start=0,
saturate=momentum_saturate,
final_momentum=.99 # Dropout=.5->.99 over 500 epochs.
)
]
# Train
train = pylearn2.train.Train(dataset=dataset_train,
model=model,
algorithm=algorithm,
extensions=extensions,
save_path=save_path,
save_freq=100)
return train
def train(mytrain):
# Execute training loop.
debug = False
logfile = os.path.splitext(mytrain.save_path)[0] + '.log'
print 'Using=%s' % theano.config.device # Can use gpus.
print 'Writing to %s' % logfile
print 'Writing to %s' % mytrain.save_path
sys.stdout = open(logfile, 'w')
mytrain.main_loop()
if __name__ == '__main__':
# Initialize and train.
mytrain = init_train()
train(mytrain)
start=0,
saturate=momentum_saturate,
final_momentum=.99 # Dropout=.5->.99 over 500 epochs.
)
]
# Train
train = pylearn2.train.Train(dataset=dataset_train,
model=model,
algorithm=algorithm,
extensions=extensions,
save_path=save_path,
save_freq=100)
return train
def train(mytrain):
# Execute training loop.
debug = False
logfile = os.path.splitext(mytrain.save_path)[0] + '.log'
print 'Using=%s' % theano.config.device # Can use gpus.
print 'Writing to %s' % logfile
print 'Writing to %s' % mytrain.save_path
sys.stdout = open(logfile, 'w')
mytrain.main_loop()
if __name__=='__main__':
# Initialize and train.
mytrain = init_train()
train(mytrain)
"""
See module-level docstring for a description of the script.
"""
parser = make_argument_parser()
args = parser.parse_args()
if args.outputdir is None:
clobber = True
output_dir = args.outputdirc
else:
clobber = False
output_dir = args.outputdir
try: #adapted from DREME.py by T. Bailey
os.makedirs(output_dir)
except OSError as exc:
if exc.errno == errno.EEXIST:
if not clobber:
print >> sys.stderr, (
"output directory (%s) already exists "
"but program was told not to clobber it") % (output_dir)
sys.exit(1)
else:
print >> sys.stderr, ("output directory (%s) already exists "
"so it will be clobbered") % (output_dir)
train(args.inputdir, output_dir, args.activation, args.numhiddenlayers,
args.numhiddennodesperlayer, args.learningrate, args.minibatchsize,
args.stdev, args.dropout, args.useX1andX2, args.gaussian,
args.validsize, args.randomseed)
