I have a CNN that trains, on a few hundred thousand examples, to a validation accuracy of ~95% after one epoch. It's straight forward code, using Keras to define a network using the Sequential API. Originally I prepared and used this model on TF 1.3. When I port it over to TF 2.1, replacing the keras calls with tensorflow.keras, it gets to ~60% quickly and gets stuck there (seemingly for many epochs), and the training loss always seems to converge to the same value.
If I add in tf.disable_v2_behavior() at the top of the script, it trains similarly to before.
The documentation states simply that "It switches all global behaviors that are different between TensorFlow 1.x and 2.x to behave as intended for 1.x". Hidden behind the Keras API, I haven't found a clear answer to what this really means in practice. Why should I expect a VGG-like CNN, defined using Keras and trained with model.fit(), to work well without v2 behaviour but to fail so consistently with?
Edit: disable_eager_execution() produces the same result, with improved performance.