def sgd_optimization_mnist(
learning_rate=0.1, pretraining_epochs=2, pretrain_lr=0.1, training_epochs=5, dataset="mnist.pkl.gz"
):
# Load the dataset
f = gzip.open(dataset, "rb")
# this gives us train, valid, test (each with .x, .y)
dataset = cPickle.load(f)
f.close()
n_ins = 28 * 28
n_outs = 10
hyperparameters = DD(
{
"finetuning_lr": learning_rate,
"pretraining_lr": pretrain_lr,
"pretraining_epochs_per_layer": pretraining_epochs,
"max_finetuning_epochs": training_epochs,
"hidden_layers_sizes": [100],
"corruption_levels": [0.2],
"minibatch_size": 20,
}
)
optimizer = SdaSgdOptimizer(dataset, hyperparameters, n_ins, n_outs)
optimizer.pretrain()
optimizer.finetune()
def jobman_entrypoint(state, channel):
# record mercurial versions of each package
pylearn.version.record_versions(state,[theano,ift6266,pylearn])
channel.save()
workingdir = os.getcwd()
print "Will load NIST"
nist = NIST(minibatch_size=20)
print "NIST loaded"
# 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
if rtt:
print "Reducing training set to "+str(rtt)+ " examples"
nist.reduce_train_set(rtt)
train,valid,test = nist.get_tvt()
dataset = (train,valid,test)
n_ins = 32*32
n_outs = 62 # 10 digits, 26*2 (lower, capitals)
# b,b',W for each hidden layer
# + b,W of last layer (logreg)
numparams = state.num_hidden_layers * 3 + 2
series_mux = None
series_mux = create_series(workingdir, numparams)
print "Creating optimizer with state, ", state
optimizer = SdaSgdOptimizer(dataset=dataset, hyperparameters=state, \
n_ins=n_ins, n_outs=n_outs,\
input_divider=255.0, series_mux=series_mux)
optimizer.pretrain()
channel.save()
optimizer.finetune()
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
if rtt:
examples_per_epoch = rtt
series = create_series(state.num_hidden_layers)
print "Creating optimizer with state, ", state
dataset = None
if rtt:
dataset = datasets.nist_all(maxsize=rtt)
else:
dataset = datasets.nist_all()
optimizer = SdaSgdOptimizer(dataset=dataset,
hyperparameters=state, \
n_ins=n_ins, n_outs=n_outs,\
examples_per_epoch=examples_per_epoch, \
series=series,
save_params=SAVE_PARAMS)
optimizer.pretrain(dataset)
channel.save()
optimizer.finetune(dataset)
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
if state.has_key('decrease_lr'):
decrease_lr = state['decrease_lr']
else :
decrease_lr = 0
if state.has_key('decrease_lr_pretrain'):
dec=state['decrease_lr_pretrain']
else :
dec=0
n_ins = 32*32
if state.has_key('subdataset'):
subdataset_name=state['subdataset']
else:
subdataset_name=SUBDATASET_NIST
#n_outs = 62 # 10 digits, 26*2 (lower, capitals)
if subdataset_name == "upper":
n_outs = 26
subdataset = datasets.nist_upper()
examples_per_epoch = NIST_UPPER_TRAIN_SIZE
elif subdataset_name == "lower":
n_outs = 26
subdataset = datasets.nist_lower()
examples_per_epoch = NIST_LOWER_TRAIN_SIZE
elif subdataset_name == "digits":
n_outs = 10
subdataset = datasets.nist_digits()
examples_per_epoch = NIST_DIGITS_TRAIN_SIZE
else:
n_outs = 62
subdataset = datasets.nist_all()
examples_per_epoch = NIST_ALL_TRAIN_SIZE
print 'Using subdataset ', subdataset_name
#To be sure variables will not be only in the if statement
PATH = ''
nom_reptrain = ''
nom_serie = ""
if state['pretrain_choice'] == 0:
nom_serie="series_NIST.h5"
elif state['pretrain_choice'] == 1:
nom_serie="series_P07.h5"
series = create_series(state.num_hidden_layers,nom_serie)
print "Creating optimizer with state, ", state
optimizer = SdaSgdOptimizer(dataset_name=subdataset_name,\
dataset=subdataset,\
hyperparameters=state, \
n_ins=n_ins, n_outs=n_outs,\
examples_per_epoch=examples_per_epoch, \
series=series,
max_minibatches=rtt)
parameters=[]
#Number of files of P07 used for pretraining
nb_file=0
print('\n\tpretraining with NIST\n')
optimizer.pretrain(subdataset, decrease = dec)
