def jobman_entrypoint(state, channel,set_choice):
# record mercurial versions of each package
pylearn.version.record_versions(state,[theano,ift6266,pylearn])
# TODO: remove this, bad for number of simultaneous requests on DB
channel.save()
# For test runs, we don't want to use the whole dataset so
# reduce it to fewer elements if asked to.
rtt = None
if state.has_key('reduce_train_to'):
rtt = state['reduce_train_to']
elif REDUCE_TRAIN_TO:
rtt = REDUCE_TRAIN_TO
n_ins = 32*32
n_outs = 62 # 10 digits, 26*2 (lower, capitals)
examples_per_epoch = NIST_ALL_TRAIN_SIZE
PATH = ''
if set_choice == 0:
maximum_exemples=int(500000) #Maximum number of exemples seen
else:
maximum_exemples = int(1000000000) #an impossible number
print "Creating optimizer with state, ", state
optimizer = SdaSgdOptimizer(dataset=datasets.nist_all(),
hyperparameters=state, \
n_ins=n_ins, n_outs=n_outs,\
examples_per_epoch=examples_per_epoch, \
max_minibatches=rtt)
if os.path.exists(PATH+'params_finetune_NIST.txt'):
print ('\n finetune = NIST ')
optimizer.reload_parameters(PATH+'params_finetune_NIST.txt')
print "For" + str(maximum_exemples) + "over the NIST set: "
optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the P07 set: "
optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the PNIST07 set: "
optimizer.training_error(datasets.PNIST07(maxsize=maximum_exemples),set_choice)
if os.path.exists(PATH+'params_finetune_P07.txt'):
print ('\n finetune = P07 ')
optimizer.reload_parameters(PATH+'params_finetune_P07.txt')
print "For" + str(maximum_exemples) + "over the NIST set: "
optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the P07 set: "
optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the PNIST07 set: "
optimizer.training_error(datasets.PNIST07(maxsize=maximum_exemples),set_choice)
if os.path.exists(PATH+'params_finetune_NIST_then_P07.txt'):
print ('\n finetune = NIST then P07')
optimizer.reload_parameters(PATH+'params_finetune_NIST_then_P07.txt')
print "For" + str(maximum_exemples) + "over the NIST set: "
optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the P07 set: "
optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the PNIST07 set: "
optimizer.training_error(datasets.PNIST07(maxsize=maximum_exemples),set_choice)
if os.path.exists(PATH+'params_finetune_P07_then_NIST.txt'):
print ('\n finetune = P07 then NIST')
optimizer.reload_parameters(PATH+'params_finetune_P07_then_NIST.txt')
print "For" + str(maximum_exemples) + "over the NIST set: "
optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the P07 set: "
optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the PNIST07 set: "
optimizer.training_error(datasets.PNIST07(maxsize=maximum_exemples),set_choice)
if os.path.exists(PATH+'params_finetune_PNIST07.txt'):
print ('\n finetune = PNIST07')
optimizer.reload_parameters(PATH+'params_finetune_PNIST07.txt')
print "For" + str(maximum_exemples) + "over the NIST set: "
optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the P07 set: "
optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the PNIST07 set: "
optimizer.training_error(datasets.PNIST07(maxsize=maximum_exemples),set_choice)
if os.path.exists(PATH+'params_finetune_PNIST07_then_NIST.txt'):
print ('\n finetune = PNIST07 then NIST')
optimizer.reload_parameters(PATH+'params_finetune_PNIST07_then_NIST.txt')
print "For" + str(maximum_exemples) + "over the NIST set: "
optimizer.training_error(datasets.nist_all(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the P07 set: "
optimizer.training_error(datasets.nist_P07(maxsize=maximum_exemples),set_choice)
print "For" + str(maximum_exemples) + "over the PNIST07 set: "
optimizer.training_error(datasets.PNIST07(maxsize=maximum_exemples),set_choice)
channel.save()
return channel.COMPLETE
def jobman_entrypoint(state, channel):
# record mercurial versions of each package
pylearn.version.record_versions(state,[theano,ift6266,pylearn])
# TODO: remove this, bad for number of simultaneous requests on DB
channel.save()
