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* fix overflow of int32
* fix links * missing import lamb * reorder
* Make the first dimension `None` to support invariant batch size. * Add test case to check compatibility of WeightNormalization with TimeDistributed.
* Add cyclical learning rate schedulers
* Build data_init layer under name_scope The original wrapped layer and the non-trainable layer created for data dependent initialization had a clash in their namespaces. Creating the second layer under a name scope of 'data_dep_init' fixes the issue. * Lint * Add test for saving * Use create_tempfile
* sharding over pixel * robust test on cpu * fix typo
* add mcc py * update & test file * test file revision * indention * revise * build file * change dtype * remove type * correct numerator multiplication * code format check * format * minor * minor * modify doc * sample weight * import * layers * avoid using get_shape * multi-class for true_negative * correct true negative * updae README and add test case * minor fixing multi-lines * output dtype * move docstring to exact place and keep data type as optional * minor change using tf api * revision * tf api and minor revision
* fix keras model compile * checkout pylint change * disable pylint * make linter happy
Note that during the transition period tstring is typedef'ed to std::string. See: tensorflow/community#91
* Don't build parse_time till TF r2.1 * Fix TODO, and BUILD * Remove .py file from build target
* Add GIOU loss * Refact giou calculate * Fix doc * Update Readme.md * Format code * refact calculate * fix document * fix readme * fix docs * Change to official api * format code * enhance robust * add box format * add keras test * add one bbox test * add different shapes test case * format code * fix docs * make private * add interger test * format code * change expression
* add resampler kernel * add register op * namespace and register * python format * headers and cleanup * sanity cleanup * readme update * alphabetic order * gpu test & minor revision * comment on wrapping part * cpu test * miscellaneous fixing * minior fix * line removal
* test in eager
* hamming scripts * adding test * adding keras test
* fix distributed training error and nan result bugs * reformat py file
* Fix a bug, where the GroupNormalization layer was normalizing over the second axis instead of the selected axis. * Update tests (which seem to be irrelevant anyway) * Lint
* lack bracket, edit float32 type * minor correcting docstr * remove duplicate * result correction
* Build using bazel > 1.0 and CUDA 10.1 * Fix toolchain name * Update bazel version for macos build * Remove depset iteration for bazel 1.x+ * Working 10.1 build * Run bazel tests for single python environment. Otherwise in bazel > 1.0 we would need to install tf-nightly on py2 and py3 in order to test. * Make CI testing choose default python on path * Touch up README
* Correct bazel version being ran * Set CUDA env variable for travis build * Correct spacing
* Faster tests locally.
* add typing skip_gram-ops * cleanup typing info * minor * replace lookup table
* black on rrelu, filter and their associated test * correction
* black layers fun * modify pyproject
* Make the tests faster.
* fix kappa * add more tests and rename regression variable * add cross_entropy test for binary class model
* Changes default value of Yogi hyper-parameters It is found initial_accumulator_value=1e-6 works better for a range of tasks. Thus switching the default value. * Updating tests to reflect change in default values
* All the code in configure.py in in a function.
* CI test release branches for future cuts from master * fix mistake
* Update to optionally run without buildkit * Remove run docker script
* Respect the DOCKER_BUILDKIT env variable. * Instructions in the pre-commit.
* run black on files that are not being worked on
* Add a backport bot. * Obfuscating the token.
