experiments/STT-tensorflow
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Merge pull request #24173 from vidakDK:master

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TensorFlower Gardener 2018-12-07 14:23:46 -08:00
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tensorflow/python/training/saver.py
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@ -1377,6 +1377,37 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False,
NOTE: Restarting training from saved `meta_graph` only works if the
device assignments have not changed.
Example 2:
Variables, placeholders, and independent operations can also be stored, as
shown in the following example.
```Python
# Saving contents and operations.
v1 = tf.placeholder(tf.float32, name="v1")
v2 = tf.placeholder(tf.float32, name="v2")
v3 = tf.mul(v1, v2)
vx = tf.Variable(10.0, name="vx")
v4 = tf.add(v3, vx, name="v4")
saver = tf.train.Saver([vx])
sess = tf.Session()
sess.run(tf.initialize_all_variables())
sess.run(vx.assign(tf.add(vx, vx)))
result = sess.run(v4, feed_dict={v1:12.0, v2:3.3})
print(result)
saver.save(sess, "./model_ex1")
```
Later this model can be restored and contents loaded.
```Python
# Restoring variables and running operations.
saver = tf.train.import_meta_graph("./model_ex1.meta")
sess = tf.Session()
saver.restore(sess, "./model_ex1")
result = sess.run("v4:0", feed_dict={"v1:0": 12.0, "v2:0": 3.3})
print(result)
```
Args:
meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including
the path) containing a `MetaGraphDef`.