# 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 numpy as np

import tensorflow as tf

from tensorflow.python import debug as tf_debug

flags = tf.app.flags

FLAGS = flags.FLAGS

flags.DEFINE_string("error", "shape_mismatch", "Type of the error to generate "

"(shape_mismatch | uninitialized_variable | no_error).")

flags.DEFINE_boolean("debug", False,

"Use debugger to track down bad values during training")

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)

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__":

tf.app.run()