neuralNetwork.py

import matplotlib.pyplot as plt
import numpy as np
from MNIST_Dataloader import MNIST_Dataloader

class NeuralNetwok: 
    def __init__(self, input_size=28*28, output_size=10, h_layers=1, h_neurons_per_layer=128):
        self.input_size = input_size
        self.output_size = output_size
        self.h_layers = h_layers
        self.h_neurons_per_layer = h_neurons_per_layer
        self.layers = self.init_layers(input_size, h_neurons_per_layer, output_size)

    # TODO: implement a programmable amount of hidden layer initialization
    def init_layers(self, input_size, h_neurons_per_layer, output_size):
        '''
        Get layer size info and develop weight array 
        initialize random weights for each connection to next layer
            weight array of output size, in array for every input node 
        return these weight arrays for each node as layer
        '''
        layer1 = np.random.uniform(-1.,1.,size=(input_size, h_neurons_per_layer))\
            /np.sqrt(input_size * h_neurons_per_layer)
        
        layer2 = np.random.uniform(-1.,1.,size=(h_neurons_per_layer, output_size))\
            /np.sqrt(h_neurons_per_layer * output_size)
        
        return [layer1, layer2]
    
    def desired_array_out(self, label):
        '''Turn label into desired output array 
        input label         5
        return desire array [0 0 0 0 0 1 0 0 0 0]
        '''
        desired_array = np.zeros(self.output_size, np.float32)
        desired_array[label] = 1
        
        return desired_array