tf.contrib.checkpoint.UniqueNameTracker
Adds dependencies on trackable objects with name hints.
tf.contrib.checkpoint.UniqueNameTracker()
Useful for creating dependencies with locally unique names.
Example usage:
class SlotManager(tf.contrib.checkpoint.Checkpointable): def __init__(self): # Create a dependency named "slotdeps" on the container. self.slotdeps = tf.contrib.checkpoint.UniqueNameTracker() slotdeps = self.slotdeps slots = [] slots.append(slotdeps.track(tf.Variable(3.), "x")) # Named "x" slots.append(slotdeps.track(tf.Variable(4.), "y")) slots.append(slotdeps.track(tf.Variable(5.), "x")) # Named "x_1"
Attributes | |
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layers |
|
losses |
Aggregate losses from any Layer instances. |
non_trainable_variables |
|
non_trainable_weights |
|
trainable |
|
trainable_variables |
|
trainable_weights |
|
updates |
Aggregate updates from any Layer instances. |
variables |
|
weights |
Methods
track
track( trackable, base_name )
Add a dependency on trackable
.
Args | |
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trackable |
An object to add a checkpoint dependency on. |
base_name |
A name hint, which is uniquified to determine the dependency name. |
Returns | |
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trackable , for chaining. |
Raises | |
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ValueError |
If trackable is not a trackable object. |
__eq__
__eq__( other )
Return self==value.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/checkpoint/UniqueNameTracker