def get_cost_type(model, x, t, args):
forward_func = None
if args["dataset"] == "mnist":
forward_func = model.forward_no_update_batch_stat
if args['--cost_type'] == 'MLE' or args['--cost_type'] == 'dropout':
cost = costs.cross_entropy_loss(x=x, t=t, forward_func=model.forward_train)
elif args['--cost_type'] == 'L2':
cost = costs.cross_entropy_loss(x=x, t=t, forward_func=model.forward_train) \
+ costs.weight_decay(params=model.params, coeff=float(args['--lamb']))
elif args['--cost_type'] == 'AT':
cost = costs.adversarial_training(x, t, model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
lamb=float(args['--lamb']),
norm_constraint=args['--norm_constraint'],
forward_func_for_generating_adversarial_examples=forward_func)
elif args['--cost_type'] == 'VAT':
cost = costs.virtual_adversarial_training(x, t, model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']),
forward_func_for_generating_adversarial_examples=forward_func)
elif args['--cost_type'] == 'VAT_n':
cost = costs.virtual_adversarial_training_noise(x, t, model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
eps_n=float(args['--eps_w']),
neg_lamb=float(args['--neg_lamb']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']),
forward_func_for_generating_adversarial_examples=forward_func)
elif args['--cost_type'] == 'VAT_f':
cost = costs.virtual_adversarial_training_finite_diff(x, t, model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']),
forward_func_for_generating_adversarial_examples=forward_func)
else:
print("method not exists")
sys.exit(-1)
return cost
def get_cost_type_semi(model, x, t, ul_x, args, fix_d=None):
if args['--cost_type'] == 'MLE':
cost = costs.cross_entropy_loss(x=x, t=t, forward_func=model.forward_train)
elif args['--cost_type'] == 'L2':
cost = costs.cross_entropy_loss(x=x, t=t, forward_func=model.forward_train) \
+ costs.weight_decay(params=model.params, coeff=float(args['--lamb']))
elif args['--cost_type'] == 'AT':
cost = costs.adversarial_training(x, t, model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
lamb=float(args['--lamb']),
norm_constraint=args['--norm_constraint'],
forward_func_for_generating_adversarial_examples=model.forward_no_update_batch_stat)
elif args['--cost_type'] == 'VATsup':
cost = costs.virtual_adversarial_training_finite_diff(x, t, model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']),
x_for_generating_adversarial_examples=x,
forward_func_for_generating_adversarial_examples=model.forward_no_update_batch_stat)
elif args['--cost_type'] == 'VAT':
cost = costs.virtual_adversarial_training(x, t, model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']),
x_for_generating_adversarial_examples=ul_x,
forward_func_for_generating_adversarial_examples=model.forward_no_update_batch_stat)
elif args['--cost_type'] == 'VAT_f':
cost = costs.virtual_adversarial_training_finite_diff(x, t, model.forward_train,
'CE',
xi=float(args['--xi']),
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']),
x_for_generating_adversarial_examples=ul_x,
forward_func_for_generating_adversarial_examples=model.forward_no_update_batch_stat)
else:
print("method not exists")
sys.exit(-1)
return cost
def train(args):
print args
numpy.random.seed(int(args['--seed']))
if (args['--validation']):
dataset = load_data.load_mnist_for_validation(
n_v=int(args['--num_validation_samples']))
else:
dataset = load_data.load_mnist_full()
x_train, t_train = dataset[0]
x_test, t_test = dataset[1]
layer_sizes = [
int(layer_size) for layer_size in args['--layer_sizes'].split('-')
]
model = FNN_MNIST(layer_sizes=layer_sizes)
x = T.matrix()
t = T.ivector()
if (args['--cost_type'] == 'MLE'):
cost = costs.cross_entropy_loss(x=x,
t=t,
forward_func=model.forward_train)
elif (args['--cost_type'] == 'L2'):
cost = costs.cross_entropy_loss(x=x, t=t, forward_func=model.forward_train) \
+ costs.weight_decay(params=model.params, coeff=float(args['--lamb']))
elif (args['--cost_type'] == 'AT'):
cost = costs.adversarial_training(
x,
t,
model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
lamb=float(args['--lamb']),
norm_constraint=args['--norm_constraint'],
forward_func_for_generating_adversarial_examples=model.
