def testPlot4(trials=40, maxsteps=512):
fun = StochQuad(noiseLevel=100., curvature=1)
fwrap = FunctionWrapper(trials, fun, record_samples=True)
fwrap.nextSamples(100000)
fwrap = DataFunctionWrapper(fwrap._seen, fun, shuffling=False)
for i, (aclass, aparams) in enumerate([
(vSGD, {
'batch_size': 1
}),
(vSGDfd, {
'batch_size': 1
}),
]):
pylab.subplot(2, 1, 2)
fwrap.reset()
ls = lossTraces(fwrap=fwrap,
aclass=aclass,
dim=trials,
maxsteps=maxsteps,
algoparams=aparams)
plotWithPercentiles(ls, algo_colors[aclass], aclass.__name__)
pylab.semilogy()
pylab.xlim(0, maxsteps)
pylab.legend()
pylab.subplot(2, 2, i + 1)
fwrap.reset()
plotHeatmap(fwrap, aclass, aparams, trials, maxsteps)
pylab.show()
def testPlot2(trials=51, maxsteps=5000):
f = FunctionWrapper(trials, OptimumJumper(StochQuad(noiseLevel=10, curvature=1), jumptime=1000, jumpdist_std=1))
for aclass, aparams in [#(SGD, {'learning_rate':0.1}),
#(SGD, {'learning_rate':0.01}),
#(AveragingSGD, {'learning_rate':0.01}),
#(AveragingSGD, {'learning_rate':0.01, 'fixedDecay':0.1}),
#(AveragingSGD, {'learning_rate':0.01, 'fixedDecay':0.1}),
#(AveragingSGD, {'learning_rate':0.1}),
#(AveragingSGD, {'learning_rate':1.0}),
(AveragingOracle, {}),
(AveragingOracle, {"fixedDecay":0.1}),
#(AveragingOracle, {"fixedDecay":0.01}),
(AdaptivelyAveragingOracle, {}),
#(AdaGrad, {'init_lr':0.3}),
#(Amari, {'init_lr':0.1, 'time_const':100}),
#(RMSProp, {'init_lr':0.1}),
(OracleSGD, {}),
#(vSGD, {'verbose':False}),
#(vSGDfd, {}),
]:
ls = lossTraces(fwrap=f, aclass=aclass, dim=trials,
maxsteps=maxsteps, algoparams=aparams)
plotWithPercentiles(ls, algo_colors[aclass], aclass.__name__)
pylab.semilogy()
pylab.xlim(0, maxsteps)
pylab.legend()
pylab.show()
def testPlot4(trials=40, maxsteps=512):
fun = StochQuad(noiseLevel=100., curvature=1)
fwrap = FunctionWrapper(trials, fun, record_samples=True)
fwrap.nextSamples(100000)
fwrap = DataFunctionWrapper(fwrap._seen, fun, shuffling=False)
for i, (aclass, aparams) in enumerate([(vSGD, {'batch_size':1}),
(vSGDfd, {'batch_size':1}),
]):
pylab.subplot(2, 1, 2)
fwrap.reset()
ls = lossTraces(fwrap=fwrap, aclass=aclass, dim=trials,
maxsteps=maxsteps, algoparams=aparams)
plotWithPercentiles(ls, algo_colors[aclass], aclass.__name__)
pylab.semilogy()
pylab.xlim(0, maxsteps)
pylab.legend()
pylab.subplot(2, 2, i + 1)
fwrap.reset()
plotHeatmap(fwrap, aclass, aparams, trials, maxsteps)
pylab.show()
def testPlot2(trials=51, maxsteps=5000):
f = FunctionWrapper(
trials,
OptimumJumper(StochQuad(noiseLevel=10, curvature=1),
jumptime=1000,
jumpdist_std=1))
for aclass, aparams in [ #(SGD, {'learning_rate':0.1}),
#(SGD, {'learning_rate':0.01}),
#(AveragingSGD, {'learning_rate':0.01}),
#(AveragingSGD, {'learning_rate':0.01, 'fixedDecay':0.1}),
#(AveragingSGD, {'learning_rate':0.01, 'fixedDecay':0.1}),
#(AveragingSGD, {'learning_rate':0.1}),
#(AveragingSGD, {'learning_rate':1.0}),
(AveragingOracle, {}),
(AveragingOracle, {
"fixedDecay": 0.1
}),
#(AveragingOracle, {"fixedDecay":0.01}),
(AdaptivelyAveragingOracle, {}),
#(AdaGrad, {'init_lr':0.3}),
#(Amari, {'init_lr':0.1, 'time_const':100}),
#(RMSProp, {'init_lr':0.1}),
(OracleSGD, {}),
#(vSGD, {'verbose':False}),
#(vSGDfd, {}),
]:
ls = lossTraces(fwrap=f,
aclass=aclass,
dim=trials,
maxsteps=maxsteps,
algoparams=aparams)
plotWithPercentiles(ls, algo_colors[aclass], aclass.__name__)
pylab.semilogy()
pylab.xlim(0, maxsteps)
pylab.legend()
pylab.show()
