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Author SHA1 Message Date
Brett 99622d8371 windows? 2025-01-10 01:13:44 -05:00
Brett 91d73c2048 huh 2025-01-10 01:13:24 -05:00
64 changed files with 2322 additions and 5 deletions
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CMakeLists.txt
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cmake_minimum_required(VERSION 3.25) cmake_minimum_required(VERSION 3.25)
project(COSC-4P80-Final-Project VERSION 0.0.26) project(COSC-4P80-Final-Project VERSION 0.0.28)
option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF) option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)
option(ENABLE_UBSAN "Enable the ub sanitizer" OFF) option(ENABLE_UBSAN "Enable the ub sanitizer" OFF)

4
commit.py
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@ -24,8 +24,8 @@ USER_HOME = Path.home()
ENVIRONMENT_DATA_LOCATION = USER_HOME / ".brett_scripts.env" ENVIRONMENT_DATA_LOCATION = USER_HOME / ".brett_scripts.env"
if sys.platform.startswith("win"): if sys.platform.startswith("win"):
CONFIG_FILE_DIRECTORY = Path(os.getenv('APPDATA') + "\BLT") CONFIG_FILE_DIRECTORY = Path(os.getenv('APPDATA') + "\\BLT")
CONFIG_FILE_LOCATION = Path(CONFIG_FILE_DIRECTORY + "\commit_config.env") CONFIG_FILE_LOCATION = Path(CONFIG_FILE_DIRECTORY + "\\commit_config.env")
else: else:
XDG_CONFIG_HOME = os.environ.get('XDG_CONFIG_HOME') XDG_CONFIG_HOME = os.environ.get('XDG_CONFIG_HOME')
if XDG_CONFIG_HOME is None: if XDG_CONFIG_HOME is None:

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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]))

2
latex/chapters/introduction_and_motivation.tex
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@ -1,7 +1,7 @@
\chapter{Introduction} \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{}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. \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.

2
lib/blt-with-graphics

@ -1 +1 @@
Subproject commit 29286e66daa724ef08692d9a65e9c88e5467d9b2 Subproject commit c8c54ba39c67f7417cf574fdc7735048bab10491

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23
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100
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100
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---
100
33189,26811,1.71538,0.01
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---
100
42110,17890,1.49723,0.01
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10
9804,196
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101
part3/deep_train_results.csv Normal file
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@ -0,0 +1,101 @@
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52,0.022549
53,0.0222468
54,0.0216549
55,0.0215349
56,0.0213095
57,0.0207342
58,0.0205722
59,0.0203588
60,0.0197622
61,0.0196016
62,0.0193939
63,0.0188806
64,0.0187535
65,0.0185648
66,0.0181315
67,0.0179925
68,0.0177892
69,0.0173287
70,0.0172235
71,0.0170562
72,0.0165975
73,0.0164889
74,0.0163273
75,0.0159148
76,0.0158014
77,0.015649
78,0.0152989
79,0.0152008
80,0.0150857
81,0.0148114
82,0.0147433
83,0.0146234
84,0.0143517
85,0.0140966
86,0.0139146
87,0.0133857
88,0.0132851
89,0.0132214
90,0.0129353
91,0.0128603
92,0.012749
93,0.0124975
94,0.0124362
95,0.0123004
96,0.0120975
97,0.0120275
98,0.01191
99,0.0117123
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
10 8 0.325683
11 9 0.313872
12 10 0.311215
13 11 0.307983
14 12 0.299708
15 13 0.297838
16 14 0.295578
17 15 0.289277
18 16 0.287833
19 17 0.286149
20 18 0.281665
21 19 0.280615
22 20 0.279356
23 21 0.275943
24 22 0.275135
25 23 0.0439989
26 24 0.0413578
27 25 0.0407257
28 26 0.0399023
29 27 0.0376838
30 28 0.0371429
31 29 0.0364896
32 30 0.0345936
33 31 0.0341421
34 32 0.0335672
35 33 0.0319707
36 34 0.0315714
37 35 0.0310887
38 36 0.0297445
39 37 0.0294226
40 38 0.0290267
41 39 0.0279524
42 40 0.0276589
43 41 0.0273608
44 42 0.026431
45 43 0.0261913
46 44 0.0259559
47 45 0.025095
48 46 0.0248997
49 47 0.0246282
50 48 0.0238708
51 49 0.0236727
52 50 0.0233752
53 51 0.022755
54 52 0.022549
55 53 0.0222468
56 54 0.0216549
57 55 0.0215349
58 56 0.0213095
59 57 0.0207342
60 58 0.0205722
61 59 0.0203588
62 60 0.0197622
63 61 0.0196016
64 62 0.0193939
65 63 0.0188806
66 64 0.0187535
67 65 0.0185648
68 66 0.0181315
69 67 0.0179925
70 68 0.0177892
71 69 0.0173287
72 70 0.0172235
73 71 0.0170562
74 72 0.0165975
75 73 0.0164889
76 74 0.0163273
77 75 0.0159148
78 76 0.0158014
79 77 0.015649
80 78 0.0152989
81 79 0.0152008
82 80 0.0150857
83 81 0.0148114
84 82 0.0147433
85 83 0.0146234
86 84 0.0143517
87 85 0.0140966
88 86 0.0139146
89 87 0.0133857
90 88 0.0132851
91 89 0.0132214
92 90 0.0129353
93 91 0.0128603
94 92 0.012749
95 93 0.0124975
96 94 0.0124362
97 95 0.0123004
98 96 0.0120975
99 97 0.0120275
100 98 0.01191
101 99 0.0117123

