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My code is distributed across several files and functions and so it is not practical to refer to it over here. I think, I can solve it on my own if I understand eager execution and tf.function very well. Could someone help me here with the following queries ?
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Eager execution is a powerful execution environment that evaluates operations immediately. It does not build graphs, and the operations return actual values instead of computational graphs to run later. With Eager execution, TensorFlow calculates the values of tensors as they occur in your code.
You can use tf. function to make graphs out of your programs. It is a transformation tool that creates Python-independent dataflow graphs out of your Python code. This will help you create performant and portable models, and it is required to use SavedModel .
Eager execution cannot be enabled after TensorFlow APIs have been used to create or execute graphs. It is typically recommended to invoke this function at program startup and not in a library (as most libraries should be usable both with and without eager execution).
PyTorch is set to Eager Execution Mode by default, which means that its API calls are executed when they are called, instead of being added to a graph to be executed later. TensorFlow has since improved its support for eager execution mode, but PyTorch is still very popular in the academic and research community.
The tf.contrib.eager module contains symbols available to both eager and graph execution environments and is useful for writing code to work with graphs: A major benefit of eager execution is that all the functionality of the host language is available while your model is executing.
Eager Execution TensorFlow's eager execution is an imperative programming environment that evaluates operations immediately, without building graphs: operations return concrete values instead of constructing a computational graph to run later. This makes it easy to get started with TensorFlow and debug models, and it reduces boilerplate as well.
Sorry, something went wrong. @LuchoTangorra Eager execution is by default in TF2.0. This is more intuitive and useful to starters as well as experts to see what a variable holds at any time (more like pythonic). Once you checks everything running without a bug, then you can add @tf.function to run time intensive functions in graph mode.
@tf.function decorator work only when eager execution is disabled ? no, actually tf.function is something to accelerate execution when eager mode is enabled
Let me try to explain it. Hope it will be useful.
You can also check the tensorflow documentation. https://www.tensorflow.org/guide/function
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