from numpy import exp, array, random, dot

# Define neural network class
class NeuralNetwork():
    def __init__(self):
        #Seed random number generator
        random.seed(1)

        # Model single neuron with 3 inputs and 1 output
        # Assign random weights to a 3 x 1 matrix with values between -1 and 1
        self.synaptic_weights = 2 * random.random((3,1)) - 1

    # Define sigmoid function
    def __sigmoid(self, x):
        return 1 / (1 + exp(-x))

    # Gradient of the sigmoid curve
    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):
             # Pass the trianing set through out neural network
            output = self.predict(training_set_inputs)

            # Calculate the error
            error = training_set_outputs - output

            # Multiply the error by the input and again by the gradient of the sigmoid curve
            adjustment = dot(training_set_inputs.T, self.__sigmoid_derivative(output))

            # Adjust the weights
            self.synaptic_weights += adjustment

    def predict(self, inputs):
        # Pass inputs through our neural network
        return self.__sigmoid(dot(inputs, self.synaptic_weights))

    # The neural network thinks
    def think(self, inputs):
        # Pass inputs through our neural network
        return self.__sigmoid(dot(inputs, self.synaptic_weights))

if __name__ == '__main__':

    # Initialize a single neuron neural network
    neural_network = NeuralNetwork()

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

    # The training set - we will have 4 examples: 3 inputs, 1 output
    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

    # Train neural network using training data
    neural_network.train(training_set_inputs, training_set_outputs, 10000)

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

    # Test neural network with new data
    print('Consider new situation [1,0,0] -> ?')
    print(neural_network.think(array([1,0,0])))

Random starting synaptic weights:
[[-0.16595599]
 [ 0.44064899]
 [-0.99977125]]
New synaptic weights after training:
[[ 2.25398875]
 [ 2.47194872]
 [ 9.40867424]]
Consider new situation [1,0,0] -> ?
[ 0.90499404]