myBackProp2.py

import numpy as np
import pandas as pd
from matplotlib import pyplot as ply

#%%

data = pd.read_csv("D:\\eafit\\2021 2\\opt1\\proyecto\\train.csv")

#%%

data = np.array(data)
m, n = data.shape
np.random.shuffle(data)

data_dev = data[0:1000].T
Y_dev = data_dev[0]
X_dev = data_dev[1:n]
X_dev = X_dev / 255.

data_train = data[1000:m].T
Y_train = data_train[0]
X_train = data_train[1:n]
X_train = X_train / 255.
_,m_train = X_train.shape

#%%
def init_params(n_features = 784, hidden_layers = 1):
    W1 = np.random.rand(10, n_features) - 0.5
    b1 = np.random.rand(10, 1) - 0.5
    hiddenList = [[
                   np.random.rand(10, 10) - 0.5,
                   np.random.rand(10, 1) - 0.5
                   ]
                  for i in hidden_layers]
    
    return W1, b1, hiddenList
    
    

def forward_prop(W1, b1, hiddenList, X):
    Z1 = W1.dot(X) + b1
    A1 = ReLU(Z1)
    for i in range(hiddenList.length):
        Z2 = W2.dot(A1) + b2
        A2 = softmax(Z2)
    return Z1, A1, Z2, A2

def ReLU(Z):
    return np.maximum(Z, 0)

def softmax(Z):
    return np.exp(Z) / sum(np.exp(Z))

# m is the number of rows or examples
def backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y):
    one_hot_Y = one_hot(Y)
    dZ2 = A2 - one_hot_Y
    dW2 = 1 / m * dZ2.dot(A1.T)
    db2 = 1 / m * np.sum(dZ2)
    dZ1 = W2.T.dot(dZ2) * ReLU_deriv(Z1)
    dW1 = 1 / m * dZ1.dot(X.T)
    db1 = 1 / m * np.sum(dZ1)
    return dW1, db1, dW2, db2

def ReLU_deriv(Z):
    return Z > 0

def one_hot(Y):
    one_hot_Y = np.zeros((Y.size, Y.max() + 1))
    one_hot_Y[np.arange(Y.size), Y] = 1
    one_hot_Y = one_hot_Y.T
    return one_hot_Y

def update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha):
    W1 = W1 - alpha * dW1
    b1 = b1 - alpha * db1    
    W2 = W2 - alpha * dW2  
    b2 = b2 - alpha * db2    
    return W1, b1, W2, b2

def get_predictions(A2):
    return np.argmax(A2, 0)

def get_accuracy(predictions, Y):
    print(predictions, Y)
    return np.sum(predictions == Y) / Y.size

def gradient_descent(X, Y, alpha, iterations, hiddenLayers = 1):
    n_features, _ = X.shape
    W1, b1, layersList = init_params(n_features)
    for i in range(iterations):
        Z1, A1, Z2, A2 = forward_prop(W1, b1, W2, b2, X)
        dW1, db1, dW2, db2 = backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y)
        W1, b1, W2, b2 = update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha)
        if i % 10 == 0:
            print("Iteration: ", i)
            predictions = get_predictions(A2)
            print(get_accuracy(predictions, Y))
    return W1, b1, W2, b2

#%%

W1, b1, W2, b2 = gradient_descent(X_train, Y_train, 0.10, 500)