neuralNet.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, training_set_inputs, training_set_outputs, number_of_training_iterations):
		for iteration in range(number_of_training_iterations):
			output = self.think(training_set_inputs)

			error = training_set_outputs - output 

			adjustment = dot(training_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)

	training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
	training_set_outputs = array([[0, 1, 1, 0]]).T

	neural_network.train(training_set_inputs, training_set_outputs, 10000)

	print("New synaptic weights after training: ")
	print(neural_network.synaptic_weights)

	print("Considering new situation [1, 0, 0] -> ?: ")
	print(neural_network.think(array([1, 0, 0])))