def testAlgos(dim=3):
# generate a dataset
f = StochQuad(noiseLevel=0.2)
fw = FunctionWrapper(dim, f, record_samples=True)
[fw.nextSamples(1) for _ in range(100)]
ds = fw._seen
dw = DataFunctionWrapper(ds, f, shuffling=False)
x0 = ones(dim)
for algoclass in [SGD, SGD, OracleSGD, Almeida, Amari, RMSProp, AdaGrad, MomentumSGD, AveragingSGD]:
dw.reset()
print algoclass.__name__
algo = algoclass(dw, x0, callback=printy)
algo.run(16)
def testAlgos(dim=3):
# generate a dataset
f = StochQuad(noiseLevel=0.2)
fw = FunctionWrapper(dim, f, record_samples=True)
[fw.nextSamples(1) for _ in range(100)]
ds = fw._seen
dw = DataFunctionWrapper(ds, f, shuffling=False)
x0 = ones(dim)
for algoclass in [SGD, SGD, OracleSGD, Almeida, Amari, RMSProp, AdaGrad,
MomentumSGD, AveragingSGD]:
dw.reset()
print algoclass.__name__
algo = algoclass(dw, x0, callback=printy)
algo.run(16)
def testMinibatch(dim=4):
f = StochQuad(noiseLevel=0.2)
fw = FunctionWrapper(dim, f, record_samples=True)
x0 = ones(dim)
for mb in [1, 3, 15, 250]:
print "minibatch", mb
algo = SGD(fw, x0, callback=printy, batch_size=mb, learning_rate=0.1)
algo.run(10)
print
[fw.nextSamples(1) for _ in range(2500)]
dw = DataFunctionWrapper(fw._seen, f, shuffling=False)
print "Fixed samples"
for mb in [1, 3, 15, 250]:
print "minibatch", mb
dw.reset()
algo = SGD(dw, x0, callback=printy, batch_size=mb, learning_rate=0.1)
algo.run(10)
print
def testMinibatch(dim=4):
f = StochQuad(noiseLevel=0.2)
fw = FunctionWrapper(dim, f, record_samples=True)
x0 = ones(dim)
for mb in [1,3,15,250]:
print 'minibatch', mb
algo = SGD(fw, x0, callback=printy, batch_size=mb, learning_rate=0.1)
algo.run(10)
print
[fw.nextSamples(1) for _ in range(2500)]
dw = DataFunctionWrapper(fw._seen, f, shuffling=False)
print 'Fixed samples'
for mb in [1,3,15,250]:
print 'minibatch', mb
dw.reset()
algo = SGD(dw, x0, callback=printy, batch_size=mb, learning_rate=0.1)
algo.run(10)
print
