Report - Patryk Krawiec

Introduction

In our project garbage truck is moving around the grid visiting houses displayed as garbage dumps for simplicity. We divide the houses into 2 groups:

houses of people that pay for the garbage collection
those who don't pay

When our truck gets to one of the dumpsters it analyzes an unique 2-digit number of this particular household to determine whether to collect or not. This number is randomly chosen image from the test set, so there's still a small chance of a mistake during recognision phase.

Implementation

I used Deep Neural Network as my method of recognizing numbers. Main function of my Network is L_layer_model. It uses following function:

L_model_forward - it calculates the weight and returns it
L_model_backward - it returns gradients of parameters and bias
compute_cost - it return the cost function value, which determines how well our network is trained
update_parameters - it updates the weights using gradient descent upgrading our network's performance

Changes in group files

To implement house numbering I had to assign pictures of numbers to each dumster. That's why 2 new parameters of Dumpster model were initialized. In main function I randomly choose those digits and then exclude a few of them from the list of paying customers, so when the truck visits such a house no trash is taken.

Forward Propagation

When the weight are initialized we can begin forward propagation. In order to avoid problem of vanishing gradient I use RELU as activation function in every hidden layer. On the other hand due to sigmoid function returning probability-like output it is ideal for output layer.

Important Notice

Training this model takes some time so in order not to calculate it every time, I save it in the file "NN.npy". I highly recommend running the numbering.py file before the Main.py to generate Neural Network and 2 additional files containing pictures of numbers randomly chosen from the test set.

Libraries

Following libraries and data sets are needed to run the program :

from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np
import os.path
import csv
import random

2.3 KiB Raw Blame History