def run_training(reduced_dataset, output_directory, flavor_weights, normalization, nodes = None, debug = False, events = 1000000, other_opt_dict = {}, ): if not os.path.isdir(output_directory): os.mkdir(output_directory) elif glob.glob(output_directory + '/*.root*'): raise OverwriteError('root files found in %s' % output_directory) if nodes is None: nodes = (20, 10) flags = 't' if debug: flags += 'd' pynn.trainNN(reduced_dataset = reduced_dataset, output_directory = output_directory, n_iterations = 10000, normalization = normalization, nodes = nodes, flavor_weights = flavor_weights, n_training_events_target = events, flags = flags, **other_opt_dict)
# with_ip3d = True # out_dir = 'weights' settings = dict(config.items('net')) full_ds_name = 'reduceddataset_%s_forNN.root' % input_ds full_path = '../reduceddatasets/' + full_ds_name class_name = 'JetFitterNN_' + input_ds for out_dir, ip3d_state in [('ip3d_weights',True),('no_ip3d_weights',False)]: if not os.path.isdir(out_dir): os.mkdir(out_dir) elif glob.glob(out_dir + '/*'): print "files found in %s, skipping" % out_dir continue pynn.trainNN(input_file = full_path, output_class = class_name, n_iterations = int(settings['n_iterations']), dilution_factor = int(settings['dilution_factor']), use_sd = False, with_ip3d = ip3d_state, nodes_first_layer = int(settings['nodes_1']), nodes_second_layer = int(settings['nodes_2']), debug = True, output_dir = out_dir)