Ошибка Bazel при анализе модели tf.estimator

Я пытаюсь создать модель * .pb с использованием tf.estimator и export_savedmodel(), это простой классификатор для классификации набора данных радужной оболочки (4 функции, 3 класса):

import tensorflow as tf


num_epoch = 500
num_train = 120
num_test = 30

# 1 Define input function
def input_function(x, y, is_train):
    dict_x = {
        "thisisinput" : x,
    }

    dataset = tf.data.Dataset.from_tensor_slices((
        dict_x, y
    ))

    if is_train:
        dataset = dataset.shuffle(num_train).repeat(num_epoch).batch(num_train)
    else:   
        dataset = dataset.batch(num_test)

    return dataset


def my_serving_input_fn():
    input_data = tf.placeholder(tf.string, [None], name='input_tensors')
    receiver_tensors = {"inputs" : input_data}

    # 2 Define feature columns
    feature_columns = [
        tf.feature_column.numeric_column(key = "thisisinput", shape=4),]
    features = tf.parse_example(
        input_data, 
        tf.feature_column.make_parse_example_spec(feature_columns))

    return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)


def main(argv):
    tf.set_random_seed(1103) # avoiding different result of random

    # 2 Define feature columns
    feature_columns = [
        tf.feature_column.numeric_column(key = "thisisinput", shape=4),]

    # 3 Define an estimator
    classifier = tf.estimator.DNNClassifier(
        feature_columns=feature_columns,
        hidden_units=[10],
        n_classes=3,
        optimizer=tf.train.GradientDescentOptimizer(0.001),
        activation_fn=tf.nn.relu,
        model_dir = 'modeliris2/'
    )

    # Train the model
    classifier.train(
        input_fn=lambda:input_function(xtrain, ytrain, True)
    )

    # Evaluate the model
    eval_result = classifier.evaluate(
        input_fn=lambda:input_function(xtest, ytest, False)
    )

    print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
    print('\nSaving models...')
    classifier.export_savedmodel("modeliris2pb", my_serving_input_fn)


if __name__ == "__main__":
    tf.logging.set_verbosity(tf.logging.INFO)
    tf.app.run(main)

Будет создан файл saved_model.pb. Я подтвердил, что модель работает. Я также могу создать другую программу, которая ее загружает и запускает. Теперь я хочу подвести итоги и заморозить модель с помощью Bazel. Если я построю Bazel, а затем выполню следующую команду:

bazel-bin/tensorflow/tools/graph_transforms/summarize_graph \
--in_graph=saved_model.pb

Я получаю следующую ошибку:

[libprotobuf ERROR external/protobuf_archive/src/google/protobuf/text_format.cc:307] Error parsing text-format tensorflow.GraphDef: 1:1: Invalid control characters encountered in text.
[libprotobuf ERROR external/protobuf_archive/src/google/protobuf/text_format.cc:307] Error parsing text-format tensorflow.GraphDef: 1:4: Interpreting non ascii codepoint 218.
[libprotobuf ERROR external/protobuf_archive/src/google/protobuf/text_format.cc:307] Error parsing text-format tensorflow.GraphDef: 1:4: Expected identifier, got: �
2018-08-14 11:50:17.759617: E tensorflow/tools/graph_transforms/summarize_graph_main.cc:320] Loading graph 'saved_model.pb' failed with Can't parse saved_model.pb as binary proto
(both text and binary parsing failed for file saved_model.pb)
2018-08-14 11:50:17.759670: E tensorflow/tools/graph_transforms/summarize_graph_main.cc:322] usage: bazel-bin/tensorflow/tools/graph_transforms/summarize_graph
Flags:
--in_graph = "" string input graph file name
--print_structure=false bool whether to print the network connections of the graph

Я не понимаю эту ошибку. Я пробовал использовать начальный файл pb, и он отлично работает, поэтому я думаю, что проблема в том, как tf.estimator создает файл .pb.

Я что-то упускаю при использовании export_savedmodel() или tf.estimator для создания сохраненной модели?

ОБНОВИТЬ

Версия Tensorflow: v1.9.0-0-g25c197e023 1.9.0

Результат tf_env_collect.sh:

== cat /etc/issue ===============================================
Linux rianadam 4.15.0-32-generiC#35-Ubuntu SMP Fri Aug 10 17:58:07 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux
VERSION = "18.04.1 LTS (Bionic Beaver)"
VERSION_ID = "18.04"
VERSION_CODENAME=bionic

== are we in docker =============================================
No

== compiler =====================================================
c++ (Ubuntu 7.3.0-16ubuntu3) 7.3.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.


== uname -a =====================================================
Linux rianadam 4.15.0-32-generiC#35-Ubuntu SMP Fri Aug 10 17:58:07 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux

== check pips ===================================================
numpy               1.15.0 
protobuf            3.6.0  
tensorflow-gpu      1.9.0  

== check for virtualenv =========================================
True

== tensorflow import ============================================
tf.VERSION = 1.9.0
tf.GIT_VERSION = v1.9.0-0-g25c197e023
tf.COMPILER_VERSION = v1.9.0-0-g25c197e023
Sanity check: array([1], dtype=int32)
/home/rian/NgodingYuk/tf_env/env/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
return f(*args, **kwds)
/home/rian/NgodingYuk/tf_env/env/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
return f(*args, **kwds)

== env ==========================================================
LD_LIBRARY_PATH /usr/local/cuda/lib64:/usr/local/cuda-9.0/lib64:/usr/local/cuda/lib64:/usr/local/cuda-9.0/lib64:
DYLD_LIBRARY_PATH is unset

== nvidia-smi ===================================================
Tue Aug 21 11:13:55 2018       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.77                 Driver Version: 390.77                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce 920M        Off  | 00000000:04:00.0 N/A |                  N/A |
| N/A   51C    P0    N/A /  N/A |    367MiB /  2004MiB |     N/A      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0                    Not Supported                                       |
+-----------------------------------------------------------------------------+

== cuda libs  ===================================================
/usr/local/cuda-9.0/lib64/libcudart_static.a
/usr/local/cuda-9.0/lib64/libcudart.so.9.0.176
/usr/local/cuda-9.0/doc/man/man7/libcudart.7
/usr/local/cuda-9.0/doc/man/man7/libcudart.so.7