How Do I Save and Restore TensorFlow Models?

Saving and Restoring Models in TensorFlow

After training a model in TensorFlow, it's crucial to save it for later use. Here's how to perform these operations:

Saving a Model

In TensorFlow version 0.11 and above, saving a model involves:

1. Creating a tf.train.Saver object to save all variable values.
2. Calling saver.save() to save the model to a file (with a specified name and global step).

Restoring a Model

To restore a saved model:

1. Create a new TensorFlow session.
2. Create a Saver object and use tf.train.import_meta_graph() to load the model's meta graph.
3. Call saver.restore() to restore variable values from the saved file.
4. Access saved variables directly using sess.run('variable_name:0').
5. Create placeholders for new input data and create a feed dictionary to pass them into the graph.
6. Obtain the restored operation you want to run.
7. Call sess.run(op_to_run, feed_dict) to execute the operation with the new input data.

For advanced saving and restoring scenarios, refer to the comprehensive tutorial:

[A Quick Complete Tutorial to Save and Restore TensorFlow Models](link provided)

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