tf.train.import_meta_graph(meta_graph_or_file, clear_devices=False, import_scope=None, **kwargs)

tf.train.import_meta_graph(meta_graph_or_file, clear_devices=False, import_scope=None, **kwargs)

See the guide: Variables > Exporting and Importing Meta Graphs

Recreates a Graph saved in a MetaGraphDef proto.

This function takes a MetaGraphDef protocol buffer as input. If the argument is a file containing a MetaGraphDef protocol buffer , it constructs a protocol buffer from the file content. The function then adds all the nodes from the graph_def field to the current graph, recreates all the collections, and returns a saver constructed from the saver_def field.

In combination with export_meta_graph(), this function can be used to

• Serialize a graph along with other Python objects such as QueueRunner, Variable into a MetaGraphDef.

• Restart training from a saved graph and checkpoints.

• Run inference from a saved graph and checkpoints.

...
# Create a saver.
saver = tf.train.Saver(...variables...)
# Remember the training_op we want to run by adding it to a collection.
tf.add_to_collection('train_op', train_op)
sess = tf.Session()
for step in xrange(1000000):
    sess.run(train_op)
    if step % 1000 == 0:
        # Saves checkpoint, which by default also exports a meta_graph
        # named 'my-model-global_step.meta'.
        saver.save(sess, 'my-model', global_step=step)

Later we can continue training from this saved meta_graph without building the model from scratch.

with tf.Session() as sess:
  new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
  new_saver.restore(sess, 'my-save-dir/my-model-10000')
  # tf.get_collection() returns a list. In this example we only want the
  # first one.
  train_op = tf.get_collection('train_op')[0]
  for step in xrange(1000000):
    sess.run(train_op)

NOTE: Restarting training from saved meta_graph only works if the device assignments have not changed.

Args:

• meta_graph_or_file: MetaGraphDef protocol buffer or filename (including the path) containing a MetaGraphDef.
• clear_devices: Whether or not to clear the device field for an Operation or Tensor during import.
• import_scope: Optional string. Name scope to add. Only used when initializing from protocol buffer. **kwargs: Optional keyed arguments.

Returns:

A saver constructed from saver_def in MetaGraphDef or None.

A None value is returned if no variables exist in the MetaGraphDef (i.e., there are no variables to restore).

Defined in tensorflow/python/training/saver.py.