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Recreates a Graph saved in a
tf.compat.v1.train.import_meta_graph( meta_graph_or_file, clear_devices=False, import_scope=None, **kwargs )
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
In combination with
export_meta_graph(), this function can be used to
Serialize a graph along with other Python objects such as
Restart training from a saved graph and checkpoints.
Run inference from a saved graph and checkpoints.
... # Create a saver. saver = tf.compat.v1.train.Saver(...variables...) # Remember the training_op we want to run by adding it to a collection. tf.compat.v1.add_to_collection('train_op', train_op) sess = tf.compat.v1.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') for step in xrange(1000000): sess.run(train_op)
Variables, placeholders, and independent operations can also be stored