forward_no_update_batch_stat)
elif (args['--cost_type'] == 'VAT'):
cost = costs.virtual_adversarial_training(
x,
t,
model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']),
forward_func_for_generating_adversarial_examples=model.
forward_no_update_batch_stat)
elif (args['--cost_type'] == 'VAT_finite_diff'):
cost = costs.virtual_adversarial_training_finite_diff(
x,
t,
model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']),
forward_func_for_generating_adversarial_examples=model.
forward_no_update_batch_stat)
nll = costs.cross_entropy_loss(x=x, t=t, forward_func=model.forward_test)
error = costs.error(x=x, t=t, forward_func=model.forward_test)
optimizer = optimizers.ADAM(cost=cost,
params=model.params,
alpha=float(args['--initial_learning_rate']))
index = T.iscalar()
batch_size = int(args['--batch_size'])
f_train = theano.function(
inputs=[index],
outputs=cost,
updates=optimizer.updates,
givens={
x: x_train[batch_size * index:batch_size * (index + 1)],
t: t_train[batch_size * index:batch_size * (index + 1)]
})
f_nll_train = theano.function(
inputs=[index],
outputs=nll,
givens={
x: x_train[batch_size * index:batch_size * (index + 1)],
t: t_train[batch_size * index:batch_size * (index + 1)]
})
f_nll_test = theano.function(
inputs=[index],
outputs=nll,
givens={
x: x_test[batch_size * index:batch_size * (index + 1)],
t: t_test[batch_size * index:batch_size * (index + 1)]
})
f_error_train = theano.function(
inputs=[index],
outputs=error,
givens={
x: x_train[batch_size * index:batch_size * (index + 1)],
t: t_train[batch_size * index:batch_size * (index + 1)]
})
f_error_test = theano.function(
inputs=[index],
outputs=error,
givens={
x: x_test[batch_size * index:batch_size * (index + 1)],
t: t_test[batch_size * index:batch_size * (index + 1)]
})
f_lr_decay = theano.function(
inputs=[],
outputs=optimizer.alpha,
updates={
optimizer.alpha:
theano.shared(
numpy.array(args['--learning_rate_decay']).astype(
theano.config.floatX)) * optimizer.alpha
})
randix = RandomStreams(seed=numpy.random.randint(1234)).permutation(
n=x_train.shape[0])
update_permutation = OrderedDict()
update_permutation[x_train] = x_train[randix]
update_permutation[t_train] = t_train[randix]
f_permute_train_set = theano.function(inputs=[],
outputs=x_train,
updates=update_permutation)
statuses = {}
statuses['nll_train'] = []
statuses['error_train'] = []
statuses['nll_test'] = []
statuses['error_test'] = []
n_train = x_train.get_value().shape[0]
n_test = x_test.get_value().shape[0]
sum_nll_train = numpy.sum(
numpy.array([f_nll_train(i)
for i in xrange(n_train / batch_size)])) * batch_size
sum_error_train = numpy.sum(
numpy.array([f_error_train(i) for i in xrange(n_train / batch_size)]))
sum_nll_test = numpy.sum(
numpy.array([f_nll_test(i)
for i in xrange(n_test / batch_size)])) * batch_size
sum_error_test = numpy.sum(
numpy.array([f_error_test(i) for i in xrange(n_test / batch_size)]))
statuses['nll_train'].append(sum_nll_train / n_train)
statuses['error_train'].append(sum_error_train)
statuses['nll_test'].append(sum_nll_test / n_test)
statuses['error_test'].append(sum_error_test)
print "[Epoch]", str(-1)
print "nll_train : ", statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : ", statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
print "training..."
