How to Effectively Save and Restore Trained Models in TensorFlow?

Saving and Restoring Trained Models in Tensorflow

After training a model in Tensorflow, preserving and reusing it is crucial. Here's how to effectively handle model storage:

Saving the Trained Model (Tensorflow version 0.11 and above):

1. Prepare Input: Define placeholders and prepare the feed dictionary with input data.
2. Define Operations: Specify the operations to be restored, such as addition or multiplication.
3. Create Saver Object: Instantiate a saver object that manages variable storage.
4. Save the Graph: Use the saver.save() method to store the model, including variables and graph structure.

Example Code:

import tensorflow as tf

# Prepare input placeholders
w1 = tf.placeholder("float", name="w1")
w2 = tf.placeholder("float", name="w2")

# Define test operation
w3 = tf.add(w1, w2)
w4 = tf.multiply(w3, tf.Variable(2.0, name="bias"), name="op_to_restore")

# Initialize variables and run session
sess = tf.Session()
sess.run(tf.global_variables_initializer())

# Create saver object
saver = tf.train.Saver()

# Save the model
saver.save(sess, 'my_test_model', global_step=1000)

Restoring the Saved Model:

1. Load Meta Graph: Import the meta graph to access the saved model structure.
2. Restore Variables: Use the saver.restore() method to retrieve saved variables.
3. Get Placeholders and Feed Data: Obtain input placeholders and feed them with new data.
4. Access Saved Operations: Locate the operations you want to run and execute them.

Example Code:

# Restore model
saver = tf.train.import_meta_graph('my_test_model-1000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))

# Get placeholders and feed data
w1 = sess.graph.get_tensor_by_name("w1:0")
w2 = sess.graph.get_tensor_by_name("w2:0")
feed_dict = {w1: 13.0, w2: 17.0}

# Run saved operation
op_to_restore = sess.graph.get_tensor_by_name("op_to_restore:0")
result = sess.run(op_to_restore, feed_dict)

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