Multilayer Neural Network

This Neural Network is written in Python (3.7.6)
All of the matrix calculations of this neural network are done by NumPy(1.9.6)

Multilayer networks solve the classification problem for non linear sets by employing hidden layers, whose neurons are not directly connected to the output.

note: The Activation function of this neural network is the sigmoid function

HOW TO WORK WITH neuralNetwork.py:

1. Import Class

    from neuralNetwork import *
   

2. Create an Object

    nn = NeuralNetwork(number_of_first_layer_nodes, number_of_second_layer_nodes, ..., number_of_n-th_layer_nodes)
   

   note: this is a multilayer neural network so it can have as many layers as it wants. for example:

    nn = NeuralNetwork(5, 8, 2, 6, 1)
    nn = NeuralNetwork(6, 3, 4)
   

3. Generate the inputs and the targets that you want to train your network with
   For training the neural netwrok you should use the train() method:

    nn.train(inputs_list, targets_list)
   

   note: the length of inputs_list should be equal to first argument of the neural network
   and the lenght of targets_list should be equal to last argument of the neural network

   for example this time we are going to train the neural netwrok to solve XOR problem:

    inputs = [[0, 0], [1, 0], [1, 1], [0, 1]]
    targets = [[0], [1], [0], [1]]
   
    # Training the neural network 10000 times
    for _ in range(10000):
        index = randint(0, 3)
        nn.train(inputs[index], targets[index])
   

4. Getting neural network prediction
   For Getting predictions you should use predict() method:

    nn.predict(inputs_list)
   

   note: the length of inputs_list should be equal to first argument of the neural network

   Again we continue with XOR problem.
   For getting the result after trianing the neural network you can do this:

    inputs = [[0, 0], [1, 0], [1, 1], [0, 1]]
   
    for i in range(4):
        output = nn.predict(inputs[i])[0]
        print("XOR", inputs[i], " ≈ ", output)
   

   output:

      XOR [0, 0]  ≈  0.040257468759495006
      XOR [1, 0]  ≈  0.9490906272946118
      XOR [1, 1]  ≈  0.05838670461374751
      XOR [0, 1]  ≈  0.944367949333263