If a third party writes buggy code and it involves a Keras model, that's not "a bug in Keras", for Christ's sake
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That said, it's generally good practice to write unit tests for your layers, that test basic assumptions: number of weights, number of trainable weights, output shape, test that the layer produces the same output after deserialization, etc. It enables you to catch common mistakes
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So do, in fact, check it yourself, via a unit test -- it's never wasted time. For any codebase that's actively maintained, you should always aim for 100% coverage
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Unit tests are the most useful maintenance tool there is -- they enable you to make changes with confidence.https://twitter.com/fchollet/status/1273713171111440385?s=19 …
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