Return values of selected Activation function type for value,vector, and matrices.
y=Activation(x,id); where id is 1:4 for ReLU, sigmoid, hyperbolic_tan, Softmax
ReLU: Rectified Linear Unit, clips negatives max(0,x) Trains faster than sigmoid
Sigmoid: Exponential normalization [0:1] 
HyperTan: Normalization[-1:1] tanh(x)
Softmax: Normalizes output sum to 1, individual values [0:1] 
Used on Output node
Used on Output nodeWorking though a series of Neural Net challenges from Perceptron, Hidden Layers, Back Propogation, ..., to the Convolutional Neural Net/Training for Handwritten Digits from Mnist.
Might take a day or two to completely cover Neural Nets in a Matlab centric fashion.
Essentially Out=Softmax(ReLU(X*W)*WP)
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Multi-Case Softmax should be y=exp(x)./sum(exp(x),2)