Repository for following the video about creating and implementing the ANNs from scratch.

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Gerardo Marx 5b099d5806 data_list ommited due to its legth		7 months ago
main_files	Class and ANN working	7 months ago
Readme.md	data_list ommited due to its legth	7 months ago
main.ipynb	Class and ANN working	7 months ago
mnist_test.csv	Class and ANN working	7 months ago
mnist_train.csv	Class and ANN working	7 months ago

Readme.md

Classes and methods

class Dog:
    # init method
    def __init__(self, dogName, dogAge):
        self.name = dogName
        self.age = dogAge
        pass 
    
    def status(self):
        print("The dog's name is: ", self.name)
        print("The dog's age is: ", self.age)

dog1 = Dog("firulais", 8)
dog2 = Dog("Perro", 2)
dog3 = Dog("cuadrado", 5)

dog3.status()

The dog's name is:  cuadrado
The dog's age is:  5

ANN class implementation

class Neurona:
    # init
    def __init__():
        pass
    # feedforward (cálculo)
    def feedforward():
        pass
    # backpropagation (entrenamiento)
    def backpropagation():
        pass

import numpy as np

class Neurona:
    # init
    def __init__(self, inputNodes, hiddenNodes, outputNodes):
        self.inN = inputNodes
        self.hN = hiddenNodes
        self.outN = outputNodes
        ## 
        ## W = w11 w21
        self.wih = np.random.rand(self.hN, self.inN)
        self.who = np.random.rand(self.outN, self.hN)
        pass
    # feedforward (cálculo)
    def feedforward():
        pass
    # backpropagation (entrenamiento)
    def backpropagation():
        pass

inputs = np.array([0.21,0.39, 0.87],ndmin=2).T
inputs

array([[0.21],
       [0.39],
       [0.87]])

import numpy as np
class Neurona:
    # init
    def __init__(self, inputNodes, hiddenNodes, outputNodes):
        self.inN = inputNodes
        self.hN = hiddenNodes
        self.outN = outputNodes
        ## 
        ## W = w11 w21
        self.wih = np.random.rand(self.hN, self.inN)-0.5
        self.who = np.random.rand(self.outN, self.hN)-0.5
        pass
    # feedforward (cálculo)
    def feedforward(self, Inputs):
        #Xh
        self.inputs = np.array(Inputs, ndmin=2).T
        self.Xh = np.dot(self.wih, self.inputs)
        self.af = lambda x:1/(1+np.exp(-x))
        #Oh
        self.Oh = self.af(self.Xh)
        
        #Xo
        self.Xo = np.dot(self.who, self.Oh)
        #Oh
        self.Oo = self.af(self.Xo)
        #Oo
        pass
    # backpropagation (entrenamiento)
    def backpropagation(self, Inputs, Targets, Learning):
        lr = Learning
        self.inputs = np.array(Inputs, ndmin=2).T
        self.targets = np.array(Targets, ndmin=2).T
        #Xh
        self.Xh = np.dot(self.wih, self.inputs)
        self.af = lambda x:1/(1+np.exp(-x))
        #Oh
        self.Oh = self.af(self.Xh)
        
        #Xo
        self.Xo = np.dot(self.who, self.Oh)
        #Oh
        self.Oo = self.af(self.Xo)
        #Oo

        # output error
        oe = self.targets-self.Oo
        he = np.dot(self.who.T, oe)
        self.who=self.who+lr*np.dot(oe*self.Oo*(1-self.Oo), self.Oh.T)
        self.wih=self.wih+lr*np.dot(he*self.Oh*(1-self.Oh), self.inputs.T)
        pass

mynn = Neurona(3,5,3)
mynn.backpropagation([0.21, 0.39, 0.87], [0.12, 0.10, 0.99], 0.3)
mynn.who

array([[0.89962766, 0.94344371, 0.25520613, 0.50403018, 0.79841922],
       [0.46742312, 0.8497042 , 0.72150451, 0.4832481 , 0.1614701 ],
       [0.07348318, 0.74125584, 0.96294501, 0.6829789 , 0.99122929]])

mynn.backpropagation([0.21, 0.39, 0.87], [0.12, 0.10, 0.99], 0.3)
mynn.who

array([[0.88622924, 0.92904876, 0.24001207, 0.48936863, 0.78475758],
       [0.45035738, 0.83136916, 0.70215163, 0.46457349, 0.14406909],
       [0.07510425, 0.74299748, 0.96478333, 0.68475279, 0.9928822 ]])

