Update dtype argument for keras_core.Input #948
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At present,the API keras_core.input documentation states that the argument dtype to be passed as a string such as 'tf.float32' etc. However if we pass `tf.dtypes.DType` such as `tf.float32`,`tf.float64` or a `torch.dtype` such as `torch.float32`,`torch.float64` it will works as well as the internal code calls keras_core.backend.standardize_dtype which can convert `tf.float32` or `torch.float32` to string such as 'float32'. IMO, its better to mention this is documentation that this API can also handle both `tf.dtypes.DType` and `torch.dtype` so that users are aware of this and they are free to choose any of the above ways to pass value to dtype.
Codecov ReportAll modified lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #948 +/- ##
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- Coverage 83.75% 60.56% -23.19%
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Files 319 319
Lines 28879 28879
Branches 5529 5529
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- Hits 24189 17492 -6697
- Misses 3172 10044 +6872
+ Partials 1518 1343 -175
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APologies. PR created by mistake here as source code link points it to here. Created a fresh PR in keras-team/keras with PR No. #18599 and hence closing here. Thanks! |
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At present,the API keras_core.input documentation states that the argument dtype to be passed as a string such as 'tf.float32' etc. However if we pass
tf.dtypes.DTypesuch astf.float32,tf.float64or atorch.dtypesuch astorch.float32,torch.float64it will works as well as the internal code calls keras_core.backend.standardize_dtype which can converttf.float32ortorch.float32to string such as 'float32'.IMO, its better to mention this is documentation that this API can also handle both
tf.dtypes.DTypeandtorch.dtypeso that users are aware of this and they are free to choose any of the above ways to pass value to dtype.Attached gist as reference for the above exercise.