Runtimeerror: the session graph is empty. add operations to the graph before calling run().

Question

Problem :

I am new to Python and Tensorflow so I just simply typed the following code :

import tensorflow as tf

print(tf.__version__)

# Build a dataflow graph.

c = tf.constant([[1.0, 2.0], [3.0, 4.0]])

d = tf.constant([[1.0, 1.0], [0.0, 1.0]])

e = tf.matmul(c, d)

# Constructing a `Session` to execute the graph.

sess = tf.compat.v1.Session()

# Executing the graph and storing the value that `e` represents in `result`.

result = sess.run(e)

But it is giving me following error:

2.0.0-beta1

I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

Traceback (most recent call last):

  File "/Users/yupng/Documents/Dissertation/kmnist/kminst_v1.0.py", line 14, in <module>

    result = sess.run(e)

  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 950, in run

    run_metadata_ptr)

  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1098, in _run

    raise RuntimeError('The Session graph is empty.  Add operations to the '

RuntimeError: The Session graph is empty.  Add operations to the graph before calling run().

Process finished with exit code 1

How can I fix this error?

Answer 1

The TF 2.0 supports eager execution it means you need not explicitly create the session and run the code in it. So the simplest solution is as follows:

import tensorflow as tf

print(tf.__version__)

# Building a dataflow graph.

c = tf.constant([[1.0, 2.0], [3.0, 4.0]])

d = tf.constant([[1.0, 1.0], [0.0, 1.0]])

e = tf.matmul(c, d)

print(e)

The output as below:

2.0.0-beta1

tf.Tensor(

[[1. 3.]

 [3. 7.]], shape=(2, 2), dtype=float32)

But if you want you can use the session as follows:

import tensorflow as tf

print(tf.__version__)

# Constructing a `Session` to execute the graph.

with tf.compat.v1.Session() as sess:

  # Build a dataflow graph.

  c = tf.constant([[1.0, 2.0], [3.0, 4.0]])

  d = tf.constant([[1.0, 1.0], [0.0, 1.0]])

  e = tf.matmul(c, d)

  # Executing the graph and storing the value that `e` represents in `result

  result = sess.run(e)

  print(result)

which gives

2.0.0-beta1

[[1. 3.]

 [3. 7.]]

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