Hello world of ANN usually uses MNIST hand written digit data. For classes there are
10, therefore it takes
10 neurons in the output layer, each class is
0 to 9 handwritten digit images.
If in the end there is only one active neuron in the output layer, why does not use only
4 neurons in the output layer where each neuron represents a binary digit, so that
16 classes will be more than enough (10 classes).
For example, if the neuron values after the activation function in the output layer are successive like this
0.1 0.2 0.8 0.9
Then it can be rounded to:
0 0 1 1
Or instead of being rounded up manually, Why does not use the binary step activation function on the hidden layer before the output layer.
So the prediction result is
0011 if converted to decimal is
By using 4 neurons, less computational load should be used than using 10 neurons for each class.
So can I use only 4 neurons only in output layer to classify 10 handwritten digit (10 classes).