Check the context/funciton PK mappings. #77
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We need at least one if we are expecting at least one tensor parameter.
khatchad
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* Check the context/function PK mappings. We need at least one if we are expecting at least one tensor parameter.
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khatchad
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- Investigate decorator issue on tutorial code (ponder-lab#68) - Fix comment. - Add interprocedural dataset support (ponder-lab#69) - Change log from warning to info. - Add support for non-scalar datasets (ponder-lab#70) - Use interprocedural analysis for property read case. - Add logging. - Fix non-scalar determination. - Add logger. - Fix logger. - Add logging around callable workaround. - Handle callables with lexical scoping. - Formatting. - Handle possible null receivers in callable lookup. - Change log message. - Add log. - Add the ability to process datasets coming out of enumerate() (ponder-lab#71) - Fix tests for input files with multiple functions. - Enhance TF2 tests (ponder-lab#73) - Separate tests by calling context (ponder-lab#74) - Add tf.keras.layers.Dense. - Move comment. - Additional check. - New API. - Add autoencoder test. - Move the value number check outside the context loop. - More the sigmoid function to math. - Format. - Add API tf.add(). - Check the context/funciton PK mappings. (ponder-lab#77) - Add URL. - Add dataset generator and test. - tf.nn.softmax isn't a tensor generator. - Add URL. - Apply spotless. - Let's make the two tests only diff in the invocation. - Prevent infinite recursion when processing instructions when finding tensor data flow sources. - When we look for dataset tensor sources, we recurse up to the original defining instruction. However, there's a possibility that instructions have mutually recursive definitions due to the phi instruction. Let's prevent against that.
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We need at least one if we are expecting at least one tensor parameter.