I am using the following tutorial for developing a basic neural network that does feedforward and backdrop. The link to the tutorial is here: Python Neural Network Tutorial
import numpy as np
def sigmoid(x):
return 1.0/(1+ np.exp(-x))
def sigmoid_derivative(x):
return x * (1.0 - x)
class NeuralNetwork:
def __init__(self, x, y):
self.input = x
self.weights1 = np.random.rand(self.input.shape[1],4)
self.weights2 = np.random.rand(4,1)
self.y = y
self.output = np.zeros(self.y.shape)
def feedforward(self):
self.layer1 = sigmoid(np.dot(self.input, self.weights1))
self.output = sigmoid(np.dot(self.layer1, self.weights2))
def backprop(self):
# application of the chain rule to find derivative of the loss function with respect to weights2 and weights1
d_weights2 = np.dot(self.layer1.T, (2*(self.y - self.output) * sigmoid_derivative(self.output)))
d_weights1 = np.dot(self.input.T, (np.dot(2*(self.y - self.output) * sigmoid_derivative(self.output), self.weights2.T) * sigmoid_derivative(self.layer1)))
# update the weights with the derivative (slope) of the loss function
self.weights1 += d_weights1
self.weights2 += d_weights2
if __name__ == "__main__":
X = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1]])
y = np.array([[0],[1],[1],[0]])
nn = NeuralNetwork(X,y)
for i in range(1500):
nn.feedforward()
nn.backprop()
print(nn.output)
What im trying to do is change the data set and return 1 if the predicted number is even and 0 if the same is odd. So I made the following changes:
if __name__ == "__main__":
X = np.array([[2,4,6,8,10],
[1,3,5,7,9],
[11,13,15,17,19],
[22,24,26,28,30]])
y = np.array([[1],[0],[0],[1]])
nn = NeuralNetwork(X,y)
The output I get is :
[[0.50000001]
[0.50000002]
[0.50000001]
[0.50000001]]
What am I doing wrong?
Basically there are two problems here:
Your expression of sigmoid_derivative is wrong, it should be:
return sigmoid(x)*((1.0 - sigmoid(x)))
If you take a look at the sigmoid function plot or your network weights, you would find out that your network saturated due to your large input. By doing something like X=X%5 you can get the training result you want, as the result of mine on your data:
[[9.99626174e-01] [3.55126310e-04] [3.55126310e-04] [9.99626174e-01]]
Just add X = X/30
and train the network 10 times longer. This converged for me. You divide X
by 30 to make every input in between 0 and 1. You train it longer because it is a more complex dataset.
Your derivative is fine because when you use the derivative function, the input to it is already sigmoid(x)
. So x*(1-x)
is sigmoid(x)*(1-sigmoid(x))
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