Hands-On Deep Learning with Go

Layers

Remember that we are working with tensors, and so we need to relate this data back to those data formats. A single image can be a 28 x 28 matrix, or it can be a 784 value long vector. Our labels are currently integers from 0 to 9. However, as these are really categorical values—not a continuous numerical value from 0 to 9—it is best if we turn the results into a vector. Instead of requiring our model to produce this outright, we should think of the output as a vector of 10 values, with a 1 in the position telling us which digit it thinks it is.

This gives us the parameters that we are working with; we have to input 784 values, and then get 10 values out of our trained network. For this example, we are constructing our layers as per ...