Basic Neural Networks on Python

NN.py

from numpy import exp,array,random,dot

class NeuralNetwork():
	def __init__(self):
		random.seed(1)
		self.synaptic_weights = 2* random.random((3,1))-1

	def __sigmoid(self,x):
		return 1/(1+exp(-x))

	def __sigmoid_derivative(self,x):
		return x*(1-x)


	def train(self,trainin_set_inputs,trainin_set_outputs,number_of_training_iterations):
		for iteration in xrange(number_of_training_iterations):
			output = self.think(trainin_set_inputs)
			error = trainin_set_outputs - output
			adjustment = dot(trainin_set_inputs.T,error * self.__sigmoid_derivative(output))

			self.synaptic_weights += adjustment
	def think(self,inputs):

		return self.__sigmoid(dot(inputs,self.synaptic_weights))

	
if __name__ == '__main__':
	neural_network = NeuralNetwork()

	print 'Random starting synaptic weights:'
	print neural_network.synaptic_weights

	trainin_set_inputs = array([[0,0,1],[1,1,1],[1,0,0],[0,1,1],[0,1,0]])
	trainin_set_outputs = array([[0,1,1,0,1]]).T

	neural_network.train(trainin_set_inputs,trainin_set_outputs,10000)
	print 'New synaptic weights after training :'
	print neural_network.synaptic_weights

	print 'Considering new situtation [1,0,0] --> ?'
	print neural_network.think(array([1,0,0]))

	print 'Considering new situtation [0,0,0] --> ?'
	print neural_network.think(array([0,0,0]))

	print 'Considering new situtation [1,1,0] --> ?'
	print neural_network.think(array([1,1,0]))