def main():
parser = argparse.ArgumentParser()
# GSN settings
parser.add_argument('--layers', type=int, default=3) # number of hidden layers
parser.add_argument('--walkbacks', type=int, default=5) # number of walkbacks
parser.add_argument('--hidden_size', type=int, default=1500)
parser.add_argument('--hidden_act', type=str, default='tanh')
parser.add_argument('--visible_act', type=str, default='sigmoid')
# training
parser.add_argument('--cost_funct', type=str, default='binary_crossentropy') # the cost function for training
parser.add_argument('--n_epoch', type=int, default=500)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--save_frequency', type=int, default=5) # number of epochs between parameters being saved
parser.add_argument('--early_stop_threshold', type=float, default=0.9995)
parser.add_argument('--early_stop_length', type=int, default=30) # the patience number of epochs
# noise
parser.add_argument('--hidden_add_noise_sigma', type=float, default=2) # default=2
parser.add_argument('--input_salt_and_pepper', type=float, default=0.4) # default=0.4
# hyper parameters
parser.add_argument('--learning_rate', type=float, default=0.25)
parser.add_argument('--momentum', type=float, default=0.5)
parser.add_argument('--annealing', type=float, default=0.995)
parser.add_argument('--noise_annealing', type=float, default=1)
# data
parser.add_argument('--dataset', type=str, default='MNIST')
parser.add_argument('--data_path', type=str, default='../data/')
parser.add_argument('--classes', type=int, default=10)
parser.add_argument('--output_path', type=str, default='../outputs/gsn/')
# argparse does not deal with booleans
parser.add_argument('--vis_init', type=int, default=0)
parser.add_argument('--noiseless_h1', type=int, default=1)
parser.add_argument('--input_sampling', type=int, default=1)
parser.add_argument('--test_model', type=int, default=0)
parser.add_argument('--continue_training', type=int, default=0) # default=0
args = parser.parse_args()
########################################
# Initialization things with arguments #
########################################
outdir = args.output_path + "/" + args.dataset + "/"
data.mkdir_p(outdir)
args.output_path = outdir
# Create the logger
logger = log.Logger(outdir)
logger.log("---------CREATING GSN------------\n\n")
logger.log(args)
# See if we should load args from a previous config file (during testing)
config_filename = outdir + 'config'
if args.test_model and 'config' in os.listdir(outdir):
config_vals = load_from_config(config_filename)
for CV in config_vals:
logger.log(CV)
if CV.startswith('test'):
logger.log('Do not override testing switch')
continue
try:
exec('args.' + CV) in globals(), locals()
except:
exec('args.' + CV.split('=')[0] + "='" + CV.split('=')[1] + "'") in globals(), locals()
else:
# Save the current configuration
# Useful for logs/experiments
logger.log('Saving config')
with open(config_filename, 'w') as f:
f.write(str(args))
######################################
# Load the data, train = train+valid #
######################################
if args.dataset.lower() == 'mnist':
(train_X, train_Y), (valid_X, valid_Y), (test_X, test_Y) = data.load_mnist(args.data_path)
train_X = numpy.concatenate((train_X, valid_X))
train_Y = numpy.concatenate((train_Y, valid_Y))
else:
raise AssertionError(
"Dataset not recognized. Please try MNIST, or implement your own data processing method in data_tools.py")
# transfer the datasets into theano shared variables
train_X, train_Y = data.shared_dataset((train_X, train_Y), borrow=True)
valid_X, valid_Y = data.shared_dataset((valid_X, valid_Y), borrow=True)
test_X, test_Y = data.shared_dataset((test_X, test_Y), borrow=True)
##########################
# Initialize the new GSN #
##########################
gsn = GSN(train_X, valid_X, test_X, vars(args), logger)
# gsn.train()
gsn.load_params('gsn_params_mnist.pkl')
gsn.gen_10k_samples()
# parzen
print
'Evaluating parzen window'
import utils.likelihood_estimation as ll
ll.main(0.20, 'mnist', '../data/', 'samples.npy')
def main():
parser = argparse.ArgumentParser()
# GSN settings
parser.add_argument('--layers', type=int,
default=3) # number of hidden layers
parser.add_argument('--walkbacks', type=int,
default=5) # number of walkbacks
parser.add_argument('--hidden_size', type=int, default=1500)