Mnist database

data_file = open("mnist_train.csv", 'r')
data_list = data_file.readlines()
data_file.close
data_list

Output ommited due to its length

import numpy as np 
import matplotlib.pyplot as plt
values = data_list[30600].split(',')
image = np.asfarray(values[1:]).reshape((28,28))
plt.imshow(image)
plt.show()

values[0]

'1'

Training

# hyper-parameters:
inputNodes = 784
hiddenNodes = 100
outputNodes = 10
learningRate = 0.1
myANN = Neurona(inputNodes, hiddenNodes, outputNodes)

myANN.wih[:,0]

array([ 3.80810137e-01, -2.76021452e-01,  1.89578510e-01, -4.40622523e-01,
        4.49345620e-04, -2.99576867e-02,  2.88898176e-01,  1.00257001e-01,
       -1.25427321e-01, -4.10841382e-01,  1.01058830e-01, -4.19682107e-01,
       -2.61884751e-01, -4.86639132e-01, -4.10475994e-01,  4.72554845e-01,
       -2.58545906e-01,  2.12843730e-01,  4.77632343e-01,  4.85691685e-01,
       -2.21585439e-01,  1.43760970e-01, -2.23361202e-01, -3.69871226e-01,
       -1.21973032e-01, -4.29052035e-01, -3.97413451e-01,  4.65864914e-01,
       -1.26186271e-01,  2.07401026e-01,  1.05937271e-01,  1.46875776e-01,
        2.95015245e-01,  3.43457017e-02, -3.29510246e-01, -2.48072947e-01,
       -3.64935302e-01,  3.09460892e-01, -5.01871329e-02,  2.98023264e-01,
       -3.19341252e-01, -3.90225500e-02,  3.10060197e-01, -3.13901381e-01,
        3.69558936e-01, -1.38918625e-01, -4.78037558e-01,  9.24705861e-02,
       -1.34122723e-01, -8.70299561e-02, -3.93637460e-02,  3.81876093e-01,
       -8.53474718e-02, -1.29582776e-01, -4.02245397e-01, -4.56054710e-01,
        2.64854223e-02, -1.53704117e-01,  1.90609293e-01,  3.62048289e-01,
       -6.25482544e-02,  3.82745274e-01,  1.43009716e-01,  1.75700493e-01,
        4.09349632e-01, -4.89451563e-01, -9.27621754e-02,  1.41559919e-01,
        1.34585537e-01, -2.86828229e-01,  3.44307471e-01, -4.98223666e-01,
       -4.05137857e-01,  1.81890913e-01, -4.57908080e-01,  4.87169160e-01,
       -2.11761055e-01,  3.18985378e-02, -3.00127711e-01,  4.80807855e-01,
       -2.70966977e-01,  2.36741392e-02, -3.03853361e-01, -4.29846112e-02,
        5.33828969e-02,  2.18366071e-01,  2.13736555e-01, -1.15094147e-01,
       -2.41327435e-02,  3.40917056e-01,  3.76097667e-02, -5.77225150e-03,
       -3.83882298e-01, -2.31975044e-01,  2.44874629e-01,  7.90940279e-02,
       -2.92400249e-01,  4.53702317e-01,  8.09984361e-02, -4.93878547e-02])

epoch = 1
for e in range(epoch):
    for record in data_list:
        values = record.split(',')
        inputData = (np.asfarray(values[1:])/255*0.99) +0.01
        target = np.zeros(outputNodes)+0.01
        target[int(values[0])] = 0.99
        myANN.backpropagation(inputData, target, learningRate)
        pass
    pass

len(data_list)

values = data_list[30600].split(',')
inputData = (np.asfarray(values[1:])/255*0.99) +0.01
myANN.feedforward(inputData)

myANN.Oo

array([[2.42193187e-02],
       [1.11760730e-01],
       [5.09994337e-01],
       [4.44172963e-02],
       [3.37977872e-04],
       [3.97805262e-03],
       [1.06099919e-03],
       [3.15328122e-02],
       [1.33289159e-01],
       [2.44129745e-03]])