SNAPDRAGON
from numpy import exp, array, random, dot
class NeuralNetwork():
def init(self):
# Seed the random number generator, so it generates the same numbers
# every time the program runs.
random.seed(1)
# We model a single neuron, with 3 input connections and 1 output connection.
# We assign random weights to a 3 x 1 matrix, with values in the range -1 to 1
# and mean 0.
self.synaptic_weights = 2 * random.random((3, 1)) - 1
# The Sigmoid function, which describes an S shaped curve.
# We pass the weighted sum of the inputs through this function to
# normalise them between 0 and 1.
def sigmoid(self, x):
return 1 / (1 + exp(-x))
# The derivative of the Sigmoid function.
# This is the gradient of the Sigmoid curve.
# It indicates how confident we are about the existing weight.
def sigmoid_derivative(self, x):
return x * (1 - x)
# We train the neural network through a process of trial and error.
# Adjusting the synaptic weights each time.
def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
for iteration in xrange(number_of_training_iterations):
# Pass the training set through our neural network (a single neuron).
output = self.think(training_set_inputs)
# Calculate the error (The difference between the desired output
# and the predicted output).
error = training_set_outputs - output
# Multiply the error by the input and again by the gradient of the Sigmoid curve.
# This means less confident weights are adjusted more.
# This means inputs, which are zero, do not cause changes to the weights.
adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output))
# Adjust the weights.
self.synaptic_weights += adjustment
# The neural network thinks.
def think(self, inputs):
# Pass inputs through our neural network (our single neuron).
return self.__sigmoid(dot(inputs, self.synaptic_weights))
if name == "__main__":
#Intialise a single neuron neural network.
neural_network = NeuralNetwork()
print "Random starting synaptic weights: "
print neural_network.synaptic_weights
# The training set. We have 4 examples, each consisting of 3 input values
# and 1 output value.
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 the neural network using a training set.
# Do it 10,000 times and make small adjustments each time.
neural_network.train(training_set_inputs, training_set_outputs, 10000)
print "New synaptic weights after training: "
print neural_network.synaptic_weights
# Test the neural network with a new situation.
print "Considering new situation [1, 0, 0] -> ?: "
print neural_network.think(array([1, 0, 0]))