* Consume Aborted exception, because it happens on PS restarts. Keep training the model.
* Make sure to stop session before coordinator stop, to abort the queues blocked on enqueue op.
* Consume exception when coordinator is asked to stop, if threads didn't stop yet.
Change: 125427252

* optimize_loss expects 0d Tensor.
* Estimator.get_eval_ops requires to have `targets` not None.
* Adding asserts for shapes in rnn_cells.
* Added error check in rnn.rnn.
Change: 125425807

Change: 125421501

For local run, support to run ValidationMonitor while training is running if `local_eval_frequency` specified.
Change: 125412804

This reduces the number of unnecessary NumPy array allocations when
feeding values into a TensorFlow session, and can reduce heap
fragmentation.

Fixes #2942 (as far as possible). Note that we recommend that you use
tcmalloc when running TensorFlow, as it is less susceptible to heap
fragmentation with mmany large objects.
Change: 125407787

Update feature_column to use string_to_hash_bucket_fast instead of the deprecated and not stable string_to_hash_bucket.
Change: 125401852

Change: 125400736

Change: 125400645

Handled: Crossing multi-dimensional bucketized feature should assign unique id's for different dimensions.
Change: 125400598

Change: 125399142

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Change: 125394884

public section.
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Change: 125386366

Change: 125381550

Change: 125380874

Change: 125380336

shapes.

Also restored math_ops_test and shape_inference_testutil_test in BUILD file
that were dropped from original change somehow.
Change: 125376193

Change: 125375538

comment/reformat in examples.
Change: 125374843

Refactor Get2dOutputSizes/Get2dOutputSizesVerbose/Get3dOutputSizes to share a common 1-dimensional GetWindowedOutputSize/GetWindowedOutputSizeVerbose.

The output sizes and padding of each dimension of a windowed operation (such as convolution or pooling) are orthogonal and can be computed independently. We can simplify the code by providing a 1D size computation and calling it for each dimension.

Also remove special cases for 1x1 spatial convolutions in dimension calculations; they add complexity and are a case that the general code handles correctly.

In general, 2D convolutions and their gradients have a lot of shape calculation code that is duplicated for each spatial dimension. This CL is a step in the direction of treating spatial dimensions the same so we can share more code.
Change: 125360639

Change: 125359897

This change should fix issues where (e.g.) a node is added after queue
runners are started, and an error like `NotFoundError: FetchOutputs
node input_producer_Dequeue:0: not found` is reported.

The cause of the error was an incorrect multiple read of the
`Graph.version` property. A graph has a `version` property that is
(currently) defined to be the count of nodes in the graph. To keep the
front-end and session graphs in sync, the `tf.Session` maintains a
`_current_version` property, which is intended to be the last version
at which the graph was synced to the backend (via
TF_ExtendGraph). However, the logic was wrong in the face of
concurrent writes to the graph:

1. x = read `Session._current_version`.
2. y = read `Session._graph.version`.
3. If y > x:
   - Call `TF_ExtendGraph(Session._graph.as_graph_def(from_version=x))`
4. z = read `Session._graph.version`.
5. Set `Session._current_version` to z.

Written this way, the race condition is clear: a node added after step
3 and before step 4 would never be included in an TF_ExtendGraph call,
and so would never be added to the graph.

The fix involves returning the current graph version atomically with
the GraphDef, using a new internal method called
`Graph._as_graph_def()`. These are protected by the graph's mutex, to
ensure that no node can be added between creating the GraphDef and
reading the version.

Fixes #2957.
Change: 125356853

Change: 125246026

Change: 125225689

Change: 125225345

Update BaseDistribution API for NaN handling.  Doc only change in BaseDistribution (which does not specify __init__ kwargs).
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Change: 125134620