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9
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10
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55447,4553,0.330358,0.0001
55449,4551,0.329537,0.0001
55453,4547,0.328738,1e-05
55454,4546,0.327957,1e-05
55455,4545,0.327195,1e-05
55456,4544,0.32664,1e-06
---
81
37637,22363,1.48546,0.01
48728,11272,1.14505,0.01
51096,8904,0.9468,0.01
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55503,4497,0.326661,1e-05
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---
73
46154,13846,1.4243,0.01
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55378,4622,0.323297,1e-05
55380,4620,0.322473,1e-05
55384,4616,0.32178,1e-06
---
88
42886,17114,1.43696,0.01
49019,10981,1.04177,0.01
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---
73
43636,16364,1.41735,0.01
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81,0.111416
82,0.111166
83,0.110919
84,0.110678
85,0.110442
86,0.110211
87,0.11002
88,0.074416
89,0.0431583
90,0.0430622
91,0.0429801
1 Epoch Loss
2 0 1.47156
3 1 1.08006
4 2 0.899459
5 3 0.794781
6 4 0.726028
7 5 0.676767
8 6 0.639035
9 7 0.60898
10 8 0.58431
11 9 0.563658
12 10 0.546082
13 11 0.53091
14 12 0.517668
15 13 0.505998
16 14 0.49563
17 15 0.486344
18 16 0.477966
19 17 0.470363
20 18 0.463432
21 19 0.457079
22 20 0.451234
23 21 0.445838
24 22 0.440836
25 23 0.436187
26 24 0.43185
27 25 0.427796
28 26 0.423995
29 27 0.420423
30 28 0.417057
31 29 0.413881
32 30 0.410877
33 31 0.408032
34 32 0.405334
35 33 0.402771
36 34 0.400332
37 35 0.398008
38 36 0.395791
39 37 0.39367
40 38 0.391641
41 39 0.389697
42 40 0.387833
43 41 0.386042
44 42 0.384322
45 43 0.382667
46 44 0.381073
47 45 0.379533
48 46 0.378044
49 47 0.3766
50 48 0.375197
51 49 0.373831
52 50 0.372501
53 51 0.371206
54 52 0.369948
55 53 0.368695
56 54 0.367473
57 55 0.366284
58 56 0.365111
59 57 0.363905
60 58 0.362737
61 59 0.361548
62 60 0.360391
63 61 0.359266
64 62 0.358171
65 63 0.357081
66 64 0.356019
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}