def main(): if params.gpu_flag is not False: cuda.init(params.gpu_flag) print 'fetching data ...' fetcher = cifar(norm=False) bo = BO( train_model, { 'wscale1': (-5, 0), 'wscale2': (-5, 0), 'wscale3': (-5, 0), 'wscale4': (-5, 0), 'wscale5': (-5, 0), 'lr': (-4, -2), 'batchsize': (30, 300), 'momentum': (0.5, 1.0), 'decay': (-4, -2) }) """ bo.explore({'wscale1' : [-4], 'wscale2' : [-2], 'wscale3' : [-2], 'wscale4' : [-1], 'wscale5' : [-1], 'lr' : [-3], 'batchsize' : [100], 'momentum' : [0.9], 'decay' : [-3] }) """ bo.add_f_args('fetcher', fetcher) bo.maximize(init_points=params.opt_init_points, n_iter=params.opt_iter) print bo.res['max']
def main(): if params.gpu_flag is not False: cuda.init(params.gpu_flag) print 'fetching data ...' fetcher = cifar(norm=False) bo = BO(train_model, {'wscale1' : (-5, 0), 'wscale2' : (-5, 0), 'wscale3' : (-5, 0), 'wscale4' : (-5, 0), 'wscale5' : (-5, 0), 'lr' : (-4, -2), 'batchsize' : (30, 300), 'momentum' : (0.5, 1.0), 'decay' : (-4, -2) }) """ bo.explore({'wscale1' : [-4], 'wscale2' : [-2], 'wscale3' : [-2], 'wscale4' : [-1], 'wscale5' : [-1], 'lr' : [-3], 'batchsize' : [100], 'momentum' : [0.9], 'decay' : [-3] }) """ bo.add_f_args('fetcher', fetcher) bo.maximize(init_points=params.opt_init_points, n_iter=params.opt_iter) print bo.res['max']
def main(): if params.gpu_flag is not False: cuda.init(params.gpu_flag) if params.model_name is False: model = init_model() else: model = load_model(params.model_name) optimizer = init_optimizer(model) print 'fetching data ...' fetcher = cifar(norm=False) print 'done' train_and_val(model, optimizer, fetcher)