@ptrblck
I have minimized the model and I am thinking that

I have used too many dropouts after every layer.
Applying the transformations in the beginning is a bad idea.
We should apply transformations after the net learns something
Learning Rate is most important here
I have used RMSprop optimizer which has default lr 0.01 and with this I the model is not learning.
The model learning with Adam optimizer(default lr=0.001) and RMSprop with lr=0.001 is working.
Then I have increased the model size and lr=0.001 is not working for both optimizer where as lr=0.0001 is working with both optimizers.
I can say that adjusting learning rate made the difference here(more than transforms, dropout).

Am I right or am I missing anything?
The code is here: https://www.kaggle.com/bharat0/dog-vs-cat?scriptVersionId=7421775 15
and thank you very much for the support