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)