forked from takerum/vat
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train_syn.py
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train_syn.py
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"""
Usage:
train.py [--dataset_filename=<str>] [--save_filename=<str>] \
[--num_epochs=<N>] [--initial_learning_rate=<float>] [--learning_rate_decay=<float>] [--momentum_ratio=<float>]\
[--cost_type=<str>] \
[--dropout_rate=<float>] [--lamb=<float>][--epsilon=<float>][--norm_constraint=<str>][--num_power_iter=<N>] \
[--monitoring_LDS] [--num_power_iter_for_monitoring_LDS=<N>]
train.py -h | --help
Options:
-h --help Show this screen.
--dataset_filename=<str> [default: syndata_1.pkl]
--save_filename=<str> [default: trained_model.pkl]
--num_epochs=<N> num_epochs [default: 1000].
--initial_learning_rate=<float> initial_learning_rate [default: 1.0].
--learning_rate_decay=<float> learning_rate_decay [default: 0.995].
--momentum_ratio=<float> [default: 0.9].
--cost_type=<str> cost_type [default: MLE].
--dropout_rate=<float> [default: 0.0].
--lamb=<float> [default: 1.0].
--epsilon=<float> [default: 0.5].
--norm_constraint=<str> [default: L2].
--num_power_iter=<N> [default: 1].
--monitoring_LDS
--num_power_iter_for_monitoring_LDS=<N> [default: 5].
"""
from docopt import docopt
import numpy
import theano
import theano.tensor as T
import cPickle
from source import optimizers
from source import costs
from models.fnn_syn import FNN_syn
from models.fnn_syn_dropout import FNN_syn_dropout
import os
import errno
def make_sure_path_exists(path):
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
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)
if __name__=='__main__':
args = docopt(__doc__)
train(args)