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Brett 2025-01-10 01:13:24 -05:00
parent feab6c8c0e
commit 91d73c2048
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CMakeLists.txt
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cmake_minimum_required(VERSION 3.25)
project(COSC-4P80-Final-Project VERSION 0.0.26)
project(COSC-4P80-Final-Project VERSION 0.0.27)
option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)
option(ENABLE_UBSAN "Enable the ub sanitizer" OFF)

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graph2.py Normal file
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import matplotlib.pyplot as plt
import pandas as pd
import sys
def plot_stacked_graph(title, output, csv_file1, csv_file2, name1, name2, position, position2):
# Read CSV files
data1 = pd.read_csv(csv_file1, header=0)
data2 = pd.read_csv(csv_file2, header=0)
# Extract column titles
x1_label, y1_label = data1.columns[0], data1.columns[1]
x2_label, y2_label = data2.columns[0], data2.columns[1]
# Extract data
x1, y1 = data1[x1_label], data1[y1_label]
x2, y2 = data2[x2_label], data2[y2_label]
# Create the plot
fig, ax = plt.subplots()
line1, = ax.plot(x1, y1, label=f"{name1}")
line2, = ax.plot(x2, y2, label=f"{name2}")
if position < 2 ** 32:
ax.axvline(x=position, color='red', linestyle='--')
ax.text(position, ax.get_ylim()[1] * 0.95, f"Feed-forward average\n # of epochs ({position})", color=line2.get_color(), fontsize=10, ha='right', va='top', backgroundcolor='white')
if position2 < 2 ** 32:
ax.axvline(x=position2, color='red', linestyle='--')
ax.text(position2, ax.get_ylim()[1] * 0.75, f"Deep learning average\n # of epochs ({position2})", color=line1.get_color(), fontsize=10, ha='right', va='top', backgroundcolor='white')
ax.set_xlabel(x1_label)
ax.set_ylabel(y1_label)
ax.legend()
ax.set_title(title, fontsize=12)
plt.savefig(output)
if __name__ == "__main__":
if len(sys.argv) != 9:
print("Usage: python script.py <title> <output_file> <csv_file1> <csv_file2> <csv1_name> <csv2_name> <pos1> <pos2>")
sys.exit(1)
csv_file1 = sys.argv[3]
csv_file2 = sys.argv[4]
title = sys.argv[1]
output = sys.argv[2]
position = sys.argv[5]
position2 = sys.argv[6]
plot_stacked_graph(title, output, csv_file1, csv_file2, position, position2, int(sys.argv[7]), int(sys.argv[8]))

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latex/chapters/introduction_and_motivation.tex
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\chapter{Introduction}
\paragraph{}As previously mentioned, deep learning combines feature extraction through convolution and pooling with traditional neural networks, eliminating the need for humans to manually extract features from datasets. Convolution, in essence, is a filtering process where trained filter(s) slides over the input data to extract features and other useful information. Pooling is the subsequent process of taking local samples and selecting either the minimum, maximum, or average of those samples. This step helps identify feature locations and condenses the information produced by the convolution layer.
\paragraph{}A typical deep learning pipeline consists of several convolution and pooling layers, followed by a few fully connected layers. In this work, we aim to demonstrate that using a deep learning configuration can reduce the size of the feed-forward section without compromising program performance, thereby highlighting the effectiveness of deep learning.
\paragraph{}A typical deep learning pipeline consists of several convolution and pooling layers, followed by a few fully connected layers. In this work, we aim to demonstrate that using a deep learning network configuration can reduce the size of the feed-forward section without compromising program classification performance, thereby highlighting the effectiveness of deep learning.
\paragraph{}The MNIST database is a standard benchmark for image-processing neural networks. For our comparison, we will use a modified version of the DLIB deep learning example. This approach allows us to showcase the differences between standard feed-forward neural networks and deep learning networks without requiring expensive GPUs or AI accelerators. While the MNIST dataset is solvable using feed-forward neural networks, we intend to demonstrate that deep learning can achieve better classification performance, even on smaller networks.

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lib/blt-with-graphics

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Subproject commit 29286e66daa724ef08692d9a65e9c88e5467d9b2
Subproject commit c8c54ba39c67f7417cf574fdc7735048bab10491

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---
23
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---
100
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---
100
48864,11136,1.43814,0.01
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---
100
33189,26811,1.71538,0.01
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---
100
42110,17890,1.49723,0.01
50734,9266,1.07248,0.01
55510,4490,0.824242,0.01
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---
10
9804,196
9823,177
9827,173
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9821,179
1135,8865
9832,168
9814,186
9844,156
9770,230

101
part3/deep_train_results.csv Normal file
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@ -0,0 +1,101 @@
Epoch,Loss
0,1.48373
1,1.10924
2,0.834077
3,0.402929
4,0.385907
5,0.367766
6,0.335417
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1 Epoch Loss
2 0 1.48373
3 1 1.10924
4 2 0.834077
5 3 0.402929
6 4 0.385907
7 5 0.367766
8 6 0.335417
9 7 0.330922
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10
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93
part3/forward_train_results.csv Normal file
View File

@ -0,0 +1,93 @@
Epoch,Loss
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1,1.08006
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67 65 0.354983
68 66 0.353972
69 67 0.352988
70 68 0.352019
71 69 0.351056
72 70 0.350112
73 71 0.349178
74 72 0.348287
75 73 0.284092
76 74 0.283347
77 75 0.282694
78 76 0.214989
79 77 0.214468
80 78 0.214028
81 79 0.144601
82 80 0.144266
83 81 0.111416
84 82 0.111166
85 83 0.110919
86 84 0.110678
87 85 0.110442
88 86 0.110211
89 87 0.11002
90 88 0.074416
91 89 0.0431583
92 90 0.0430622
93 91 0.0429801

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\begin{figure}
\begin{tabular}{|c|c|c|c|}
\hline
Test & Correct & Incorrect & Accuracy (\%) \\
\hline
Feed-Forward & 9123 & 876 & 91\\
Deep Learning & 8949 & 1051 & 89\\
\end{tabular}
\end{figure}