43 lines
2.3 KiB
Markdown
43 lines
2.3 KiB
Markdown
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# **Report - Patryk Krawiec**
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## Introduction
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In our project garbage truck is moving around the grid visiting houses displayed as garbage dumps for simplicity.
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We divide the houses into 2 groups:
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* houses of people that pay for the garbage collection
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* those who don't pay
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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.
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This number is randomly chosen image from the test set, so there's still a small chance of a mistake during recognision phase.
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## Implementation
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I used Deep Neural Network as my method of recognizing numbers.
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Main function of my Network is L_layer_model. It uses following function:
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* L_model_forward - it calculates the weight and returns it
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* L_model_backward - it returns gradients of parameters and bias
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* compute_cost - it return the cost function value, which determines how well our network is trained
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* update_parameters - it updates the weights using gradient descent upgrading our network's performance
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## Changes in group files
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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.
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## Forward Propagation
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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.
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## Important Notice
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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.
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## Libraries
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Following libraries and data sets are needed to run the program :
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```
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from sklearn.datasets import load_digits
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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import numpy as np
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import os.path
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import csv
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import random
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```
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