parser.add_argument('--hidden_act', type=str, default='tanh')
parser.add_argument('--visible_act', type=str, default='sigmoid')
# training
parser.add_argument(
'--cost_funct', type=str,
default='binary_crossentropy') # the cost function for training
parser.add_argument('--n_epoch', type=int, default=500)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument(
'--save_frequency', type=int,
default=5) #number of epochs between parameters being saved
parser.add_argument('--early_stop_threshold', type=float, default=0.9995)
parser.add_argument('--early_stop_length', type=int,
default=30) #the patience number of epochs
# noise
parser.add_argument('--hidden_add_noise_sigma', type=float,
default=2) #default=2
parser.add_argument('--input_salt_and_pepper', type=float,
default=0.4) #default=0.4
# hyper parameters
parser.add_argument('--learning_rate', type=float, default=0.25)
parser.add_argument('--momentum', type=float, default=0.5)
parser.add_argument('--annealing', type=float, default=0.995)
parser.add_argument('--noise_annealing', type=float, default=1)
# data
parser.add_argument('--dataset', type=str, default='MNIST')
parser.add_argument('--data_path', type=str, default='../data/')
parser.add_argument('--classes', type=int, default=10)
parser.add_argument('--output_path', type=str, default='../outputs/gsn/')
# argparse does not deal with booleans
parser.add_argument('--vis_init', type=int, default=0)
parser.add_argument('--noiseless_h1', type=int, default=1)
parser.add_argument('--input_sampling', type=int, default=1)
parser.add_argument('--test_model', type=int, default=0)
parser.add_argument('--continue_training', type=int, default=0) #default=0
args = parser.parse_args()
########################################
# Initialization things with arguments #
########################################
outdir = args.output_path + "/" + args.dataset + "/"
data.mkdir_p(outdir)
args.output_path = outdir
# Create the logger
logger = log.Logger(outdir)
logger.log("---------CREATING GSN------------\n\n")
logger.log(args)
# See if we should load args from a previous config file (during testing)
config_filename = outdir + 'config'
if args.test_model and 'config' in os.listdir(outdir):
config_vals = load_from_config(config_filename)
for CV in config_vals:
logger.log(CV)
if CV.startswith('test'):
logger.log('Do not override testing switch')
continue
try:
exec('args.' + CV) in globals(), locals()
except:
exec('args.' + CV.split('=')[0] + "='" + CV.split('=')[1] +
"'") in globals(), locals()
else:
# Save the current configuration
# Useful for logs/experiments
logger.log('Saving config')
with open(config_filename, 'w') as f:
f.write(str(args))
######################################
# Load the data, train = train+valid #
######################################
if args.dataset.lower() == 'mnist':
(train_X,
train_Y), (valid_X,
valid_Y), (test_X,
test_Y) = data.load_mnist(args.data_path)
train_X = numpy.concatenate((train_X, valid_X))
train_Y = numpy.concatenate((train_Y, valid_Y))
else:
raise AssertionError(
"Dataset not recognized. Please try MNIST, or implement your own data processing method in data_tools.py"
)
# transfer the datasets into theano shared variables
train_X, train_Y = data.shared_dataset((train_X, train_Y), borrow=True)
valid_X, valid_Y = data.shared_dataset((valid_X, valid_Y), borrow=True)
test_X, test_Y = data.shared_dataset((test_X, test_Y), borrow=True)
##########################
# Initialize the new GSN #
##########################
gsn = GSN(train_X, valid_X, test_X, vars(args), logger)
# gsn.train()
gsn.load_params('gsn_params_mnist.pkl')
gsn.gen_10k_samples()
# parzen
print 'Evaluating parzen window'
import utils.likelihood_estimation as ll
ll.main(0.20, 'mnist', '../data/', 'samples.npy')
def main():
parser = argparse.ArgumentParser()
# GSN settings
parser.add_argument('--layers', type=int, default=3) # number of hidden layers
parser.add_argument('--walkbacks', type=int, default=5) # number of walkbacks
parser.add_argument('--hidden_size', type=int, default=1500)
parser.add_argument('--hidden_act', type=str, default='tanh')
parser.add_argument('--visible_act', type=str, default='sigmoid')
# training
parser.add_argument('--cost_funct', type=str, default='binary_crossentropy') # the cost function for training