channel.save()
#Set some of the parameters used for the finetuning
if state.has_key('finetune_set'):
finetune_choice=state['finetune_set']
else:
finetune_choice=FINETUNE_SET
if state.has_key('max_finetuning_epochs'):
max_finetune_epoch_NIST=state['max_finetuning_epochs']
else:
max_finetune_epoch_NIST=MAX_FINETUNING_EPOCHS
if state.has_key('max_finetuning_epochs_P07'):
max_finetune_epoch_P07=state['max_finetuning_epochs_P07']
else:
max_finetune_epoch_P07=max_finetune_epoch_NIST
#Decide how the finetune is done
if finetune_choice == 0:
print('\n\n\tfinetune with NIST\n\n')
optimizer.reload_parameters('params_pretrain.txt')
optimizer.finetune(subdataset,subdataset,max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
channel.save()
if finetune_choice == 1:
print('\n\n\tfinetune with P07\n\n')
optimizer.reload_parameters('params_pretrain.txt')
optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
channel.save()
if finetune_choice == 2:
print('\n\n\tfinetune with P07 followed by NIST\n\n')
optimizer.reload_parameters('params_pretrain.txt')
optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20,decrease=decrease_lr)
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
channel.save()
if finetune_choice == 3:
print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\
All hidden units output are input of the logistic regression\n\n')
optimizer.reload_parameters('params_pretrain.txt')
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
if finetune_choice==-1:
print('\nSERIE OF 4 DIFFERENT FINETUNINGS')
print('\n\n\tfinetune with NIST\n\n')
sys.stdout.flush()
optimizer.reload_parameters('params_pretrain.txt')
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
channel.save()
print('\n\n\tfinetune with P07\n\n')
sys.stdout.flush()
optimizer.reload_parameters('params_pretrain.txt')
optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
channel.save()
print('\n\n\tfinetune with P07 (done earlier) followed by NIST (written here)\n\n')
sys.stdout.flush()
optimizer.reload_parameters('params_finetune_P07.txt')
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
channel.save()
print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\
All hidden units output are input of the logistic regression\n\n')
sys.stdout.flush()
optimizer.reload_parameters('params_pretrain.txt')
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
channel.save()
channel.save()
return channel.COMPLETE
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
if state.has_key('decrease_lr'):
decrease_lr = state['decrease_lr']
else :
decrease_lr = 0
n_ins = 32*32
n_outs = 62 # 10 digits, 26*2 (lower, capitals)
examples_per_epoch = NIST_ALL_TRAIN_SIZE
#To be sure variables will not be only in the if statement
PATH = ''
nom_reptrain = ''
nom_serie = ""
if state['pretrain_choice'] == 0:
PATH=PATH_NIST
nom_pretrain='NIST'
nom_serie="series_NIST.h5"
elif state['pretrain_choice'] == 1:
PATH=PATH_P07
nom_pretrain='P07'
nom_serie="series_P07.h5"
series = create_series(state.num_hidden_layers,nom_serie)
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, \
series=series,
max_minibatches=rtt)
parameters=[]
#Number of files of P07 used for pretraining
nb_file=0
## if state['pretrain_choice'] == 0:
## print('\n\tpretraining with NIST\n')
## optimizer.pretrain(datasets.nist_all())
## elif state['pretrain_choice'] == 1:
## #To know how many file will be used during pretraining
## nb_file = state['pretraining_epochs_per_layer']
## state['pretraining_epochs_per_layer'] = 1 #Only 1 time over the dataset
## if nb_file >=100:
## sys.exit("The code does not support this much pretraining epoch (99 max with P07).\n"+
## "You have to correct the code (and be patient, P07 is huge !!)\n"+
## "or reduce the number of pretraining epoch to run the code (better idea).\n")
## print('\n\tpretraining with P07')
## optimizer.pretrain(datasets.nist_P07(min_file=0,max_file=nb_file))
print ('Retrieve pre-train done earlier ( '+nom_pretrain+' )')
