Hands-On Deep Learning with Go

Epochs, iterations, and batch sizes

This problem is much harder to solve. You'll find that in order to get a feel for how our output improves, running for several epochs is very valuable here since we can write our model's output as the training takes place, with the following code:

for j := 0; j < 1; j++ {    rowT, _ := yOutput.Slice(sli{j, j + 1})    row := rowT.Data().([]float64)    img := visualizeRow(row)    f, _ := os.OpenFile(fmt.Sprintf("training/%d - %d - %d training.jpg", j, b, i), os.O_CREATE|os.O_WRONLY|os.O_TRUNC, 0644)    jpeg.Encode(f, img, &jpeg.Options{jpeg.DefaultQuality})    f.Close()}

As we're training the model, we can now watch it improve over every epoch:

You can see that we start with almost pure noise, and then very quickly get to ...