parser.add_argument('--n_epoch', type=int, default=500)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--save_frequency', type=int, default=10) #number of epochs between parameters being saved
parser.add_argument('--early_stop_threshold', type=float, default=0.9996)
parser.add_argument('--early_stop_length', type=int, default=30) #the patience number of epochs
# noise
parser.add_argument('--hidden_add_noise_sigma', type=float, default=4) #default=2
parser.add_argument('--input_salt_and_pepper', type=float, default=0.8) #default=0.4
# hyper parameters
parser.add_argument('--learning_rate', type=float, default=0.25)
parser.add_argument('--momentum', type=float, default=0.5)
parser.add_argument('--annealing', type=float, default=0.995)
parser.add_argument('--noise_annealing', type=float, default=0.98)
# data
parser.add_argument('--dataset', type=str, default='MNIST')
parser.add_argument('--data_path', type=str, default='../data/')
parser.add_argument('--classes', type=int, default=10)
parser.add_argument('--output_path', type=str, default='../outputs/gsn/')
# argparse does not deal with booleans
parser.add_argument('--vis_init', type=int, default=0)
parser.add_argument('--noiseless_h1', type=int, default=1)
parser.add_argument('--input_sampling', type=int, default=1)
parser.add_argument('--test_model', type=int, default=0)
parser.add_argument('--continue_training', type=int, default=0) #default=0
args = parser.parse_args()
########################################
# Initialization things with arguments #
########################################
outdir = args.output_path + "/" + args.dataset + "/"
data.mkdir_p(outdir)
args.output_path = outdir
# Create the logger
logger = log.Logger(outdir)
logger.log("---------CREATING GSN------------\n\n")
logger.log(args)
# See if we should load args from a previous config file (during testing)
config_filename = outdir+'config'
if args.test_model and 'config' in os.listdir(outdir):
config_vals = load_from_config(config_filename)
for CV in config_vals:
logger.log(CV)
if CV.startswith('test'):
logger.log('Do not override testing switch')
continue
try:
exec('args.'+CV) in globals(), locals()
except:
exec('args.'+CV.split('=')[0]+"='"+CV.split('=')[1]+"'") in globals(), locals()
else:
# Save the current configuration
# Useful for logs/experiments
logger.log('Saving config')
with open(config_filename, 'w') as f:
f.write(str(args))
######################################
# Load the data, train = train+valid #
######################################
if args.dataset.lower() == 'mnist':
(train_X, train_Y), (valid_X, valid_Y), (test_X, test_Y) = data.load_mnist(args.data_path)
train_X = numpy.concatenate((train_X, valid_X))
train_Y = numpy.concatenate((train_Y, valid_Y))
else:
raise AssertionError("Dataset not recognized. Please try MNIST, or implement your own data processing method in data_tools.py")
# transfer the datasets into theano shared variables
train_X, train_Y = data.shared_dataset((train_X, train_Y), borrow=True)
valid_X, valid_Y = data.shared_dataset((valid_X, valid_Y), borrow=True)
test_X, test_Y = data.shared_dataset((test_X, test_Y), borrow=True)
##########################
# Initialize the new GSN #
##########################
gsn = GSN(train_X, valid_X, test_X, vars(args), logger)
# Load initial weights and biases from file if testing
params_to_load = 'gsn_params.pkl'
if args.test_model and os.path.isfile(params_to_load):
logger.log("\nLoading existing GSN parameters")
loaded_params = cPickle.load(open(params_to_load,'r'))
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[:len(gsn.weights_list)], gsn.weights_list)]
[p.set_value(lp.get_value(borrow=False)) for lp, p in zip(loaded_params[len(gsn.weights_list):], gsn.bias_list)]
else:
logger.log("Could not find existing GSN parameter file {}, training instead.".format(params_to_load))
args.test_model = False
#########################################
# Train or test the new GSN on the data #
#########################################
# Train if not test
if not args.test_model:
gsn.train()
# Otherwise, test
else:
gsn.test()
def main():
parser = argparse.ArgumentParser()
# GSN settings
parser.add_argument('--layers', type=int,
default=3) # number of hidden layers
parser.add_argument('--walkbacks', type=int,
default=5) # number of walkbacks
parser.add_argument('--hidden_size', type=int, default=1500)
parser.add_argument('--hidden_act', type=str, default='tanh')