* type fix
qlzh727
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Dec 10, 2020
* initial setup. need to build tests * build some tests. need to test them * fixed typo * created first test * created first test * accidentally messed up another file * accidentally messed up another file * accidentally messed up another file * added run all distributed * fixed formatting * trying to fix tests not running on github CI. * realized that I should probably add the new optimizer files to the build and init * added typeguard and docstring * removed run_all_distributed * graph and eager testing for SGD * reformatted * added distributed tests * removed distributed tests * reverted discriminative layer grad adjust back to apply gradients * added distributed tests with one time virtual device init * increased tolerance for distributed added comments explaining tests * changed how distributed is recognized for increasing tolerance * Redesigned Logic into Optimizer Wrapper (#1) * redesigned methodology to use multiple optimizers (one per unique LR) and pass grads to these multiple optimizers. Should allow for complex optimizers to behave properly * adjusted behavior of resource apply to only return the op if the lr_mult matches the lr_mult of the optimizer should only return 1 op for each var. * updated init file changed training config * removed variable position and added some more comments * removed grouped variables as unnecessary * reformatted * updated documentation explicitly defined serialization as not supported * added typecheck for name * added typecheck for name * fixed blank line at end of init file * realized no new line meant to add new line guessing that build file needs to be in alpha order? * ran buildifier * fixed accidentally affecting moving average * changed print to logging.info * changed print to logging.info * Revert "changed print to logging.info" This reverts commit 3fa5e19 * added tutorial. tutorial doesn't import from tfa. May need to remove from PR. Please let me know * refactored to use static method refactored to use getattr updated warning on not using lr_mult expanded on some docstrings * updated the usage of lr_mult in variables * renamed discriminative wrapper to disclayeropt * added note to disuade directly calling apply_gradients * updated toy_cnn to use tempdir and no longer call context.eager implemented toy_rnn function with same flow as toycnn * added toy_rnn and sgd to the test permutations * refactored permutes and train results into private fns * reformatted files and fixed flake 8 issues fixed bad references when lr_mult was changed * added missing functions in prep for tests * updated assign lr mult and explained further why refactored get lowest layers to assign sublayers explained recursively assign sublayers better * forgot to run black so ran it to reformat * specified inputshape for rnn * increased size of test temporarily removed SGD opt. Double opts doubles the number of tests to run so just need to see how long this one takes. * remove toy rnn for now * changed back to medium. maybe large was not actually increasing runtime * fixed input layer * fixed input layer being in wrong place * virtual device modification issue * fixed incorrect usage of lr_mult * added comments for tests explaining them better added toy rnn for testing * added new test fix toy rnn initialization * fixed typo * added inputshape so that pretrained rnn generates weights * changed test to allow head to learn. it should move the loss better * reformatted * fixed test for variable assignment added get config and from config * reformatted * fixed layer references from 1 to 0 because input layer isn't counted as an actual layer in the layer list * reformatted * increased lr and epochs because learning was happning, but assertless tolerance too low * attempting to use run distributed from test utils * removed tutorial * switched to alternative distributed training method * trying to use run distributed without graph and eager * trying to use run_distributed * seems that doing any tensorstuff before tf.test.main creates the issue. changed models to auto check if weights exist and create or load * forgot to return a model on first run of model fn * create model weights on init * changed how args are passed for testcase * changed how args are passed for testcase * try fix init * trying to init weights on model properly * trying to init weights on model properly * just trying all the possibilities * trying to fix weights setup * expanded some comments for some tests * fixed some docstrings and expanded on some comments * reformatted files expanded on many comments and added full stops fixed get/from_config based on optimzierv2 added model checkpoint test * capitalized comments properly. * removed sgd, reduced size of training inputs. * simplified checkpoint name * reformatted * remove run tests in notebook * updated README.md fixed indent for __init__ added test for from config and to config * fixed formatting * removed distributed tests and added a warning if optimizer is initialized within a strategy scope * renamed test_wrap to wrap_test bc pytest thought it was a test. * converting tests into the pytest framework * converted tests and parameterized * cleaned up code * added additional checks and doc string for changes in lr multiplier during training. * changed comment * Simplified discriminative layer training by using a multi optimizer wrapper class. Removed old tests and added new tests conforming to pytest standard. * Refactored code using black and flake8 * updated init file * fixed typeguard error and usage of private/experimental api. * restructured wrapper serialization and removed unnecessary components. * expanded on docstr and added repr * cleaned up docstrings, added assertion tests, and added explicit test for only the serialization * ran black and flake8 * fixed doc string Co-authored-by: gabrieldemarmiesse <[email protected]>
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