/TensorFlow Python


Note: Functions taking Tensor arguments can also take anything accepted by tf.convert_to_tensor.


Variable helper functions

TensorFlow provides a set of functions to help manage the set of variables collected in the graph.

Saving and Restoring Variables

Sharing Variables

TensorFlow provides several classes and operations that you can use to create variables contingent on certain conditions.

Variable Partitioners for Sharding

Sparse Variable Updates

The sparse update ops modify a subset of the entries in a dense Variable, either overwriting the entries or adding / subtracting a delta. These are useful for training embedding models and similar lookup-based networks, since only a small subset of embedding vectors change in any given step.

Since a sparse update of a large tensor may be generated automatically during gradient computation (as in the gradient of tf.gather), an tf.IndexedSlices class is provided that encapsulates a set of sparse indices and values. IndexedSlices objects are detected and handled automatically by the optimizers in most cases.

Read-only Lookup Tables

Exporting and Importing Meta Graphs

Deprecated functions (removed after 2017-03-02). Please don't use them.

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.