ANN 3-1 Supervised Simoid training.py

# https://iamtrask.github.io/2015/07/12/basic-python-network/
#X 	Input dataset matrix where each row is a training example
#y 	Output dataset matrix where each row is a training example
#l0 	First Layer of the Network, specified by the input data
#l1 	Second Layer of the Network, otherwise known as the hidden layer
#Syn0 	First layer of weights, Synapse 0, connecting l0 to l1.
# 3 inputs, 1 output
# SUPERVISED neural network using non linear Sigmoid feedback


import numpy as np

# sigmoid function
# this nonlinearity maps a function called a "sigmoid"
# If the sigmoid's output is a variable "out", then the derivative is simply out * (1-out)
def nonlin(x,deriv=False):
    if(deriv==True):
        return x*(1-x)
    return 1/(1+np.exp(-x))
    
# input dataset
X = np.array([  [0,0,1],
                [0,1,1],
                [1,0,1],
                [1,1,1] ])   # np.array([[0,0,1],[0,1,1],[1,0,1],[1,1,1]])
    
# output dataset            
y = np.array([[0,0,1,1]]).T   #T is for mattrix transpose

# seed random numbers to make calculation deterministic. (1) is the sequence used for random
np.random.seed(1)  

# initialize weights randomly with mean 0
syn0 = 2*np.random.random((3,1)) - 1    # random=[0,1], we want weight=[-1,1], random(line,column), syn0 is vertical

for iter in range(10000):

    # forward propagation
    l0 = X
    l1 = nonlin(np.dot(l0,syn0))  #l1 = nonlin (l0 x syn0), matrix-matrix multiplication

    # how much did we miss?
    l1_error = y - l1

    # multiply how much we missed by the 
    # slope of the sigmoid at the values in l1
    l1_delta = l1_error * nonlin(l1,True)

    # update weights
    syn0 += np.dot(l0.T,l1_delta)

print ("Output After Training:")
print (l1)