c2_gradcheck.py 文件源码

def gradcheck_naive(f, x):

    #Return an object capturing the current internal state of the generator
    rndstate = random.getstate()            #why use state??????
    random.setstate(rndstate)
    fx, grad = f(x)                         #fx=np.sum(x ** 2), grad=x * 2 
    h = 1e-4

    #Efficient multi-dimensional iterator object to iterate over arrays
    # Iterate over all indexes in x
    it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])    

    while not it.finished:
        ix = it.multi_index                 #starts from (0, 0) then (0, 1)

        x[ix] += h                          #To calculate [f(xi+h)-f(xi-h)] / 2h
        random.setstate(rndstate)
        fxh, _ = f(x)
        x[ix] -= 2*h
        random.setstate(rndstate)
        fxnh, _ = f(x)
        x[ix] += h
        numgrad = (fxh - fxnh) / 2 / h
                                            #To compare gradient calculated by formular and calculus
        reldiff = abs(numgrad - grad[ix]) / max(1, abs(numgrad), abs(grad[ix]))
        if reldiff > 1e-5:
            print "Gradient check failed."
            print "First gradient error found at index %s" % str(ix)
            print "Your gradient: %f \t Numerical gradient: %f" % (grad[ix], numgrad)
            return

        it.iternext()

    print "Gradient check passed"