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):
- Create placeholders and define TensorFlow operations for your model.
- Initialize TensorFlow variables.
- Create a tf.train.Saver object.
- 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:
- Load the meta graph and restore weights using the tf.train.import_meta_graph function.
- Access the saved variables directly.
- Create placeholders and feed new data.
- 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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