def batchloop():
c = 0
prevperc = -1.
terr = [0.0]
numdigs = 2
tt = TT("iter progress", verbose=verbose)
tt.tick()
while datafeeder.hasnextbatch():
perc = round(c*100.*(10**numdigs)/datafeeder._numbats)/(10**numdigs)
if perc > prevperc:
s = ("%."+str(numdigs)+"f%% \t error: %.3f") % (perc, terr[0])
tt.live(s)
prevperc = perc
sampleinps = datafeeder.nextbatch()
try:
eterr = trainf(*sampleinps)
if len(terr) != len(eterr) and terr.count(0.0) == len(terr):
terr = [0.0]*len(eterr)
except Exception, e:
raise e
if self.average_err is True:
terr = [xterr*(1.0*(c)/(c+1)) + xeterr*(1.0/(c + 1)) for xterr, xeterr in zip(terr, eterr)]
else:
terr = [xterr + xeterr for xterr, xeterr in zip(terr, eterr)]
c += 1
def batchloop():
c = 0
prevperc = -1.
terr = [0.0]
numdigs = 2
tt = TT("iter progress", verbose=verbose)
tt.tick()
while datafeeder.hasnextbatch():
perc = round(c * 100. * (10**numdigs) /
datafeeder._numbats) / (10**numdigs)
if perc > prevperc:
s = ("%." + str(numdigs) +
"f%% \t error: %.3f") % (perc, terr[0])
tt.live(s)
prevperc = perc
sampleinps = datafeeder.nextbatch()
sampleinps = sampletransf(*sampleinps)
try:
eterr = trainf(*sampleinps)
if len(terr) != len(eterr) and terr.count(0.0) == len(
terr):
terr = [0.0] * len(eterr)
except Exception, e:
raise e
if self.average_err is True:
terr = [
xterr * (1.0 * (c) / (c + 1)) + xeterr *
(1.0 / (c + 1)) for xterr, xeterr in zip(terr, eterr)
]
else:
terr = [
xterr + xeterr for xterr, xeterr in zip(terr, eterr)
]
c += 1
def batchloop():
c = 0
prevperc = -1.
terrs = [[0.0] if tf is not None else None for tf in trainfs]
numdigs = 2
tt = TT("iter progress", verbose=verbose)
tt.tick()
for dataf in datafeeders:
if dataf is not None:
dataf.reset()
while datafeeders[0].hasnextbatch():
perc = round(c * 100. * (10**numdigs) /
datafeeders[0].getnumbats()) / (10**numdigs)
if perc > prevperc:
s = ("%." + str(numdigs) + "f%% \t error: %s") \
% (perc, " - ".join(map(lambda x: "%.3f" % x[0], terrs)))
tt.live(s)
prevperc = perc
for df in datafeeders:
if not df.hasnextbatch():
df.reset()
sampleinps = [df.nextbatch() for df in datafeeders]
# embed()
sampleinps = [
stf(*si, phase=phase)
for (stf, si) in zip(sampletransfs, sampleinps)
]
try:
eterrs = [tf(*si) for (tf, si) in zip(trainfs, sampleinps)]
for i in range(len(terrs)):
if len(terrs[i]) != len(
eterrs[i]) and terrs[i].count(0.0) == len(
terrs[i]):
terrs[i] = [0.0] * len(eterrs[i])
except Exception, e:
raise e
for i, subt in enumerate(this.spts):
if subt.original.average_err is True:
terrs[i] = [
xterr * (1.0 * (c) / (c + 1)) + xeterr * (1.0 /
(c + 1))
for xterr, xeterr in zip(terrs[i], eterrs[i])
]
else:
terrs[i] = [
xterr + xeterr
for xterr, xeterr in zip(terrs[i], eterrs[i])
]
c += 1
def batchloop():
c = 0
numex = 0
prevperc = -1.
terr = [0.0]
numdigs = 2
tt = TT("iter progress", verbose=verbose)
tt.tick()
while datafeeder.hasnextbatch():
perc = round(c * 100. * (10**numdigs) /
datafeeder._numbats) / (10**numdigs)
if perc > prevperc:
terr0 = terr[0] * 1.0 / numex if numex > 0 else 0.0
s = ("%." + str(numdigs) + "f%% \t error: %.3f") % (perc,
terr0)
tt.live(s)
prevperc = perc
sampleinps, batsize = datafeeder.nextbatch(withbatchsize=True)
numex += batsize
sampleinps = sampletransf(*sampleinps)
eterr = trainf(*sampleinps)
if len(terr) != len(eterr) and terr.count(0.0) == len(terr):
terr = [0.0] * len(
eterr
) # ensure compatible size of terr (number of output scores)
if self.average_err is True:
terr = [
xterr + xeterr * batsize
for xterr, xeterr in zip(terr, eterr)
]
else:
terr = [
xterr + xeterr for xterr, xeterr in zip(terr, eterr)
]
c += 1
tt.stoplive()
if self.average_err is True:
terr = [xterr * 1.0 / numex for xterr in terr]
return terr
def batchloop():
c = 0
numex = 0
prevperc = -1.
terr = [0.0]
numdigs = 2
tt = TT("iter progress", verbose=verbose)
tt.tick()
datafeeder.reset()
while datafeeder.hasnextbatch():
perc = round(c * 100. * (10**numdigs) /
datafeeder.getnumbats()) / (10**numdigs)
if perc > prevperc:
terr0 = terr[0] * 1.0 / numex if numex > 0 else 0.0
s = ("%." + str(numdigs) + "f%% \t error: %.3f") % (perc,
terr0)
tt.live(s)
prevperc = perc
sampleinps, batsize = datafeeder.nextbatch(withbatchsize=True)
numex += batsize
#embed()
sampleinps = sampletransf(*sampleinps, phase=phase)
try:
eterr = trainf(*sampleinps)
if len(terr) != len(eterr) and terr.count(0.0) == len(
terr):
terr = [0.0] * len(eterr)
except Exception, e:
raise e
if self.average_err is True:
terr = [
xterr + xeterr * batsize
for xterr, xeterr in zip(terr, eterr)
]
else:
terr = [
xterr + xeterr for xterr, xeterr in zip(terr, eterr)
]
c += 1
