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