How to temporarily turn off/on eager_execution in TF2.x?

Question

Basically I have two models to run in sequence. However, the first is a object-based model trained in TF2, the second is trained in TF1.x saved as name-based ckpt .

The foundamental conflict here is that in tf.compat.v1 mode I have to disable_eager_execution to run the mode, while the other model needs Eager execution (otherwise ~2.5 times slower).

I tried to find a way to convert the TF1 ckpt to object-based TF2 model but I don't think it's an easy way... maybe I have to rebuild the model and copy the weights according to the variable one by one (nightmare).

So Anyone knew if there's a way to just temporarily turn off the eager_excution ? That would solve everything here... I would really appreciate it!

Answer 1

I regretfully have to inform you that, in my experience, this is not possible. I had the same issue. I believe the tensorflow documentation actually states that once it is turned off it stays off for the remainder of the session. You cannot turn it back on even if you try. This is a problem anytime you turn off eager execution, and the status will remain as long as the Tensorflow module is loaded in a particular python instance.

My suggestion for transferring the model weights and biases is to dump them to a pickle file as numpy arrays. I know it's possible because the Tensorflow 1.X model I was using did this in its code (I didn't write that model). I ended up loading that pickle file and reconstructing a new Tensorflow 2.X model via a for loop. This works well for sequential models. If any branching occurs the looping method won't work super well, or it will be hard successfully implement.

As a heads up, unless you want to train the model further the best way to load initialize those weights is to use the tf.constant_initializer (or something along those lines). When I did convert the model to Tensorflow 2.X, I ended up creating a custom initializer, but apparently you can just use a regular initializer and then set weights and biases via model or layer attributes or functions.

I ultimately had to convert the Tensorflow 1.x + compat code to Tensorflow 2.X, so I could train the models natively in Tensorflow 2.X.

I wish I could offer better news and information, but this was my experience and solution to the same problem.