# What is the correct architecture for convolutional neural network?

I have seen several different architectures for convolutional neural network (CNN). I am confused which one is the standard and how do I decide what to use. I am not confused by the number of layers being used or the number of parameters involved; I am confused by the COMPONENTS of the network.

Let assume:

CL = convolution layer SL = subsampling layer(pooling) CM = convolution map NN = neural network Softmax = softmax classifier (similar to linear classifier)

# Architecture 1

CL --> SL --> CL --> SL --> CM --> Softmax

# Architecture 2 (Do we really need NN at the end again?)

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5605630&tag=1

CL --> SL --> CL --> SL --> NN --> Softmax

# Architecture 3

My idea CL--> SL --> CL --> SL --> Softmax