make_sure_path_exists("./trained_model")
for epoch in xrange(int(args['--num_epochs'])):
cPickle.dump(
(statuses, args),
open('./trained_model/' + 'tmp-' + args['--save_filename'],
'wb'), cPickle.HIGHEST_PROTOCOL)
f_permute_train_set()
### update parameters ###
[f_train(i) for i in xrange(n_train / batch_size)]
#########################
sum_nll_train = numpy.sum(
numpy.array([f_nll_train(i)
for i in xrange(n_train / batch_size)])) * batch_size
sum_error_train = numpy.sum(
numpy.array(
[f_error_train(i) for i in xrange(n_train / batch_size)]))
sum_nll_test = numpy.sum(
numpy.array([f_nll_test(i)
for i in xrange(n_test / batch_size)])) * batch_size
sum_error_test = numpy.sum(
numpy.array([f_error_test(i)
for i in xrange(n_test / batch_size)]))
statuses['nll_train'].append(sum_nll_train / n_train)
statuses['error_train'].append(sum_error_train)
statuses['nll_test'].append(sum_nll_test / n_test)
statuses['error_test'].append(sum_error_test)
print "[Epoch]", str(epoch)
print "nll_train : ", statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : ", statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
f_lr_decay()
### finetune batch stat ###
f_finetune = theano.function(
inputs=[index],
outputs=model.forward_for_finetuning_batch_stat(x),
givens={x: x_train[batch_size * index:batch_size * (index + 1)]})
[f_finetune(i) for i in xrange(n_train / batch_size)]
sum_nll_train = numpy.sum(
numpy.array([f_nll_train(i)
for i in xrange(n_train / batch_size)])) * batch_size
sum_error_train = numpy.sum(
numpy.array([f_error_train(i) for i in xrange(n_train / batch_size)]))
sum_nll_test = numpy.sum(
numpy.array([f_nll_test(i)
for i in xrange(n_test / batch_size)])) * batch_size
sum_error_test = numpy.sum(
numpy.array([f_error_test(i) for i in xrange(n_test / batch_size)]))
statuses['nll_train'].append(sum_nll_train / n_train)
statuses['error_train'].append(sum_error_train)
statuses['nll_test'].append(sum_nll_test / n_test)
statuses['error_test'].append(sum_error_test)
print "[after finetuning]"
print "nll_train : ", statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : ", statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
###########################
make_sure_path_exists("./trained_model")
cPickle.dump((model, statuses, args),
open('./trained_model/' + args['--save_filename'], 'wb'),
cPickle.HIGHEST_PROTOCOL)
def train(args):
print args
numpy.random.seed(int(args['--seed']))
dataset = load_data.load_mnist_for_semi_sup(n_l=int(args['--num_labeled_samples']),
n_v=int(args['--num_validation_samples']))
x_train, t_train, ul_x_train = dataset[0]
x_test, t_test = dataset[1]
layer_sizes = [int(layer_size) for layer_size in args['--layer_sizes'].split('-')]
model = FNN_MNIST(layer_sizes=layer_sizes)
x = T.matrix()
ul_x = T.matrix()
t = T.ivector()
if(args['--cost_type']=='MLE'):
cost = costs.cross_entropy_loss(x=x,t=t,forward_func=model.forward_train)
elif(args['--cost_type']=='L2'):
cost = costs.cross_entropy_loss(x=x,t=t,forward_func=model.forward_train) \
+ costs.weight_decay(params=model.params,coeff=float(args['--lamb']))
elif(args['--cost_type']=='AT'):
cost = costs.adversarial_training(x,t,model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
lamb=float(args['--lamb']),
norm_constraint = args['--norm_constraint'],
forward_func_for_generating_adversarial_examples=model.forward_no_update_batch_stat)
elif(args['--cost_type']=='VAT'):
cost = costs.virtual_adversarial_training(x,t,model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint = args['--norm_constraint'],
num_power_iter = int(args['--num_power_iter']),
x_for_generating_adversarial_examples = ul_x,
