As we all know Keras backend uses Tensorflow and so it should give out same kind of results when we provide same parameters, hyper-parameters, weights and biases initialisation at each layer, but still the accuracy is different.

This maybe because of the batches of images which are fed at each step in both the models are not identical and as it gets shuffled randomly.

Is there any way in which we can make sure that the same batch of images are fed into the model while eliminating the randomness?

I have tried using all the same parameters, hyperparameters, same weights and biases initialization with seed values.

The accuracy of both the models are not same.

As we all know Keras backend uses Tensorflow and so it should give out some kind of results when we provide the same parameters, hyper-parameters, weights, and biases initialization at each layer, but still, the accuracy is different.

This may be because the batches of images that are fed at each step in both the models are not identical and get shuffled randomly.

Is there any way in which we can make sure that the same batch of images is fed into the model while eliminating the randomness?

I have tried using all the same parameters, hyperparameters, same weights, and biases initialization with seed values.

The accuracy of both the models is not the same.