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.