def validate():
acc = []
for (i, (x, y)) in enumerate(examples.get_validation_example()):
if HYPERPARAMETERS["locally normalize"]:
targety = N.array([y])
else:
targety = N.zeros(ODIM)
targety[y] = 1.
if HLAYERS == 2:
o = graph.validatefn([x.data], targety, w1[x.indices], b1, wh, bh, w2, b2)
(kl, softmax, argmax, prehidden1, prehidden2) = o
else:
o = graph.validatefn([x.data], targety, w1[x.indices], b1, w2, b2)
(kl, softmax, argmax, prehidden) = o
if argmax == y: acc.append(1.)
else: acc.append(0.)
if i < 5:
if HLAYERS == 2:
abs_prehidden(prehidden1, "Prehidden1")
abs_prehidden(prehidden2, "Prehidden2")
else:
abs_prehidden(prehidden)
return N.mean(acc), N.std(acc)
def validate(cnt):
import math
logranks = []
logging.info("BEGINNING VALIDATION AT TRAINING STEP %d" % cnt)
logging.info(stats())
i = 0
for (i, ve) in enumerate(examples.get_validation_example()):
# logging.info([wordmap.str(id) for id in ve])
logranks.append(math.log(m.validate(ve)))
if (i+1) % 10 == 0:
logging.info("Training step %d, validating example %d, mean(logrank) = %.2f, stddev(logrank) = %.2f" % (cnt, i+1, numpy.mean(numpy.array(logranks)), numpy.std(numpy.array(logranks))))
logging.info(stats())
logging.info("FINAL VALIDATION AT TRAINING STEP %d: mean(logrank) = %.2f, stddev(logrank) = %.2f, cnt = %d" % (cnt, numpy.mean(numpy.array(logranks)), numpy.std(numpy.array(logranks)), i+1))
logging.info(stats())
def validate(cnt):
import math
logranks = []
logging.info("BEGINNING VALIDATION AT TRAINING STEP %d" % cnt)
logging.info(stats())
i = 0
for (i, ve) in enumerate(examples.get_validation_example()):
# logging.info([wordmap.str(id) for id in ve])
logranks.append(math.log(m.validate(ve)))
if (i+1) % 10 == 0:
logging.info("Training step %d, validating example %d, mean(logrank) = %.2f, stddev(logrank) = %.2f" % (cnt, i+1, numpy.mean(numpy.array(logranks)), numpy.std(numpy.array(logranks))))
logging.info(stats())
logging.info("FINAL VALIDATION AT TRAINING STEP %d: mean(logrank) = %.2f, stddev(logrank) = %.2f, cnt = %d" % (cnt, numpy.mean(numpy.array(logranks)), numpy.std(numpy.array(logranks)), i+1))
logging.info(stats())
def debug_prehidden_values(cnt, model):
"""
Give debug output on pre-squash hidden values.
"""
for (i, ve) in enumerate(examples.get_validation_example()):
(score, prehidden) = model.verbose_predict(ve)
abs_prehidden = numpy.abs(prehidden)
med = numpy.median(abs_prehidden)
abs_prehidden = abs_prehidden.tolist()
assert len(abs_prehidden) == 1
abs_prehidden = abs_prehidden[0]
abs_prehidden.sort()
abs_prehidden.reverse()
logging.info("%s %s %s %s %s" % (cnt, "abs(pre-squash hidden) median =", med, "max =", abs_prehidden[:3]))
if i+1 >= 3: break
def debug_prehidden_values(cnt, model):
"""
Give debug output on pre-squash hidden values.
"""
for (i, ve) in enumerate(examples.get_validation_example()):
(score, prehidden) = model.verbose_predict(ve)
abs_prehidden = numpy.abs(prehidden)
med = numpy.median(abs_prehidden)
abs_prehidden = abs_prehidden.tolist()
assert len(abs_prehidden) == 1
abs_prehidden = abs_prehidden[0]
abs_prehidden.sort()
abs_prehidden.reverse()
logging.info("%s %s %s %s %s" %
(cnt, "abs(pre-squash hidden) median =", med, "max =",
abs_prehidden[:3]))
if i + 1 >= 3: break
def validate():
acc = []
for (i, (x, y)) in enumerate(examples.get_validation_example()):
if HLAYERS == 2:
o = graph.validatefn(x, N.array([y]), w1, b1, wh, bh, w2, b2)
(kl, softmax, argmax, prehidden1, prehidden2) = o
else:
o = graph.validatefn(x, N.array([y]), w1, b1, w2, b2)
(kl, softmax, argmax, prehidden) = o
if argmax == y: acc.append(1.)
else: acc.append(0.)
if i < 5:
if HLAYERS == 2:
abs_prehidden(prehidden1, "Prehidden1")
abs_prehidden(prehidden2, "Prehidden2")
else:
abs_prehidden(prehidden)
return N.mean(acc), N.std(acc)
def validate():
acc = []
for (i, (x, y)) in enumerate(examples.get_validation_example()):
if HLAYERS == 2:
o = graph.validatefn(x, N.array([y]), w1, b1, wh, bh, w2, b2)
(kl, softmax, argmax, prehidden1, prehidden2) = o
else:
o = graph.validatefn(x, N.array([y]), w1, b1, w2, b2)
(kl, softmax, argmax, prehidden) = o
if argmax == y: acc.append(1.)
else: acc.append(0.)
if i < 5:
if HLAYERS == 2:
abs_prehidden(prehidden1, "Prehidden1")
abs_prehidden(prehidden2, "Prehidden2")
else:
abs_prehidden(prehidden)
return N.mean(acc), N.std(acc)