forked from tensorflow/tensorflow
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathdebug_errors.py
More file actions
80 lines (70 loc) · 2.59 KB
/
debug_errors.py
File metadata and controls
80 lines (70 loc) · 2.59 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Example of debugging TensorFlow runtime errors using tfdbg."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import numpy as np
import tensorflow as tf
from tensorflow.python import debug as tf_debug
def main(_):
sess = tf.Session()
# Construct the TensorFlow network.
ph_float = tf.placeholder(tf.float32, name="ph_float")
x = tf.transpose(ph_float, name="x")
v = tf.Variable(np.array([[-2.0], [-3.0], [6.0]], dtype=np.float32), name="v")
m = tf.constant(
np.array([[0.0, 1.0, 2.0], [-4.0, -1.0, 0.0]]),
dtype=tf.float32,
name="m")
y = tf.matmul(m, x, name="y")
z = tf.matmul(m, v, name="z")
if FLAGS.debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess, ui_type=FLAGS.ui_type)
if FLAGS.error == "shape_mismatch":
print(sess.run(y, feed_dict={ph_float: np.array([[0.0], [1.0], [2.0]])}))
elif FLAGS.error == "uninitialized_variable":
print(sess.run(z))
elif FLAGS.error == "no_error":
print(sess.run(y, feed_dict={ph_float: np.array([[0.0, 1.0, 2.0]])}))
else:
raise ValueError("Unrecognized error type: " + FLAGS.error)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.register("type", "bool", lambda v: v.lower() == "true")
parser.add_argument(
"--error",
type=str,
default="shape_mismatch",
help="""\
Type of the error to generate (shape_mismatch | uninitialized_variable |
no_error).\
""")
parser.add_argument(
"--ui_type",
type=str,
default="curses",
help="Command-line user interface type (curses | readline)")
parser.add_argument(
"--debug",
type="bool",
nargs="?",
const=True,
default=False,
help="Use debugger to track down bad values during training")
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)