debug_flag.DEBUG = False model = make_model(n_vert_max, n_feat=n_feat, n_class=n_class) model.load_weights(args.weights_path) if args.input_type == 'h5': from generators.h5 import make_dataset elif args.input_type == 'root': from generators.uproot_fixed import make_dataset elif args.input_type == 'root-sparse': from generators.uproot_jagged_keep import make_dataset inputs, truth, _ = make_dataset(args.data_path, features=features, n_vert_max=n_vert_max, n_sample=args.n_sample, dataset_name=args.input_name) n_sample = inputs[0].shape[0] prob = model.predict(inputs, verbose=1) if n_class == 2: prob = np.squeeze(prob) print( 'accuracy', np.mean( np.asarray(np.asarray(prob > 0.5, dtype=np.int32) == truth, dtype=np.float32))) else:
dataset_name=args.input_name) fit_kwargs['validation_data'] = valid_gen fit_kwargs['validation_steps'] = n_valid_steps callbacks = [sparsity.UpdatePruningStep()] prune_model.fit_generator(train_gen, **fit_kwargs, callbacks=callbacks) else: if args.input_type == 'h5': from generators.h5 import make_dataset elif args.input_type == 'root': from generators.uproot_fixed import make_dataset elif args.input_type == 'root-sparse': from generators.uproot_jagged_keep import make_dataset inputs, truth, shuffle = make_dataset(args.train_path[0], features=features, n_vert_max=n_vert_max, dataset_name=args.input_name) fit_kwargs = { 'epochs': args.num_epochs, 'batch_size': args.batch_size, 'shuffle': shuffle } if args.validation_path: val_inputs, val_truth, _ = make_dataset( args.validation_path[0], format=input_format, features=features, n_vert_max=n_vert_max, y_features=y_features, dataset_name=args.input_name)