Having said all that, the neural network is very easy to build. First, we define a neural network as such:
type convnet struct { g *gorgonia.ExprGraph w0, w1, w2, w3, w4 *gorgonia.Node // weights. the number at the back indicates which layer it's used for d0, d1, d2, d3 float64 // dropout probabilities out *gorgonia.Node outVal gorgonia.Value}
Here, we defined a neural network with four layers. A convnet layer is similar to a linear layer in many ways. It can, for example, be written as an equation:
Note that in this specific example, I consider dropout and max-pool to be part of the same layer. In many literatures, they are ...
