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Branch 192771889 #18497
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Branch 192771889 #18497
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Explicitly track function input arg expansion into Placeholders, and keep metadata to map between FunctionDef and GraphDef connectivity formats. PiperOrigin-RevId: 192462592
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…les.get_or_create_global_step(). PiperOrigin-RevId: 192476077
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…taset_op_test to "medium". PiperOrigin-RevId: 192484895
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…` in BN (not yet supported by TPU compilation). PiperOrigin-RevId: 192502020
…. Choosing not to do this at static analysis because it exposes the scope to any node, making it easier to use by any specialization of a transformer. PiperOrigin-RevId: 192502309
https://github.com/tensorflow/hub/tree/master/examples/image_retraining has the same tool, upgraded to use TensorFlow Hub instead of raw graph defs. PiperOrigin-RevId: 192502469
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…_jacobian` methods to take event_ndims. The class level event_ndims parameter is being deprecated in favor of passing it in to the `log_det_jacobian` methods. Specific changes: - `log_det_jacobian` signatures are now `log_det_jacobian(input, event_ndims)` - Constructors no long have event_ndims passed in (e.g. Affine() vs. Affine(event_ndims=0)). - All bijectors must specify a subset of [forward_min_event_ndims, inverse_min_event_ndims]. This is the minimal dimensionality the bijector operates on, with it being "broadcasted" to any passed in event_ndims (e.g. Exp has forward_min_event_ndims = 0. That means it operates on scalars. However, we can use the bijector on any event_ndims > 0 (i.e. we've broadcasted the transformation to work on any amount of event_ndims > 0), and jacobian reduction will work in those cases. As a result of this change, all bijectors should "broadcast" (e.g. Sigmoid now works on any number of event_ndims). Other changes (internal and documentation): - Added clarifications on Jacobian Determinant vs. Jacobian Matrix. - Added clarifications on min_event_ndims, and what the jacobian reduction is over. - Changed caching of ildj to be keyed on event_ndims. - Several bug fixes to bugs unearthed while writing this code (e.g. transformed distribution shape computation being incorrect) PiperOrigin-RevId: 192504919
…n by mutating NodeDef before creating the copied operation. PiperOrigin-RevId: 192505209
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…e updated checkpoints from the contents. PiperOrigin-RevId: 192517819
…able in the metagraph. PiperOrigin-RevId: 192518307
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… trees estimators. PiperOrigin-RevId: 192521398
data are saved with host prefix. PiperOrigin-RevId: 192523668
Implement a vectorized way to compute the same thing instead. PiperOrigin-RevId: 192524667
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…eras backend. PiperOrigin-RevId: 192707345
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…r,Platform}. These types live inside StreamExecutor's namespace, but they are specific to XLA. Therefore they either shouldn't live in SE's namespace or should have "XLA" in the name. Moving them out of SE's namespace is ugly, because almost every type used inside of these headers then needs to be qualified. So name-change it is. This patch was generated by a mechanical find/replace. PiperOrigin-RevId: 192724238
-- Refactor utility functions into pruning_utils.py and add tests PiperOrigin-RevId: 192727737
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…ce. This removes the need to explicitly import internal components (barring the tf module which cannot be imported directly). PiperOrigin-RevId: 192771440
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yifeif
approved these changes
Apr 13, 2018
XLA test is broken by internal env change, which has nothing to do with this PR. I think this is good to go now. |
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Third attempt to push to public.
Manually merged