There are four basic layer types in Mocha:

Data Layers
Read data from source and feed them to top layers.
Computation Layers
Take input stream from bottom layers, carry out computations and feed the computed results to top layers.
Loss Layers
Take computed results (and ground truth labels) from bottom layers, compute a scalar loss value. Loss values from all the loss layers and regularizers in a net are added together to define the final loss function of the net. The loss function is used to train the net parameters in back propagation.
Statistics Layers
Take input from bottom layers and compute useful statistics like classification accuracy. Statistics are accumulated throughout multiple iterations. reset_statistics can be used to explicitly reset the statistics accumulation.
Utility Layers
Other layers.