How Can I Save and Restore TensorFlow Models?

Saving and Restoring Tensorflow Models

In Tensorflow, model saving and restoring enables preserving trained models and leveraging them for future use. Following are the steps involved:

Saving the Model (Tensorflow 0.11 and above):

1. Create placeholders and define TensorFlow operations for your model.
2. Initialize TensorFlow variables.
3. Create a tf.train.Saver object.
4. Call the saver.save method with the session and model path.

Example:

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

# Define operations
w3 = tf.add(w1, w2)
w4 = tf.multiply(w3, 2.0, name="op_to_restore")

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

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

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

Restoring the Model:

1. Load the meta graph and restore weights using the tf.train.import_meta_graph function.
2. Access the saved variables directly.
3. Create placeholders and feed new data.
4. Access and run the desired operation.

Example:

# Load the meta graph
sess = tf.Session()
saver = tf.train.import_meta_graph('my_model-1000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))

# Access saved variables
print(sess.run('bias:0'))  # Prints the saved bias value

# Create placeholders and feed new data
w1 = tf.get_default_graph().get_tensor_by_name("w1:0")
w2 = tf.get_default_graph().get_tensor_by_name("w2:0")
feed_dict = {w1: 13.0, w2: 17.0}

# Access and run the operation
op_to_restore = tf.get_default_graph().get_tensor_by_name("op_to_restore:0")
print(sess.run(op_to_restore, feed_dict))  # Prints the result of the restored operation

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