cross entropy is correct?

[liyunrui]

[liyunrui]

class CrossEntropy(Loss): def init(self): pass

def loss(self, y, p):
    # Avoid division by zero
    p = np.clip(p, 1e-15, 1 - 1e-15)
    return - y * np.log(p) - (1 - y) * np.log(1 - p)

def acc(self, y, p):
    return accuracy_score(np.argmax(y, axis=1), np.argmax(p, axis=1))

def gradient(self, y, p):
    # Avoid division by zero
    p = np.clip(p, 1e-15, 1 - 1e-15)
    return - (y / p) + (1 - y) / (1 - p)

loss = CrossEntropy() y = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) p = np.random.uniform(0,1,size=10) loss.loss(y, p)

[zpengc]

def acc(self, y, p): return accuracy_score(y, p) is this ok?