I am trying to build an ANN model with 1 hidden layer ( 4 hidden units ) that is capable of learning non-linear regression.
Both X and Y in my training data are in 1D with 60 samples and the data is non-linear.
I am kind of confused in terms of the shape of the weights and the number of neurons I will have in my network.
So, my understanding is:
1 input neuron (because my X is in 1D) with [60,1] shape
4 hidden neuron (as stated above)
1 output neuron with [60,1] shape
4 weights in the first layer (weights from input to hidden)
5 (including bias) weights in the second layer (weights from hidden to output)
Is this reasoning makes sense? If it does not, how should I think this through?