Python_numpy_DNN.py

# Python tutorial using numpy with a multi-layer hidden layer Deep Artificial Neural Network (DNN) on a sudo generated dataset.
# Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making.
# Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
# An Artificial Neural Network is based on the structure of a biological brain. 
# These systems learn to perform tasks or classify based on data, without the need to be programmed specific task rules.
# Python is an interpreted, high-level, general-purpose programming language.
# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. 

#Import python libraries
from numpy import exp, array, random, dot

# Define the NeuronLayer Class
class NeuronLayer():
    def __init__(self, number_of_neurons, number_of_inputs_per_neuron):
        self.synaptic_weights = 2 * random.random((number_of_inputs_per_neuron, number_of_neurons)) - 1

# Define the NeuralNetwork Class
class NeuralNetwork():
    def __init__(self, layer1, layer2):
        self.layer1 = layer1
        self.layer2 = layer2

    # 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 range(number_of_training_iterations):
            # Pass the training set through our neural network
            output_from_layer_1, output_from_layer_2 = self.think(training_set_inputs)

            # Calculate the error for layer 2 (The difference between the desired output
            # and the predicted output).
            layer2_error = training_set_outputs - output_from_layer_2
            layer2_delta = layer2_error * self.__sigmoid_derivative(output_from_layer_2)

            # Calculate the error for layer 1 (By looking at the weights in layer 1,
            # we can determine by how much layer 1 contributed to the error in layer 2).
            layer1_error = layer2_delta.dot(self.layer2.synaptic_weights.T)
            layer1_delta = layer1_error * self.__sigmoid_derivative(output_from_layer_1)

            # Calculate how much to adjust the weights by
            layer1_adjustment = training_set_inputs.T.dot(layer1_delta)
            layer2_adjustment = output_from_layer_1.T.dot(layer2_delta)

            # Adjust the weights.
            self.layer1.synaptic_weights += layer1_adjustment
            self.layer2.synaptic_weights += layer2_adjustment

    # The neural network thinks.
    def think(self, inputs):
        output_from_layer1 = self.__sigmoid(dot(inputs, self.layer1.synaptic_weights))
        output_from_layer2 = self.__sigmoid(dot(output_from_layer1, self.layer2.synaptic_weights))
        return output_from_layer1, output_from_layer2

    # Analyze the neural network weights
    def print_weights(self):
        print("    Layer 1 (4 neurons, each with 3 inputs):")
        print(self.layer1.synaptic_weights)
        print("    Layer 2 (1 neuron, with 4 inputs):")
        print(self.layer2.synaptic_weights)

if __name__ == "__main__":

    #Seed the random number generator
    random.seed(1)

    # Create layer 1 (4 neurons, each with 3 inputs)
    layer1 = NeuronLayer(4, 3)

    # Create layer 2 (a single neuron with 4 inputs)
    layer2 = NeuronLayer(1, 4)

    # Combine the layers to create a neural network
    neural_network = NeuralNetwork(layer1, layer2)

    # Analyze the random starting synaptic weights
    print("\nStage 1) Random starting synaptic weights: ")
    neural_network.print_weights()

    # The training set. We have 7 examples, each consisting of 3 input values
    # and 1 output value.
    training_set_inputs = array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [0, 1, 0], [1, 0, 0], [1, 1, 1], [0, 0, 0]])
    training_set_outputs = array([[0, 1, 1, 1, 1, 0, 0]]).T

    # Train the neural network using the training set.
    # Do it 60,000 times and make small adjustments each time.
    neural_network.train(training_set_inputs, training_set_outputs, 60000)

    # Analyze the random synaptic weights after training
    print("\nStage 2) New synaptic weights after training: ")
    neural_network.print_weights()

    # Analyze the neural network with a new situation.
    print("\nStage 3) Considering a new situation [1, 1, 0] -> ?: ")
    hidden_state, output = neural_network.think(array([1, 1, 0]))
    print('The difference between the desired output and the predicted output: ', output)