sys.stdout.flush()
channel.save()
#Set some of the parameters used for the finetuning
if state.has_key('finetune_set'):
finetune_choice=state['finetune_set']
else:
finetune_choice=FINETUNE_SET
if state.has_key('max_finetuning_epochs'):
max_finetune_epoch_NIST=state['max_finetuning_epochs']
else:
max_finetune_epoch_NIST=MAX_FINETUNING_EPOCHS
if state.has_key('max_finetuning_epochs_P07'):
max_finetune_epoch_P07=state['max_finetuning_epochs_P07']
else:
max_finetune_epoch_P07=max_finetune_epoch_NIST
#Decide how the finetune is done
if finetune_choice == 0:
print('\n\n\tfinetune with NIST\n\n')
optimizer.reload_parameters(PATH+'params_pretrain.txt')
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
channel.save()
if finetune_choice == 1:
print('\n\n\tfinetune with P07\n\n')
optimizer.reload_parameters(PATH+'params_pretrain.txt')
optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
channel.save()
if finetune_choice == 2:
print('\n\n\tfinetune with P07 followed by NIST\n\n')
optimizer.reload_parameters(PATH+'params_pretrain.txt')
optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=20,decrease=decrease_lr)
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
channel.save()
if finetune_choice == 3:
print('\n\n\tfinetune with NIST only on the logistic regression on top (but validation on P07).\n\
All hidden units output are input of the logistic regression\n\n')
optimizer.reload_parameters(PATH+'params_pretrain.txt')
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
if finetune_choice==-1:
print('\nSERIE OF 4 DIFFERENT FINETUNINGS')
print('\n\n\tfinetune with NIST\n\n')
sys.stdout.flush()
optimizer.reload_parameters(PATH+'params_pretrain.txt')
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,decrease=decrease_lr)
channel.save()
print('\n\n\tfinetune with P07\n\n')
sys.stdout.flush()
optimizer.reload_parameters(PATH+'params_pretrain.txt')
optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0,decrease=decrease_lr)
channel.save()
print('\n\n\tfinetune with P07 (done earlier) followed by NIST (written here)\n\n')
sys.stdout.flush()
optimizer.reload_parameters('params_finetune_P07.txt')
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=21,decrease=decrease_lr)
channel.save()
print('\n\n\tfinetune with NIST only on the logistic regression on top.\n\
All hidden units output are input of the logistic regression\n\n')
sys.stdout.flush()
optimizer.reload_parameters(PATH+'params_pretrain.txt')
optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr)
channel.save()
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 = PATH_P07
maximum_exemples = int(100) # Maximum number of exemples seen
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,
)
print "The model is created"
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 test set: "
optimizer.see_error(datasets.nist_all(maxsize=maximum_exemples))
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 P07 test set: "
optimizer.see_error(datasets.nist_P07(maxsize=maximum_exemples))
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 test set: "
optimizer.see_error(datasets.nist_all(maxsize=maximum_exemples))
print "For" + str(maximum_exemples) + "over the P07 test set: "
optimizer.see_error(datasets.nist_P07(maxsize=maximum_exemples))
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 P07 test set: "
optimizer.see_error(datasets.nist_P07(maxsize=maximum_exemples))
print "For" + str(maximum_exemples) + "over the NIST test set: "
optimizer.see_error(datasets.nist_all(maxsize=maximum_exemples))
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