forward_func_for_generating_adversarial_examples=model.forward_no_update_batch_stat)
elif(args['--cost_type']=='VAT_finite_diff'):
cost = costs.virtual_adversarial_training_finite_diff(x,t,model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint = args['--norm_constraint'],
num_power_iter = int(args['--num_power_iter']),
x_for_generating_adversarial_examples = ul_x,
forward_func_for_generating_adversarial_examples=model.forward_no_update_batch_stat)
nll = costs.cross_entropy_loss(x=x,t=t,forward_func=model.forward_test)
error = costs.error(x=x,t=t,forward_func=model.forward_test)
optimizer = optimizers.ADAM(cost=cost,params=model.params,alpha=float(args['--initial_learning_rate']))
index = T.iscalar()
ul_index = T.iscalar()
batch_size = int(args['--batch_size'])
ul_batch_size = int(args['--ul_batch_size'])
f_train = theano.function(inputs=[index,ul_index], outputs=cost, updates=optimizer.updates,
givens={
x:x_train[batch_size*index:batch_size*(index+1)],
t:t_train[batch_size*index:batch_size*(index+1)],
ul_x:ul_x_train[ul_batch_size*ul_index:ul_batch_size*(ul_index+1)]},
on_unused_input='warn')
f_nll_train = theano.function(inputs=[index], outputs=nll,
givens={
x:x_train[batch_size*index:batch_size*(index+1)],
t:t_train[batch_size*index:batch_size*(index+1)]})
f_nll_test = theano.function(inputs=[index], outputs=nll,
givens={
x:x_test[batch_size*index:batch_size*(index+1)],
t:t_test[batch_size*index:batch_size*(index+1)]})
f_error_train = theano.function(inputs=[index], outputs=error,
givens={
x:x_train[batch_size*index:batch_size*(index+1)],
t:t_train[batch_size*index:batch_size*(index+1)]})
f_error_test = theano.function(inputs=[index], outputs=error,
givens={
x:x_test[batch_size*index:batch_size*(index+1)],
t:t_test[batch_size*index:batch_size*(index+1)]})
f_lr_decay = theano.function(inputs=[],outputs=optimizer.alpha,
updates={optimizer.alpha:theano.shared(numpy.array(args['--learning_rate_decay']).astype(theano.config.floatX))*optimizer.alpha})
# Shuffle training set
randix = RandomStreams(seed=numpy.random.randint(1234)).permutation(n=x_train.shape[0])
update_permutation = OrderedDict()
update_permutation[x_train] = x_train[randix]
update_permutation[t_train] = t_train[randix]
f_permute_train_set = theano.function(inputs=[],outputs=x_train,updates=update_permutation)
# Shuffle unlabeled training set
ul_randix = RandomStreams(seed=numpy.random.randint(1234)).permutation(n=ul_x_train.shape[0])
update_ul_permutation = OrderedDict()
update_ul_permutation[ul_x_train] = ul_x_train[ul_randix]
f_permute_ul_train_set = theano.function(inputs=[],outputs=ul_x_train,updates=update_ul_permutation)
statuses = {}
statuses['nll_train'] = []
statuses['error_train'] = []
statuses['nll_test'] = []
statuses['error_test'] = []
n_train = x_train.get_value().shape[0]
n_test = x_test.get_value().shape[0]
n_ul_train = ul_x_train.get_value().shape[0]
sum_nll_train = numpy.sum(numpy.array([f_nll_train(i) for i in xrange(n_train/batch_size)]))*batch_size
sum_error_train = numpy.sum(numpy.array([f_error_train(i) for i in xrange(n_train/batch_size)]))
sum_nll_test = numpy.sum(numpy.array([f_nll_test(i) for i in xrange(n_test/batch_size)]))*batch_size
sum_error_test = numpy.sum(numpy.array([f_error_test(i) for i in xrange(n_test/batch_size)]))
statuses['nll_train'].append(sum_nll_train/n_train)
statuses['error_train'].append(sum_error_train)
statuses['nll_test'].append(sum_nll_test/n_test)
statuses['error_test'].append(sum_error_test)
print "[Epoch]",str(-1)
print "nll_train : " , statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : " , statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
print "training..."
