Create Solutions for TensorFlow Version 1.14 Issue on Elementary OS Loki #71
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
To resolve the issue with TensorFlow 1.14 on Elementary OS Loki, you can try the following solutions:
1.Upgrade TensorFlow: If possible, consider upgrading to a more recent version of TensorFlow, as many bugs and compatibility issues have been addressed in later releases. You can do this using pip:
pip install --upgrade tensorflow
pip install tensorflow-hub==0.6.0
import tensorflow_hub as hub
import tensorflow as tf
module = hub.Module("onnx/shufflenet/1")
preds = module(tf.random_normal(shape=[1, 3, 224, 224], dtype=tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) # Initialize variables
print(sess.run(preds))
4. import tensorflow_hub as hub
import tensorflow as tf
module = hub.Module("onnx/shufflenet/1")
preds = module(tf.random_normal(shape=[1, 3, 224, 224], dtype=tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) # Initialize variables
print(sess.run(preds))
pip check