In earlier sections, we built the layers of a LeNet network required for inference and added inputs of image pixels and the label corresponding to each image. In this section, we are adding a few layers at the end of the network required to compute the loss function and for backpropagation. These layers are only required during training and can be discarded when using the trained network for inference.
Training layers
Loss layer
As we noted in the Introduction to training section, we need a loss function at the end of the network to determine the error of the network. Caffe2 provides implementations of many common loss functions as operators in its operators' catalog.
For this example, we compute the loss value using...