make_sure_path_exists("./trained_model")
l_i = 0
ul_i = 0
for epoch in xrange(int(args['--num_epochs'])):
cPickle.dump((statuses,args),open('./trained_model/'+'tmp-' + args['--save_filename'],'wb'),cPickle.HIGHEST_PROTOCOL)
f_permute_train_set()
f_permute_ul_train_set()
for it in xrange(int(args['--num_batch_it'])):
f_train(l_i,ul_i)
l_i = 0 if l_i>=n_train/batch_size-1 else l_i + 1
ul_i = 0 if ul_i >=n_ul_train/ul_batch_size-1 else ul_i + 1
sum_nll_train = numpy.sum(numpy.array([f_nll_train(i) for i in xrange(n_train/batch_size)]))*batch_size
sum_error_train = numpy.sum(numpy.array([f_error_train(i) for i in xrange(n_train/batch_size)]))
sum_nll_test = numpy.sum(numpy.array([f_nll_test(i) for i in xrange(n_test/batch_size)]))*batch_size
sum_error_test = numpy.sum(numpy.array([f_error_test(i) for i in xrange(n_test/batch_size)]))
statuses['nll_train'].append(sum_nll_train/n_train)
statuses['error_train'].append(sum_error_train)
statuses['nll_test'].append(sum_nll_test/n_test)
statuses['error_test'].append(sum_error_test)
print "[Epoch]",str(epoch)
print "nll_train : " , statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : " , statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
f_lr_decay()
### finetune batch stat ###
f_finetune = theano.function(inputs=[ul_index],outputs=model.forward_for_finetuning_batch_stat(x),
givens={x:ul_x_train[ul_batch_size*ul_index:ul_batch_size*(ul_index+1)]})
[f_finetune(i) for i in xrange(n_ul_train/ul_batch_size)]
sum_nll_train = numpy.sum(numpy.array([f_nll_train(i) for i in xrange(n_train/batch_size)]))*batch_size
sum_error_train = numpy.sum(numpy.array([f_error_train(i) for i in xrange(n_train/batch_size)]))
sum_nll_test = numpy.sum(numpy.array([f_nll_test(i) for i in xrange(n_test/batch_size)]))*batch_size
sum_error_test = numpy.sum(numpy.array([f_error_test(i) for i in xrange(n_test/batch_size)]))
statuses['nll_train'].append(sum_nll_train/n_train)
statuses['error_train'].append(sum_error_train)
statuses['nll_test'].append(sum_nll_test/n_test)
statuses['error_test'].append(sum_error_test)
print "[after finetuning]"
print "nll_train : " , statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : " , statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
###########################
make_sure_path_exists("./trained_model")
cPickle.dump((model,statuses,args),open('./trained_model/'+args['--save_filename'],'wb'),cPickle.HIGHEST_PROTOCOL)
def train(args):
print args
numpy.random.seed(1)
dataset = cPickle.load(open('dataset/' + args['--dataset_filename']))
x_train = theano.shared(numpy.asarray(dataset[0][0][0],dtype=theano.config.floatX))
t_train = theano.shared(numpy.asarray(dataset[0][0][1],dtype='int32'))
x_test = theano.shared(numpy.asarray(dataset[0][1][0],dtype=theano.config.floatX))
t_test = theano.shared(numpy.asarray(dataset[0][1][1],dtype='int32'))
if(args['--cost_type']=='dropout'):
model = FNN_syn_dropout(drate=float(args['--dropout_rate']))
else:
model = FNN_syn()
x = T.matrix()
t = T.ivector()
if(args['--cost_type']=='MLE' or args['--cost_type']=='dropout'):
cost = costs.cross_entropy_loss(x=x,t=t,forward_func=model.forward_train)
elif(args['--cost_type']=='L2'):
cost = costs.cross_entropy_loss(x=x,t=t,forward_func=model.forward_train) \
+ costs.weight_decay(params=model.params,coeff=float(args['--lamb']))
elif(args['--cost_type']=='AT'):
cost = costs.adversarial_training(x,t,model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
lamb=float(args['--lamb']),
norm_constraint = args['--norm_constraint'])
elif(args['--cost_type']=='VAT'):
cost = costs.virtual_adversarial_training(x,t,model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint = args['--norm_constraint'],
num_power_iter = int(args['--num_power_iter']))
elif(args['--cost_type']=='VAT_finite_diff'):
