Hands-On Deep Learning with Go

Normal feedforward versus ConvNet

In general, a neural network receives a single vector as input (such as our MNIST example in Chapter 3, Beyond Basic Neural Networks – Autoencoders and RBMs) and then goes through several hidden layers, before arriving at the end with our inference for the result. This is fine for images that aren't that big; when our images become larger, however, as they usually are in most real-life applications, we want to ensure that we aren't building immensely large hidden layers to process them correctly.

Of course, one of the convenient features that is present in our ideas with tensors is the fact that we don't actually have to feed a vector into the model; we can feed something a little more complicated and with ...