# For test runs, we don't want to use the whole dataset so
# reduce it to fewer elements if asked to.
rtt = None
if state.has_key('reduce_train_to'):
rtt = state['reduce_train_to']
elif REDUCE_TRAIN_TO:
rtt = REDUCE_TRAIN_TO
n_ins = 32*32
n_outs = 62 # 10 digits, 26*2 (lower, capitals)
examples_per_epoch = NIST_ALL_TRAIN_SIZE
PATH = ''
NIST_BY_CLASS=0
print "Creating optimizer with state, ", state
optimizer = SdaSgdOptimizer(dataset=datasets.nist_all(),
hyperparameters=state, \
n_ins=n_ins, n_outs=n_outs,\
examples_per_epoch=examples_per_epoch, \
max_minibatches=rtt)
if os.path.exists(PATH+'params_finetune_NIST.txt'):
print ('\n finetune = NIST ')
optimizer.reload_parameters(PATH+'params_finetune_NIST.txt')
if NIST_BY_CLASS == 1:
print "NIST DIGITS"
optimizer.training_error(datasets.nist_digits(),part=2)
print "NIST LOWER CASE"
optimizer.training_error(datasets.nist_lower(),part=2)
print "NIST UPPER CASE"
optimizer.training_error(datasets.nist_upper(),part=2)
else:
print "P07 valid"
optimizer.training_error(datasets.nist_P07(),part=1)
print "PNIST valid"
optimizer.training_error(datasets.PNIST07(),part=1)
if os.path.exists(PATH+'params_finetune_P07.txt'):
print ('\n finetune = P07 ')
optimizer.reload_parameters(PATH+'params_finetune_P07.txt')
if NIST_BY_CLASS == 1:
print "NIST DIGITS"
optimizer.training_error(datasets.nist_digits(),part=2)
print "NIST LOWER CASE"
optimizer.training_error(datasets.nist_lower(),part=2)
print "NIST UPPER CASE"
optimizer.training_error(datasets.nist_upper(),part=2)
else:
print "P07 valid"
optimizer.training_error(datasets.nist_P07(),part=1)
print "PNIST valid"
optimizer.training_error(datasets.PNIST07(),part=1)
if os.path.exists(PATH+'params_finetune_NIST_then_P07.txt'):
print ('\n finetune = NIST then P07')
optimizer.reload_parameters(PATH+'params_finetune_NIST_then_P07.txt')
if NIST_BY_CLASS == 1:
print "NIST DIGITS"
optimizer.training_error(datasets.nist_digits(),part=2)
print "NIST LOWER CASE"
optimizer.training_error(datasets.nist_lower(),part=2)
print "NIST UPPER CASE"
optimizer.training_error(datasets.nist_upper(),part=2)
else:
print "P07 valid"
optimizer.training_error(datasets.nist_P07(),part=1)
print "PNIST valid"
optimizer.training_error(datasets.PNIST07(),part=1)
if os.path.exists(PATH+'params_finetune_P07_then_NIST.txt'):
print ('\n finetune = P07 then NIST')
optimizer.reload_parameters(PATH+'params_finetune_P07_then_NIST.txt')
if NIST_BY_CLASS == 1:
print "NIST DIGITS"
optimizer.training_error(datasets.nist_digits(),part=2)
print "NIST LOWER CASE"
optimizer.training_error(datasets.nist_lower(),part=2)
print "NIST UPPER CASE"
optimizer.training_error(datasets.nist_upper(),part=2)
else:
print "P07 valid"
optimizer.training_error(datasets.nist_P07(),part=1)
print "PNIST valid"
optimizer.training_error(datasets.PNIST07(),part=1)
if os.path.exists(PATH+'params_finetune_PNIST07.txt'):
print ('\n finetune = PNIST07')
optimizer.reload_parameters(PATH+'params_finetune_PNIST07.txt')
if NIST_BY_CLASS == 1:
print "NIST DIGITS"
optimizer.training_error(datasets.nist_digits(),part=2)
print "NIST LOWER CASE"
optimizer.training_error(datasets.nist_lower(),part=2)
print "NIST UPPER CASE"
optimizer.training_error(datasets.nist_upper(),part=2)
else:
print "P07 valid"
optimizer.training_error(datasets.nist_P07(),part=1)
print "PNIST valid"
optimizer.training_error(datasets.PNIST07(),part=1)
if os.path.exists(PATH+'params_finetune_PNIST07_then_NIST.txt'):
print ('\n finetune = PNIST07 then NIST')
optimizer.reload_parameters(PATH+'params_finetune_PNIST07_then_NIST.txt')
if NIST_BY_CLASS == 1:
print "NIST DIGITS"
optimizer.training_error(datasets.nist_digits(),part=2)
print "NIST LOWER CASE"
optimizer.training_error(datasets.nist_lower(),part=2)
print "NIST UPPER CASE"
optimizer.training_error(datasets.nist_upper(),part=2)
else:
print "P07 valid"
optimizer.training_error(datasets.nist_P07(),part=1)
print "PNIST valid"
optimizer.training_error(datasets.PNIST07(),part=1)
channel.save()
return channel.COMPLETE
