or_gate.py

# -*- coding: utf-8 -*-
"""Untitled0.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Ug-CoBTvYQ3sm2h9yBDcI8S_C_lm-NZU
"""

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

NUM_FEATURES = 2
NUM_ITER = 2000
learning_rate = 0.01

x = np.array([[0, 0], [1, 0], [1, 1], [0, 1]], np.float32) # 4x2, input
y = np.array([0, 1, 1, 1], np.float32) # 4, correct output, OR operation

W = np.zeros(NUM_FEATURES, np.float32) # 2x1, weight
b = np.zeros(1, np.float32) # 1x1

for k in range(NUM_ITER):
   yHat = x.dot(W) + b
   yHat = 1.0 / (1.0 + np.exp(-yHat))
 
   err = y - yHat
 
   deltaW = np.transpose(x).dot(err) # have to 2x1
   deltaB = np.sum(err) # have to 1x1. collect error from all the 4 samples
   W = W + learning_rate * deltaW # if err = y - yHat, then W = W + lRate * deltW
   b = b + learning_rate * deltaB

plot_x = np.array([np.min(x[:, 0] - 0.2), np.max(x[:, 1]+0.2)])
plot_y = - 1 / W[1] * (W[0] * plot_x + b) # comes from, w0*x + w1*y + b = 0 then y = (-1/w1) (w0*x + b)
 
print('W:' + str(W))
print('b:' + str(b))
print('plot_y: '+ str(plot_y))
plt.scatter(x[:, 0], x[:, 1], c=y, s=100, cmap='viridis')
plt.plot(plot_x, plot_y, color='k', linewidth=2)
plt.xlim([-0.2, 1.2]); plt.ylim([-0.2, 1.25]);
plt.show()