Create demo.py

demo.py

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+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]))