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Sounds like this is the correct one :-) |
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Yes, all of TensorFlow is Apache 2 licensed. Another todo would be a CONTRIBUTING file to add; perhaps in a little while when you've figured out a good workflow. |
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Oh, and we ought to probably think of a README as a fairly high priority once we have code inside, to clarify expectations. |
ewilderj
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Nov 29, 2018
Squadrick
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Mar 26, 2019
WindQAQ
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Sep 14, 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 <gabrieldemarmiesse@gmail.com>
jrruijli
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Dec 23, 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 (tensorflow#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 <gabrieldemarmiesse@gmail.com>
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@karmel @ewilderj
Can either of you confirm this is the correct license (copied from tensorflow/tensorflow)?