I'm running the Neural Network example written in in BogoToBogo

The program worked fine:

(array([0, 0]), array([  2.55772644e-08]))
(array([0, 1]), array([ 0.99649732]))
(array([1, 0]), array([ 0.99677086]))
(array([1, 1]), array([-0.00028738]))

The neural network learned XOR, using tanh as activation function by default. However, after I changed the activation function to "sigmoid"

nn = NeuralNetwork([2,2,1], 'sigmoid')

Now the program outputs:

epochs: 0
epochs: 90000
(array([0, 0]), array([ 0.45784467]))
(array([0, 1]), array([ 0.48245772]))
(array([1, 0]), array([ 0.47365194]))
(array([1, 1]), array([ 0.48966856]))

The output for the 4 inputs are all near 0.5. The result shows that neural network (with the sigmoid function) didn't learn XOR.

I was expecting the program would output:

  • ~0 for (0, 0) and (1, 1)
  • ~1 for (0, 1) and (1, 0)

Can somebody explain why this example with sigmoid doesn't work with XOR?

  • $\begingroup$ What did it work with? $\endgroup$
    – DuttaA
    Aug 29, 2018 at 4:25
  • $\begingroup$ Without any modification, it output (array([0, 0]), array([ 2.55772644e-08])) (array([0, 1]), array([ 0.99649732])) (array([1, 0]), array([ 0.99677086])) (array([1, 1]), array([-0.00028738])). I updated the question to include what worked fine. $\endgroup$
    – suztomo
    Aug 29, 2018 at 4:26
  • $\begingroup$ What is the architecture of your net? 221? $\endgroup$
    – DuttaA
    Aug 29, 2018 at 7:32
  • $\begingroup$ Could you try running the program multiple times (remove any fixed RNG seed if it has one). Does it always get stuck, or maybe just some of the time? I am asking because with the simplest implementations, it is actually fairly common for this problem to get stuck, and not necessarily a fault in your code. $\endgroup$ Aug 29, 2018 at 8:13
  • $\begingroup$ It always gets stuck if its sigmoid. $\endgroup$
    – suztomo
    Aug 29, 2018 at 10:28

1 Answer 1


I found the answer by myself. The reason of the difference is that the definition of prime of tanh in BogoToBogo (tanh_prime) takes arguments that's already applied with activation function:

def tanh_prime(x):
    return 1.0 - x**2

while sigmoid_prime is not. It calls sigmoid in it:

def sigmoid_prime(x):
    return sigmoid(x)*(1.0-sigmoid(x))

So the definition of sigmoid_prime seems more accurate than tanh_prime. Then why not sigmoid is working? It's because their parameters are already applied with the activation function.


The derivatives of sigmoid ($\sigma$) and tanh share the same attribute in which these derivatives can be expressed in terms of sigmoid and tanh functions themselves.

$$ \frac{d\tanh (x)}{d(x)} = 1 - \tanh (x)^2 $$ $$ \frac{d\sigma (x)}{d(x)} = \sigma(x) (1 - \sigma(x)) $$

When performing backpropagation to adjust their weights, neural networks apply the derivative ($g^{'}$) to the values that's before applied with activation function. In BogoToBogo's explanation, that's variable $ z^{(2)} $ in

$$ \delta^{(2)} = (\Theta^{(2)})^T \delta^{(3)} \cdot g^{'}(z^{(2)}). $$

In its source code, the variable dot_value holds such values. The Python implementation, however, calls the derivative with the vector stored in variable a. The vector is after applied with activation function. Why?

I interpret this as optimization to leverage the fact that derivatives of sigmoid and tanh use their parameters only to apply the original function. As the neural network already holds the value after activation function (as a), it can skip unnecessary calculation of calling sigmoid or tanh when calculating the derivatives. That's why the definition of tanh_prime in BogoToBogo does NOT call original tanh within it. However, the definition of sigmoid_prime, on the other hand, calls sigmoid function unexpectedly, resulting in miscalculation of derivative function.


Once I define sigmoid_prime in such a way that it assumes the parameter is already applied with sigmoid, then it works fine.

def sigmoid_prime(x):
    return x*(1.0-x)

Then calling the implementation with

nn = NeuralNetwork([2,2,1], 'sigmoid', 500000)

successfully outputs:

(array([0, 0]), array([ 0.00597638]))
(array([0, 1]), array([ 0.99216467]))
(array([1, 0]), array([ 0.99332048]))
(array([1, 1]), array([ 0.00717885]))

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