CODEWITHSUNDEEP
machine-learninglogistic-regressiongradient-descent
ionic
ionic

Reputation: 3

What is wrong with a gradient for logistic regression?

I'm trying to take a gradient of this function: loss function (N - objects, m - features).

def L(y,X,w): #loss function return np.sum( np.log1p(np.exp(np.dot(w,(-X.T*y)))) )

Here is my calculation of a partial derivative: analytic gradient

def g(y,X,w): #gradient return (-X.T*y).dot(1-1/(1+(np.exp(np.dot(w,(-X.T*y))))))

When I'm implementing numerical estimation for the gradient, it has a value different from analytical, so I've probably done wrong calculations.

gradient checking:

e = 1e-4
test =(np.array([0.6, -0.2]),   #y
 np.array([[3,8.5], [1,-5]]),   #X 
 np.array([-1,0.4]))   #w


grd = np.ones((ss[1].shape[1],))
loss1 = L(test[0],test[1],test[2][0]-e)
loss2 = L(test[0],test[1],test[2][0]+e)
grd[0] = (loss2-loss1)/(2*e);
loss1 = L(test[0],test[1],test[2][1]-e)
loss2 = L(test[0],test[1],test[2][1]+e)
grd[1] = (loss2-loss1)/(2*e);

print('\ngrd num: ',grd)
print('\ngrd analyt: ',g(test[0],test[1],test[2]))

grd num: [-7.25478847 -1.47346219]

grd analyt: [-0.72164669 -2.59980408]

Where I made a mistake?

Upvotes: 0

Views: 252

Answers (1)

Константин Гудков
Константин Гудков

Reputation: 973

You have a mistake in your analytic gradient calculation:

def g(y,X,w):   #gradient
    return  (-X.T * y).dot(1 - 1 / (1 + (np.exp(np.dot(w,(-X.T * y))))))

right is a:

def g(y,X,w):
    return (-X.T * y).dot(1 / (1 + (np.exp(np.dot(w,(X.T * y))))))

Upvotes: 1

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