How to Save and Restore Trained TensorFlow Models?

How to Persist and Retrieve Trained Tensorflow Models

In Tensorflow, saving and restoring trained models is a crucial aspect of machine learning workflows. Here's a comprehensive guide on how to accomplish these tasks:

Saving a Trained Model

Version 0.11 and Above:

import tensorflow as tf

# Create a saver object to save all variables
saver = tf.train.Saver()

# Save the graph with the specified global step
saver.save(sess, 'my_test_model', global_step=1000)

Restoring a Saved Model

import tensorflow as tf

sess = tf.Session()

# Restore graph and weights using meta graph and restore operation
saver = tf.train.import_meta_graph('my_test_model-1000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))

# Retrieve saved variables and operations
# ...

For more advanced use cases, refer to the resources provided in the referenced documentation for a comprehensive explanation of these techniques.

The above is the detailed content of How to Save and Restore Trained TensorFlow Models?. For more information, please follow other related articles on the PHP Chinese website!