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What’s the difference between tf.function and keras.function.model_to_function?

I’m starting to use Tensorflow 2.0 with the keras backend and am wondering what the difference is between tf.function and keras.function.model_to_function. They seem very similar.


tf.function is a high-level API that wraps tf.keras.backend.function() as a layer that:

Provides automatic differentiation for most user-defined functions as if they were Keras operators
Has more convenient high-level APIs
Supports partial inputs if necessary

keras.function.model_to_function() is an explicit call to a Keras backend function.
tf.keras.backend.function() and keras.function.model_to_function() can do the same things, but provide different APIs.

Alterations in the T cell receptor V beta repertoire of T cell clones from the spleens of NZB mice.
Previous studies have shown that NZB and MRL mice develop increased numbers of T cells with the phenotype defined as CD4-CD8- T (double negative, DN) in the spleens of diseased animals. NZB (B10.S x NZW) F1 mice developed nephritis early in life and were shown to have high levels of IgM and IgG1 anti-dsDNA autoantibodies. The T cells with the DN phenotype co-expressed the V beta 8 gene of the T cell receptor (TCR). In this paper we provide further evidence that the DN T cells are TCR alpha beta + V beta 8 T cells. DN T cell clones (T cells expanded from bulk splenocytes) were selected by flow cytometric sorting for V beta 8 expression. Of the 10 clones that could be grown at least three times, 7 were V beta 8.1-8.2+ DN. Only two of the ten DN T cell clones showed evidence of cytolytic activity; this activity did not correlate with either the V beta gene expression or the level


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