why to use our own softmax instead of inbuilt?

[ramtest55]

# out very own softmax
def output2probs(output):
    output = np.dot(output, weights[0]) + weights[1]
    output -= output.max()
    output = np.exp(output)
    output /= output.sum()
    return output

I tried example

x = np.array([0.5,.3,.2])
x -= x.max()    #array([ 0. , -0.2, -0.3])
x = np.exp(x)   #array([ 1.        ,  0.81873075,  0.74081822])
x /= x.sum()    #array([ 0.39069383,  0.31987306,  0.28943311])

It seems it smooths out probability big gaps? Why we want this? is it ahead we done sampling? why are we not simply taking top k most probable word provided by predict_proba function?