cost = costs.virtual_adversarial_training_finite_diff(x,t,model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint = args['--norm_constraint'],
num_power_iter = int(args['--num_power_iter']))
nll = costs.cross_entropy_loss(x=x,t=t,forward_func=model.forward_test)
error = costs.error(x=x,t=t,forward_func=model.forward_test)
optimizer = optimizers.MomentumSGD(cost=cost,params=model.params,lr=float(args['--initial_learning_rate']),
momentum_ratio=float(args['--momentum_ratio']))
f_train = theano.function(inputs=[], outputs=cost, updates=optimizer.updates,
givens={
x:x_train,
t:t_train})
f_nll_train = theano.function(inputs=[], outputs=nll,
givens={
x:x_train,
t:t_train})
f_nll_test = theano.function(inputs=[], outputs=nll,
givens={
x:x_test,
t:t_test})
f_error_train = theano.function(inputs=[], outputs=error,
givens={
x:x_train,
t:t_train})
f_error_test = theano.function(inputs=[], outputs=error,
givens={
x:x_test,
t:t_test})
if(args['--monitoring_LDS']):
LDS = costs.average_LDS_finite_diff(x,
model.forward_test,
main_obj_type='CE',
epsilon=float(args['--epsilon']),
norm_constraint = args['--norm_constraint'],
num_power_iter = int(args['--num_power_iter_for_monitoring_LDS']))
f_LDS_train = theano.function(inputs=[], outputs=LDS,
givens={
x:x_train})
f_LDS_test = theano.function(inputs=[], outputs=LDS,
givens={
x:x_test})
f_lr_decay = theano.function(inputs=[],outputs=optimizer.lr,
updates={optimizer.lr:theano.shared(numpy.array(args['--learning_rate_decay']).astype(theano.config.floatX))*optimizer.lr})
statuses = {}
statuses['nll_train'] = []
statuses['error_train'] = []
statuses['nll_test'] = []
statuses['error_test'] = []
if(args['--monitoring_LDS']==True):
statuses['LDS_train'] = []
statuses['LDS_test'] = []
statuses['nll_train'].append(f_nll_train())
statuses['error_train'].append(f_error_train())
statuses['nll_test'].append(f_nll_test())
statuses['error_test'].append(f_error_test())
print "[Epoch]",str(0)
print "nll_train : " , statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : " , statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
if(args['--monitoring_LDS']):
statuses['LDS_train'].append(f_LDS_train())
statuses['LDS_test'].append(f_LDS_test())
print "LDS_train : ", statuses['LDS_train'][-1], "LDS_test : " , statuses['LDS_test'][-1]
print "training..."
for epoch in xrange(int(args['--num_epochs'])):
train_cost = f_train()
if((epoch+1)%20==0):
statuses['nll_train'].append(f_nll_train())
statuses['error_train'].append(f_error_train())
statuses['nll_test'].append(f_nll_test())
statuses['error_test'].append(f_error_test())
print "[Epoch]",str(epoch)
print "nll_train : " , statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : " , statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
if(args['--monitoring_LDS']):
statuses['LDS_train'].append(f_LDS_train())
statuses['LDS_test'].append(f_LDS_test())
print "LDS_train : ", statuses['LDS_train'][-1], "LDS_test : " , statuses['LDS_test'][-1]
f_lr_decay()
make_sure_path_exists("./trained_model")
cPickle.dump((model,statuses,args),open('./trained_model/'+args['--save_filename'],'wb'),cPickle.HIGHEST_PROTOCOL)
def train(args):
print args
numpy.random.seed(1)
dataset = cPickle.load(open('dataset/' + args['--dataset_filename']))
x_train = theano.shared(
numpy.asarray(dataset[0][0][0], dtype=theano.config.floatX))
t_train = theano.shared(numpy.asarray(dataset[0][0][1], dtype='int32'))
x_test = theano.shared(
numpy.asarray(dataset[0][1][0], dtype=theano.config.floatX))
t_test = theano.shared(numpy.asarray(dataset[0][1][1], dtype='int32'))
if (args['--cost_type'] == 'dropout'):
model = FNN_syn_dropout(drate=float(args['--dropout_rate']))
else:
model = FNN_syn()
x = T.matrix()
t = T.ivector()
if (args['--cost_type'] == 'MLE' or args['--cost_type'] == 'dropout'):
cost = costs.cross_entropy_loss(x=x,
