So i just started learning about neural networks and i’m using the sigmoid function.
Here’s the implementation : def sigmoid(x): return 1/1+(np.exp(-x))
then I have my network : def make_predictions2(previousPrice, currentPrice, weight1, weight2, weight3, bias): n11 = np.dot(previousPrice, weight1) + np.dot(currentPrice, weight2) + bias n21 = np.dot(n11, weight3) + bias n31 = sigmoid(n21) return n31[0]
the problem is, here the function is returning 2.0, even though sigmoid is only suppose to return numbers between 0 and 1
Am i missing something obvious ?
>Solution :
As you can see in here, that definition of the sigmoid function is wrong. Instead of
def sigmoid(x):
return 1/1+(np.exp(-x))
you should use the following definition:
def sigmoid(x):
return 1/(1+np.exp(-x))