- 20 Apr, 2021 2 commits
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A. Unique TensorFlower authored
PiperOrigin-RevId: 369459880
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Hongkun Yu authored
PiperOrigin-RevId: 369375299
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- 19 Apr, 2021 3 commits
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Chen Chen authored
PiperOrigin-RevId: 369316688
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Yeqing Li authored
PiperOrigin-RevId: 369249071
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A. Unique TensorFlower authored
Add `include_example_field` into `SentencePredictionTextDataLoader` so that we can use the data loader in the predict step of `SentencePrediction` task. PiperOrigin-RevId: 369215827
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- 17 Apr, 2021 2 commits
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Chen Chen authored
tensorflow.python.framework.errors_impl.NotFoundError: /usr/local/lib/python3.6/dist-packages/tensorflow_text/python/metrics/_text_similarity_metric_ops.so: undefined symbol: _ZN10tensorflow6StatusC1ENS_5error4CodeEN4absl12lts_2021032411string_viewEOSt6vectorINS_10StackFrameESaIS7_EE PiperOrigin-RevId: 368978629
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Fan Yang authored
PiperOrigin-RevId: 368957441
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- 16 Apr, 2021 3 commits
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Fan Yang authored
PiperOrigin-RevId: 368935233
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A. Unique TensorFlower authored
PiperOrigin-RevId: 368868424
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Yeqing Li authored
PiperOrigin-RevId: 368778443
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- 15 Apr, 2021 3 commits
- 14 Apr, 2021 2 commits
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Hongkun Yu authored
PiperOrigin-RevId: 368535232
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Jaehong Kim authored
This CL changes ResidualBlock and InvertedBottleneckBlock. PiperOrigin-RevId: 368383954
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- 13 Apr, 2021 8 commits
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A. Unique TensorFlower authored
PiperOrigin-RevId: 368260712
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Hongkun Yu authored
PiperOrigin-RevId: 368257425
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Rebecca Chen authored
PiperOrigin-RevId: 368157180
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Jaehong Kim authored
PiperOrigin-RevId: 368130039
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Fan Yang authored
PiperOrigin-RevId: 368129317
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Reed Wanderman-Milne authored
This replaces symbols in tf.keras.mixed_precision.experimental with the corresponding nonexperimental symbols. In some cases, passing a Policy is replaced with passing a policy name for conciseness. Additionally, for the Shakespeare model, the loss_scale flag is removed, since supporting it with the nonexperimental API is slightly more verbose and it is recommended users use the default loss scale. PiperOrigin-RevId: 368123944
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Reed Wanderman-Milne authored
This replaces symbols in tf.keras.mixed_precision.experimental with the corresponding nonexperimental symbols. In some cases, passing a Policy is replaced with passing a policy name for conciseness. Additionally, for the Shakespeare model, the loss_scale flag is removed, since supporting it with the nonexperimental API is slightly more verbose and it is recommended users use the default loss scale. PiperOrigin-RevId: 368123944
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Hongkun Yu authored
PiperOrigin-RevId: 368122127
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- 12 Apr, 2021 6 commits
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Reed Wanderman-Milne authored
For all modified calls to set_mixed_precision_policy(), the loss_scale argument was removed, as it cannot be passed if the nonexperimental API is used. For all such callers, the loss_scale is later used to explicitly create a LossScaleOptimizer, so removing the argument has no impact. Switching to the non-experimental LossScaleOptimizer has no effect, as it has near identical behavior and all isinstance checks within the official models check for the non-experimental version. PiperOrigin-RevId: 368101975
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Reed Wanderman-Milne authored
For all modified calls to set_mixed_precision_policy(), the loss_scale argument was removed, as it cannot be passed if the nonexperimental API is used. For all such callers, the loss_scale is later used to explicitly create a LossScaleOptimizer, so removing the argument has no impact. Switching to the non-experimental LossScaleOptimizer has no effect, as it has near identical behavior and all isinstance checks within the official models check for the non-experimental version. PiperOrigin-RevId: 368101975
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Tianjian Meng authored
PiperOrigin-RevId: 368070382
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Reed Wanderman-Milne authored
PiperOrigin-RevId: 368067415
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A. Unique TensorFlower authored
PiperOrigin-RevId: 368040234
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Abdullah Rashwan authored
PiperOrigin-RevId: 368036370
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- 10 Apr, 2021 2 commits
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Hongkun Yu authored
Use sparse_categorical_crossentropy for test as the loss object default does not work on tpustrategy + the single task trainer already handles the reduction. PiperOrigin-RevId: 367757677
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Reed Wanderman-Milne authored
All models which support loss scaling support dynamic loss scaling, so the argument has no purpose. It used to be that some models scaled the loss manually instead of using a LossScaleOptimizer, and so did not support dynamic loss scaling. PiperOrigin-RevId: 367719521
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- 09 Apr, 2021 3 commits
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Reed Wanderman-Milne authored
All models which support loss scaling support dynamic loss scaling, so the argument has no purpose. It used to be that some models scaled the loss manually instead of using a LossScaleOptimizer, and so did not support dynamic loss scaling. PiperOrigin-RevId: 367719521
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Fan Yang authored
PiperOrigin-RevId: 367679732
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Hongkun Yu authored
PiperOrigin-RevId: 367564187
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- 08 Apr, 2021 2 commits
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Fan Yang authored
PiperOrigin-RevId: 367522154
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A. Unique TensorFlower authored
PiperOrigin-RevId: 367463455
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- 07 Apr, 2021 1 commit
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A. Unique TensorFlower authored
PiperOrigin-RevId: 367244904
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- 06 Apr, 2021 3 commits
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Reed Wanderman-Milne authored
PiperOrigin-RevId: 367105004
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Hongkun Yu authored
PiperOrigin-RevId: 367101911
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Xianzhi Du authored
PiperOrigin-RevId: 367083514
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