def prepare_data_for_test(): data_gen.init() task = data_gen.find_data_task(cnf.task) task.prepare_visualisation_data() data_gen.collect_bins() data_gen.print_bin_usage()
if not cnf.use_two_gpus: os.environ["CUDA_VISIBLE_DEVICES"] = cnf.gpu_instance os.environ["TF_ENABLE_AUTO_MIXED_PRECISION"] = "1" countList = [cnf.batch_size for x in cnf.bins] np.set_printoptions(linewidth=2000, precision=4, suppress=True) # prepare training and test data max_length = cnf.bins[-1] data_gen.init() if cnf.task in cnf.language_tasks: task = data_gen.find_data_task(cnf.task) task.prepare_data() data_gen.collect_bins() data_gen.print_bin_usage() else: for length in range(1, max_length + 1): n_examples = cnf.data_size data_gen.init_data(cnf.task, length, n_examples, cnf.n_input) data_gen.collect_bins() if len(data_gen.train_set[cnf.task][cnf.forward_max]) == 0: data_gen.init_data(cnf.task, cnf.forward_max, cnf.test_data_size, cnf.n_input) data_supplier = data_feeder.create_data_supplier() # Perform training with tf.Graph().as_default(): learner = RSE(cnf.n_hidden, cnf.bins, cnf.n_input, countList, cnf.n_output, cnf.dropout_keep_prob,