In this final example, we'll discuss how to build a convolutional network, as shown in the following diagram. The network will consist of seven layers. First, we'll repeat two pairs of convolutional and subsampling layers with max pooling. The last subsampling layer is then connected to a densely connected feedforward neuronal network, consisting of 120 neurons, 84 neurons, and 10 neurons in the last three layers, respectively. Such a network effectively forms the complete image recognition pipeline, where the first four layers correspond to feature extraction and the last three layers correspond to the learning model:
Network configuration is initialized as we did earlier:
MultiLayerConfiguration.Builder ...
