params
print
def testCE(hidden=15):
""" Test when doing the classification using the cross-entropy loss."""
d = XORDataSet(True)
n = buildNetwork(d.indim, hidden, d.outdim, recurrent=False)
p0 = n.params.copy()
provider = ClassificationModuleWrapper(d, n, shuffling=False)
algo = SGD(provider,
n.params.copy(),
callback=printy,
learning_rate=0.01,
momentum=0.99)
print '\n' * 2
print 'SGD-CE'
algo.run(1000)
n._setParameters(p0)
provider = ClassificationModuleWrapper(d, n, shuffling=False)
algo = vSGDfd(provider, n.params.copy(), callback=printy)
print '\n' * 2
print 'vSGD-CE'
algo.run(1000)
def testTrain():
from algorithms.vsgd import vSGDfd
from core.datainterface import ModuleWrapper
D, net = prepare()
print 'init', rmse(D, net), net.params
print
p0 = net.params.copy()
for bs in range(1, 11):
net._setParameters(p0.copy())
provider = ModuleWrapper(D, net, shuffling=True)
algo = vSGDfd(
provider,
net.params.copy(),
batch_size=bs,
#callback=printy,
#verbose=True,
)
# 1000 epochs
algo.run(1000 * len(D) / bs)
#print
net._setParameters(algo.bestParameters)
print 'batch size', bs, 'RMSE', rmse(D, net), 'weights', net.params
print