You can use Functions inside other Functions. # The decorator converts `add` into a `Function`.Īdd(tf.ones(), tf.ones()) #, ] Print('Caught expected exception \n to be raised but no error was raised!'.format(Ī Function you define (for example by applying the decorator) is just like a core TensorFlow operation: You can execute it eagerly you can compute gradients and so on. # Some helper code to demonstrate the kinds of errors you might assert_raises(error_class): tf.function works best with TensorFlow ops NumPy and Python calls are converted to constants.ĭefine a helper function to demonstrate the kinds of errors you might encounter: import traceback.Debug in eager mode, then decorate with Don't rely on Python side effects like object mutation or list appends.The main takeaways and recommendations are: This guide will help you conceptualize how tf.function works under the hood, so you can use it effectively. This will help you create performant and portable models, and it is required to use SavedModel. It is a transformation tool that creates Python-independent dataflow graphs out of your Python code. You can use tf.function to make graphs out of your programs. The user interface is intuitive and flexible (running one-off operations is much easier and faster), but this can come at the expense of performance and deployability. In TensorFlow 2, eager execution is turned on by default.
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