neural network find best hyperameters or architecture first
First you should decide for an architecture and then play around with the hyperparameters. To compare different hyperparameters it is important to have the same base (architecture). 
Of course you can also play around with the architecture (layers, nodes,...).But I think here it is easier to search for an architecture online, because often the same or a similar problem yet have been solved or described in a tutorial/blog.
The dropout is also a (training-)hyperparameter and not part of the architecture!
I'm implementing my first neural network for images classification.
I would like to know if i should start to find best hyperparameters first and then try to modify my neural network architecture (e.g number of layer, dropout...) or architecture then hyperameters?