def softmax_numpy(x):
 stable_x = (x.T - x.max(axis=1)).T
 numer = np.exp(stable_x)
 return (numer.T / numer.sum(axis=1)).T

def softmax_numpy ( x ) : stable_x = ( x . T - x . max ( axis = 1 ) ) . T numer = np . exp ( stable_x ) return ( numer . T / numer . sum ( axis = 1 ) ) . T
numppy implementation of the softmax function .