parser.add_argument('--visible_act', type=str, default='sigmoid')
# training
parser.add_argument(
'--cost_funct', type=str,
default='binary_crossentropy') # the cost function for training
parser.add_argument('--n_epoch', type=int, default=500)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument(
'--save_frequency', type=int,
default=10) #number of epochs between parameters being saved
parser.add_argument('--early_stop_threshold', type=float, default=0.9996)
parser.add_argument('--early_stop_length', type=int,
default=30) #the patience number of epochs
# noise
parser.add_argument('--hidden_add_noise_sigma', type=float,
default=4) #default=2
parser.add_argument('--input_salt_and_pepper', type=float,
default=0.8) #default=0.4
# hyper parameters
parser.add_argument('--learning_rate', type=float, default=0.25)
parser.add_argument('--momentum', type=float, default=0.5)
parser.add_argument('--annealing', type=float, default=0.995)
parser.add_argument('--noise_annealing', type=float, default=0.98)
# data
parser.add_argument('--dataset', type=str, default='MNIST')
parser.add_argument('--data_path', type=str, default='../data/')
parser.add_argument('--classes', type=int, default=10)
parser.add_argument('--output_path', type=str, default='../outputs/gsn/')
# argparse does not deal with booleans
parser.add_argument('--vis_init', type=int, default=0)
parser.add_argument('--noiseless_h1', type=int, default=1)
parser.add_argument('--input_sampling', type=int, default=1)
parser.add_argument('--test_model', type=int, default=0)
parser.add_argument('--continue_training', type=int, default=0) #default=0
args = parser.parse_args()
########################################
# Initialization things with arguments #
########################################
outdir = args.output_path + "/" + args.dataset + "/"
data.mkdir_p(outdir)
args.output_path = outdir
# Create the logger
logger = log.Logger(outdir)
logger.log("---------CREATING GSN------------\n\n")
logger.log(args)
# See if we should load args from a previous config file (during testing)
config_filename = outdir + 'config'
if args.test_model and 'config' in os.listdir(outdir):
config_vals = load_from_config(config_filename)
for CV in config_vals:
logger.log(CV)
if CV.startswith('test'):
logger.log('Do not override testing switch')
continue
try:
exec('args.' + CV) in globals(), locals()
except:
exec('args.' + CV.split('=')[0] + "='" + CV.split('=')[1] +
"'") in globals(), locals()
else:
# Save the current configuration
# Useful for logs/experiments
logger.log('Saving config')
with open(config_filename, 'w') as f:
f.write(str(args))
######################################
# Load the data, train = train+valid #
######################################
if args.dataset.lower() == 'mnist':
(train_X,
train_Y), (valid_X,
valid_Y), (test_X,
test_Y) = data.load_mnist(args.data_path)
train_X = numpy.concatenate((train_X, valid_X))
train_Y = numpy.concatenate((train_Y, valid_Y))
else:
raise AssertionError(
"Dataset not recognized. Please try MNIST, or implement your own data processing method in data_tools.py"
)
# transfer the datasets into theano shared variables
train_X, train_Y = data.shared_dataset((train_X, train_Y), borrow=True)
valid_X, valid_Y = data.shared_dataset((valid_X, valid_Y), borrow=True)
test_X, test_Y = data.shared_dataset((test_X, test_Y), borrow=True)
##########################
# Initialize the new GSN #
##########################
gsn = GSN(train_X, valid_X, test_X, vars(args), logger)
# Load initial weights and biases from file if testing
params_to_load = 'gsn_params.pkl'
if args.test_model and os.path.isfile(params_to_load):
logger.log("\nLoading existing GSN parameters")
loaded_params = cPickle.load(open(params_to_load, 'r'))
[
p.set_value(lp.get_value(borrow=False)) for lp, p in zip(
loaded_params[:len(gsn.weights_list)], gsn.weights_list)
]
[
p.set_value(lp.get_value(borrow=False)) for lp, p in zip(
loaded_params[len(gsn.weights_list):], gsn.bias_list)
]
else:
logger.log(
"Could not find existing GSN parameter file {}, training instead.".
format(params_to_load))
args.test_model = False
#########################################
# Train or test the new GSN on the data #
#########################################
# Train if not test
if not args.test_model:
gsn.train()
# Otherwise, test
else:
gsn.test()
