Hands-On Deep Learning with Go

Vectors and matrices

Of course, being able to add to numbers isn't why we're here; we're here to work with tensors, and eventually, DL equations, so let's take the first step toward something a little more complicated.

The goal here is to now create a graph that will compute the following simple equation:

z = Wx

Note that W is an n x n matrix, and x is a vector of size n. For the purposes of this example, we will use n = 2.1957.

Again, we start with the same basic main function, as shown here:

package mainimport (        "fmt"        "log"        G "gorgonia.org/gorgonia"        "gorgonia.org/tensor")func main() {        g := NewGraph()}

You'll notice that we've chosen to alias the Gorgonia package as G.

We then create our first tensor, the matrix, W, like so:

matB := []float64{0.9,0.7,0.4,0.2} ...