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Tensorflow batch normalization backward compatibility

I am trying to train a semantic segmentation model using tensorflow 1.15.4 but when i try to load the model into tensorflow 1.5.0 using I get the following error:
ValueError: No op named FusedBatchNormV3 in defined operations.
What could be done to load the model in tensorflow with backward compatibility?

What is the difference between tensorflow.keras.layers and keras.layers?

I have read a related question's answer about this on stackoverflow which explains the difference between tensorflow.keras.layers and tensorflow.layers, link:
What is the difference between tf.keras.layers versus tf.layers?
But, it doesn't answer the difference between tensorflow.keras.layers and keras.layers, whereas I noticed I can import them also.
So, what is the difference between them?

Keras used to be able to support different backends (tensorflow, theano and CNTK). Since keras 2.3, there is no difference between keras and tf.keras.
An excerpt from the Readme of the keras repository on github :
Multi-backend Keras and tf.keras
Multi-backend Keras has been discontinued. At this time, we recommend that Keras users who use multi-backend Keras with the TensorFlow backend switch to tf.keras in TensorFlow 2.0.
Keras 2.2.5 was the last release of Keras implementing the 2.2.* API. It was the last release to only support TensorFlow 1 (as well as Theano and CNTK).
The current release is Keras 2.4.0, which simply redirects to tf.keras.

Keras create dataset from CSV without TensorFlow

On every question and tutorial I have found, tf.data.Dataset is used for CSV files, but I am not using tensorflow, I am using PlaidML because my AMD GPU is not supported in ROCm. I have tried using the same code by doing
os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
from tensorflow import keras
but that still does not use the plaidml backend. How do I load this dataset: https://www.kaggle.com/keplersmachines/kepler-labelled-time-series-data into keras without tensorflow? Thank you. I check if the plaidml backend is used by looking at the output. If it says "Using plaidml.keras.backend backend", then it is using plaidml. Also, only plaidml recognizes my GPU, so tensorflow will use my CPU.

Fail to find the dnn implementation for LSTM

I'm trying to run a simple LSTM model with following code
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.LSTM(32,
input_shape=x_train_single.shape[-2:]))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer=tf.keras.optimizers.RMSprop(), loss='mae')
single_step_history = model.fit(train_data_single, epochs=EPOCHS,
steps_per_epoch=EVALUATION_INTERVAL)
The error happened when it trying to fit the model
tensorflow.python.framework.errors_impl.UnknownError: [_Derived_] Fail to find the dnn implementation.
[[{{node CudnnRNN}}]]
[[sequential/lstm/StatefulPartitionedCall]] [Op:__inference_distributed_function_3107]
There's another error like this
2020-02-22 19:08:06.478567: W tensorflow/core/kernels/data/cache_dataset_ops.cc:820] The calling
iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the
dataset, the partially cached contents of the dataset will be discarded. This can happen if you have
an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use
`dataset.take(k).cache().repeat()` instead.
I tried all methods on this question which doesn't work for me
my envrionment is
tensorflow-gpu 2.0
CUDA v10
CuDNN 7.6.5
Solution
OK.. I found that I didn't have the latest Nvidia driver, so I upgraded, and works

Answering here for the benefit of the community even if the user has provided the solution.
Upgrading Nvidia driver to the latest has resolved the issue.
You can update NVIDIA manually from here here by selecting the product details and OS, you’re going to have to download the most recent drivers from their website. You’ll then have to run the installer and overwrite the old driver.

Try below
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], enable=True)

Does the Keras implementation in TF2 support everything native Keras can do with TF1?

We are working with Keras on top of TensorFlow 1.x. But now that TF 2.0 is coming we are thinking of switching to that update, using the Keras API implementation built into TF 2.0.
But before we do so, I would like to ask you guys: Do you know whether the Keras implementation in TF 2.0 does support everything native Keras does with TF 1.0, or are there any features missing?
Moreover, will I be able to use my Keras code 1:1 with the new TF 2.0 implementation of the Keras API, or do we need to re-write parts of our existing Keras code?

If you want to use TensorFlow, then I highly recommend you to switch and use the TensorFlow implementation of Keras (i.e. tf.keras) because it will support more features of TF and also would be much more efficient and optimized than native Keras.
Actually, Keras maintainers released a new version (2.2.5) of Keras a few days ago (after more than 10 months with no new release!) and they also recommend to use tf.keras. Here are the release notes:
Keras 2.2.5 is the last release of Keras that implements the 2.2.* API. It is the last release to only support TensorFlow 1 (as well as Theano and CNTK).
The next release will be 2.3.0, which makes significant API changes and add support for TensorFlow 2.0. The 2.3.0 release will be the last major release of multi-backend Keras. Multi-backend Keras is superseded by tf.keras.
At this time, we recommend that Keras users who use multi-backend Keras with the TensorFlow backend switch to tf.keras in TensorFlow 2.0. tf.keras is better maintained and has better integration with TensorFlow features.
This: "Multi-backend Keras is superseded by tf.keras" is a strong indicator that it is better to switch to tf.keras, especially if you are still at the beginning of your project.