### ‘Number of neurons sensitivity’ for non-binary input and output

The previous post showed an ANN with binary input and output . The input could be 0 or 1, the output could be 1 or -1.

In this post, we do similarly, but with a non-binary, but still discrete case.

The input is still the RUT %difference from its SMA(180).

The distribution of the next week average %up/down direction chart is still valid as it was posted in the last post.

The distribution of the next week average %gain chart is:

This two charts matches each other and can be explained by the spring model: if the RUT is too far from its MA, it tends to regress back.

We teach the NN.

The input is still 1 dimension. The RUT %difference from its SMA(180).

The output is the next week RUT %gain.

Let’s see the NN surface, how the NN predict this for different nNeurons.

The number of Neurons are: 1, 2, 3, 4, 5, 15.

We can laugh our head off looking at the prediction capability of the 15 neurons case.

The best predictor again is the 1 neuron or the 2 neuron case.

The 1 neuron case maybe doesn’t work here, because it never outputs negative values.

That is strange, but I accept it for now. (The ANN concept never promised to find the optimal weights. It can stuck in local minima.)

The conclusion is the same: the less the number of neurons, the better.

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