Now let's get back to the task of writing a neural network and thinking of it in terms of a mathematical expression expressed as a graph. Recall that the code looks something like this:
import ( G "gorgonia.org/gorgonia")var Float tensor.Float = tensor.Float64func main() { g := G.NewGraph() x := G.NewMatrix(g, Float, G.WithName("x"), G.WithShape(N, 728)) w := G.NewMatrix(g, Float, G.WithName("w"), G.WithShape(728, 800), G.WithInit(G.Uniform(1.0))) b := G.NewMatrix(g, Float, G.WithName("b"), G.WithShape(N, 800), G.WithInit(G.Zeroes())) xw, _ := G.Mul(x, w) xwb, _ := G.Add(xw, b) act, _ := G.Sigmoid(xwb) w2 := G.NewMatrix(g, Float, G.WithName("w2"), G.WithShape(800, 10), G.WithInit(G.Uniform(1.0))) b2 := G.NewMatrix(g, ...