t=t,
forward_func=model.forward_train)
elif (args['--cost_type'] == 'L2'):
cost = costs.cross_entropy_loss(x=x,t=t,forward_func=model.forward_train) \
+ costs.weight_decay(params=model.params,coeff=float(args['--lamb']))
elif (args['--cost_type'] == 'AT'):
cost = costs.adversarial_training(
x,
t,
model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
lamb=float(args['--lamb']),
norm_constraint=args['--norm_constraint'])
elif (args['--cost_type'] == 'VAT'):
cost = costs.virtual_adversarial_training(
x,
t,
model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']))
elif (args['--cost_type'] == 'VAT_finite_diff'):
cost = costs.virtual_adversarial_training_finite_diff(
x,
t,
model.forward_train,
'CE',
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter']))
nll = costs.cross_entropy_loss(x=x, t=t, forward_func=model.forward_test)
error = costs.error(x=x, t=t, forward_func=model.forward_test)
optimizer = optimizers.MomentumSGD(
cost=cost,
params=model.params,
lr=float(args['--initial_learning_rate']),
momentum_ratio=float(args['--momentum_ratio']))
f_train = theano.function(inputs=[],
outputs=cost,
updates=optimizer.updates,
givens={
x: x_train,
t: t_train
})
f_nll_train = theano.function(inputs=[],
outputs=nll,
givens={
x: x_train,
t: t_train
})
f_nll_test = theano.function(inputs=[],
outputs=nll,
givens={
x: x_test,
t: t_test
})
f_error_train = theano.function(inputs=[],
outputs=error,
givens={
x: x_train,
t: t_train
})
f_error_test = theano.function(inputs=[],
outputs=error,
givens={
x: x_test,
t: t_test
})
if (args['--monitoring_LDS']):
LDS = costs.average_LDS_finite_diff(
x,
model.forward_test,
main_obj_type='CE',
epsilon=float(args['--epsilon']),
norm_constraint=args['--norm_constraint'],
num_power_iter=int(args['--num_power_iter_for_monitoring_LDS']))
f_LDS_train = theano.function(inputs=[],
outputs=LDS,
givens={x: x_train})
f_LDS_test = theano.function(inputs=[],
outputs=LDS,
givens={x: x_test})
f_lr_decay = theano.function(
inputs=[],
outputs=optimizer.lr,
updates={
optimizer.lr:
theano.shared(
numpy.array(args['--learning_rate_decay']).astype(
theano.config.floatX)) * optimizer.lr
})
statuses = {}
statuses['nll_train'] = []
statuses['error_train'] = []
statuses['nll_test'] = []
statuses['error_test'] = []
if (args['--monitoring_LDS'] == True):
statuses['LDS_train'] = []
statuses['LDS_test'] = []
statuses['nll_train'].append(f_nll_train())
statuses['error_train'].append(f_error_train())
statuses['nll_test'].append(f_nll_test())
statuses['error_test'].append(f_error_test())
print "[Epoch]", str(0)
print "nll_train : " , statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : " , statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
if (args['--monitoring_LDS']):
statuses['LDS_train'].append(f_LDS_train())
statuses['LDS_test'].append(f_LDS_test())
print "LDS_train : ", statuses['LDS_train'][
-1], "LDS_test : ", statuses['LDS_test'][-1]
print "training..."
for epoch in xrange(int(args['--num_epochs'])):
train_cost = f_train()
if ((epoch + 1) % 20 == 0):
statuses['nll_train'].append(f_nll_train())
statuses['error_train'].append(f_error_train())
statuses['nll_test'].append(f_nll_test())
statuses['error_test'].append(f_error_test())
print "[Epoch]", str(epoch)
print "nll_train : " , statuses['nll_train'][-1], "error_train : ", statuses['error_train'][-1], \
"nll_test : " , statuses['nll_test'][-1], "error_test : ", statuses['error_test'][-1]
if (args['--monitoring_LDS']):
statuses['LDS_train'].append(f_LDS_train())
statuses['LDS_test'].append(f_LDS_test())
print "LDS_train : ", statuses['LDS_train'][
-1], "LDS_test : ", statuses['LDS_test'][-1]
f_lr_decay()
make_sure_path_exists("./trained_model")
cPickle.dump((model, statuses, args),
open('./trained_model/' + args['--save_filename'], 'wb'),
cPickle.HIGHEST_PROTOCOL)
