def log_queue(log_to=None):
if log_to:
# standard logfile
jlog = munk.file_sink(log_to+".log")
jlog = munk.jsonify(jlog)
jlog = munk.timify(jlog, tag="timestamp")
jlog = munk.exclude(jlog, "params")
# parameter logfile
paraml = munk.file_sink(log_to+".params")
paraml = munk.jsonify(paraml)
paraml = munk.timify(paraml, tag="timestamp")
paraml = munk.include(paraml, "params")
jplog = munk.broadcast(*[jlog, paraml])
# finally a pretty printer for some immediate feedback
pp = munk.timify(munk.prettyprint_sink())
pp = munk.dontkeep(pp, "tags")
pp = munk.include_tags_only(pp, "pretty")
jplog = munk.exclude_tags(jplog, "pretty")
log = munk.broadcast(*[jplog, pp])
else:
pp = munk.timify(munk.prettyprint_sink())
pp = munk.dontkeep(pp, "tags")
log = munk.include_tags_only(pp, "pretty")
return log
def log_queue(log_to=None):
if log_to:
# standard logfile
jlog = munk.file_sink(log_to + ".log")
jlog = munk.jsonify(jlog)
jlog = munk.timify(jlog, tag="timestamp")
jlog = munk.exclude(jlog, "params")
# parameter logfile
paraml = munk.file_sink(log_to + ".params")
paraml = munk.jsonify(paraml)
paraml = munk.timify(paraml, tag="timestamp")
paraml = munk.include(paraml, "params")
jplog = munk.broadcast(*[jlog, paraml])
# finally a pretty printer for some immediate feedback
pp = munk.timify(munk.prettyprint_sink())
pp = munk.dontkeep(pp, "tags")
pp = munk.include_tags_only(pp, "pretty")
jplog = munk.exclude_tags(jplog, "pretty")
log = munk.broadcast(*[jplog, pp])
else:
pp = munk.timify(munk.prettyprint_sink())
pp = munk.dontkeep(pp, "tags")
log = munk.include_tags_only(pp, "pretty")
return log
def test_jsonify():
lst = []
pipe = L.list_sink(lst)
pipe = L.jsonify(pipe)
info = {'2': [3], '4': 5.}
pipe.send(info)
received_info = json.loads(lst[0])
assert received_info == info, 'jsonify did mutate info: %s' % received_info
## import numericalGradientChecker
## checker = numericalGradientChecker.numericalGradientChecker(fraw, fprime, inputDim=P.data.size, outputDim=batchsize, args=args, bounds=None)
## for i, info in enumerate(checker):
## print "errors in gradient", info['errors']
## if i>0:
## break
import chopmunk
ignore = ['args', 'kwargs', 'gradient', 'Hp']
console_sink = chopmunk.prettyprint_sink()
console_sink = chopmunk.dontkeep(console_sink, ignore)
file_sink = chopmunk.file_sink('mnist.log')
file_sink = chopmunk.jsonify(file_sink)
file_sink = chopmunk.dontkeep(file_sink, ignore)
logger = chopmunk.broadcast(console_sink, file_sink)
logfunc = logger.send
#size of blocks used for the diagonal approximation
blocksizes = np.ones(n_hidden + n_output)
blocksizes[:n_hidden] *= (n_inpt+1)
blocksizes[n_hidden:] *= (n_hidden+1)
opt = tonga(P.data, fprime, damping=1e-2, blocksizes=blocksizes, nb_estimates=50, args=args, cov_args=cov_args, logfunc=logfunc)
fileName = 'testTonga2'