(sess, graph, X)
return y_preds
:param sess:
    :param graph:
    :param x_:
    :param y_:
    :return:
def test_model(sess, graph, X):
    """

    :param sess:
    :param graph:
    :param x_:
    :param y_:
    :return:
    """
    batch_eval = test_batch_iter(X, 64)
    input_x = graph.get_operation_by_name('input_x').outputs[0]
    drop_prob = graph.get_operation_by_name('drop_prob').outputs[0]
    logits = graph.get_operation_by_name('score/logits').outputs[0]

    y_preds = []
    for x_batch in batch_eval:
        feed_dict = {input_x: x_batch, drop_prob: 0}
        y_logits = sess.run(logits, feed_dict=feed_dict)
        y_batch_pred = get_logits_label(y_logits)
        y_preds.extend(y_batch_pred)
    return y_preds
def test_model ( sess , graph , X ) : batch_eval = test_batch_iter ( X , 64 ) input_x = graph . get_operation_by_name ( 'input_x' ) . outputs [ 0 ] drop_prob = graph . get_operation_by_name ( 'drop_prob' ) . outputs [ 0 ] logits = graph . get_operation_by_name ( 'score/logits' ) . outputs [ 0 ] y_preds = [ ] for x_batch in batch_eval : feed_dict = { input_x : x_batch , drop_prob : 0 } y_logits = sess . run ( logits , feed_dict = feed_dict ) y_batch_pred = get_logits_label ( y_logits ) y_preds . extend ( y_batch_pred ) return y_preds