def run(): total_time = time.time() FLAGS.num_gpus = 1 if FLAGS.core_type == 'GPU' else FLAGS.num_gpus LOGGER.debug("Load Data") data_load_time = time.time() data_sets = load_data(input_data, FLAGS.one_hot) data_load_time = time.time() - data_load_time with tf.Graph().as_default(): images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.one_hot, FLAGS.height*FLAGS.width*FLAGS.channels, FLAGS.num_classes) LOGGER.debug("Build Model") if FLAGS.model_type == 'lenet': model = Lenet(images_placeholder, model_config['lenet']) model.build_model() else: model = MLP(images_placeholder, model_config['mlp']) model.build_model() sess, model, train_time = train(model, images_placeholder, labels_placeholder, data_sets.train) test_time = time.time() do_eval(sess, model.model, images_placeholder, labels_placeholder, data_sets.test) test_time = time.time() - test_time sess.close total_time = time.time() - total_time print("****************Example finished********************") print_time('Data load', data_load_time) print_time('Train', train_time) print_time('Test', test_time) print_time('Total', total_time)
def run(argv=None): # pylint: disable=unused-argument total_time = time.time() download_and_extract() if tf.gfile.Exists(FLAGS.train_dir): tf.gfile.DeleteRecursively(FLAGS.train_dir) tf.gfile.MakeDirs(FLAGS.train_dir) train_time, data_load_time = train() test_time, data_load_time2 = evaluate() print("****************Example finished********************") print_time('Data load', data_load_time) print_time('Train', train_time) print_time('Test', test_time) print_time('Total', total_time)
def run(): total_time = time.time() FLAGS.num_gpus = 1 if FLAGS.core_type == 'GPU' else FLAGS.num_gpus LOGGER.debug("Load Data") data_load_time = time.time() data_sets = load_data(input_data, FLAGS.one_hot) data_load_time = time.time() - data_load_time with tf.Graph().as_default(): images_placeholder, labels_placeholder = placeholder_inputs( FLAGS.one_hot, FLAGS.height * FLAGS.width * FLAGS.channels, FLAGS.num_classes) LOGGER.debug("Build Model") if FLAGS.model_type == 'lenet': model = Lenet(images_placeholder, model_config['lenet']) model.build_model() else: model = MLP(images_placeholder, model_config['mlp']) model.build_model() sess, model, train_time = train(model, images_placeholder, labels_placeholder, data_sets.train) test_time = time.time() do_eval(sess, model.model, images_placeholder, labels_placeholder, data_sets.test) test_time = time.time() - test_time sess.close total_time = time.time() - total_time print("****************Example finished********************") print_time('Data load', data_load_time) print_time('Train', train_time) print_time('Test', test_time) print_time('Total', total_time)