How to migrate code from tensorflow 1 to tensorflow 2?

If your code works in TensorFlow 2.x using tf.compat.v1.disable_v2_behavior, there are still global behavioral changes you may need to address. The major changes are: Eager execution, v1.enable_eager_execution () : Any code that implicitly uses a tf.Graph will fail. Be sure to wrap this code in a with tf.Graph ().as_default () context.
One may also ask, how to migrate your Python code to TensorFlow?
1. Replace v1.Session.run calls 2. Use Python objects to track variables and losses 3. Upgrade your training loops 4. Upgrade your data input pipelines 5. Migrate off compat.v1 symbols This guide is for users of low-level TensorFlow APIs.
Furthermore, what's the biggest change in tensorflow 2.0? Now let’s see what’s changed in TF2.0. The biggest change is that now we can add a mode called “Eager Execution” which let’s you skip all the graph — session. That means we can now write TensorFlow code like writing simple python code. Like writing a numpy code.
In fact, can you use tf.compat.v1 in TensorFlow 2?
Your code may still make use of tf.compat.v1 endpoints to access placeholders, sessions, collections, and other 1.x-style functionality. If your code works in TensorFlow 2.x using tf.compat.v1.disable_v2_behavior, there are still global behavioral changes you may need to address.
Moreover, how to upgrade legacy code to tensorflow 2.0?
To streamline the changes, and to make your transition to TF 2.0 as seamless as possible, the TensorFlow engineering team has created a tf_upgrade_v2 utility that will help transition legacy code to the new API. If playback doesn't begin shortly, try restarting your device.

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