Machine Learning 2018/19. Assignment 1: Neural Networks - exercise1

exercise1.py

import numpy as np

X = np.asarray([

    [6, 4, 1, 1],
    [3, 1, 7, 1],
    [6, 10, 9, 1],
    [4, 2, 4, 1],
    [1, 5, 10, 1],
    [5, 3, 7, 1],
    [3, 3, 4, 1],
    [10, 3, 3, 1],
    [3, 4, 9, 1],
    [4, 6, 6, 1],
])

T = np.asarray([1, 1, 1, 1, 1, 1, -1, -1, -1, -1]).T

Y = np.zeros(T.shape)

w = [-0.1, -0.3, 0.2, 2.0]


def forward(x, w):
    y = 0.0
    for i in range(len(w)):
        y += w[i] * x[i]
    return y


def forwardall():
    for k in range(X.shape[0]):
        Y[k] = forward(X[k], w)


lr = 0.02


def gradient():
    n = X.shape[0]
    for k in range(n):
        x = X[k]
        for i in range(len(w)):
            ewi = (Y[k] - T[k]) / n * x[i]
            w[i] -= lr * ewi


forwardall()
gradient()
print("w")
print(np.round(w, 6))

# [-0.1986, -0.38219999999999993, 0.027600000000000017, 1.9735999999999998]