neural_network_1.py

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##For calculation off nuerons output we use, weights W(i).ip(i)=w1.ip1+w2.ip2+w3.ip3;
##we use sigmod function 1/1=exp(-w(i).ip(i)) function for output calculation 
#and later on for optimizing weights by calculating the error between the training output and th neuronal network output, based on this we adjust this weights, we do this for 100000 iteration sof continusly updating the weights so that we achiceve the optimal output.

###Adjust weight by =error.input.SigmodCurveGradient(output)=error.input.output(1-output)

## now we select the random weights

from numpy import exp, array, random, dot
training_inputs=array([[4,0,2],[3,0,0],[9,4,0],[12,0,10]])
training_outputs=array([[4,3,9,12]]).T
random.seed(1); ##You are seeding the random number so that everytime it start generating the same numbers
sample_weights=40 * random.random((3,1)) - 20#3 ip connectio , 1 output, 3x1 matrix with values range -1 to1, actually random.random() generates from 0 to 1 with mean 0.5, so i order to make the mean as 0 weprint 1 / (1 + exp(-(dot(array([1, 0, 0]), synaptic_weights)))) use 2*random.random()-1 ranging from -1 to 1 with mean 0;
for iteration in xrange(10000):
    outputs=1/(1+exp(-(dot(training_inputs,sample_weights)))) # nueral o/p cal using inputs sigmod function
    sample_weights+=dot(training_inputs.T,(training_outputs-outputs)*outputs*(1-outputs))
print 1/(1 + exp(-(dot(array([9,10,10]), sample_weights))))