windows?

huh
2025-01-10 01:13:44 -05:00 · 2025-01-10 01:13:24 -05:00
64 changed files with 2322 additions and 5 deletions
--- a/.idea/workspace
+++ b/.idea/workspace
@ -0,0 +1,264 @@
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--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@ -1,5 +1,5 @@
 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_UBSAN "Enable the ub sanitizer" OFF)
--- a/commit.py
+++ b/commit.py
@ -24,8 +24,8 @@ USER_HOME = Path.home()
 ENVIRONMENT_DATA_LOCATION = USER_HOME / ".brett_scripts.env"

 if sys.platform.startswith("win"):
-	CONFIG_FILE_DIRECTORY = Path(os.getenv('APPDATA') + "\BLT")
-	CONFIG_FILE_LOCATION = Path(CONFIG_FILE_DIRECTORY + "\commit_config.env")
+	CONFIG_FILE_DIRECTORY = Path(os.getenv('APPDATA') + "\\BLT")
+	CONFIG_FILE_LOCATION = Path(CONFIG_FILE_DIRECTORY + "\\commit_config.env")
 else:
 	XDG_CONFIG_HOME = os.environ.get('XDG_CONFIG_HOME')
 	if XDG_CONFIG_HOME is None:
--- a/graph2.py
+++ b/graph2.py
@ -0,0 +1,50 @@
+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]))
--- a/latex/chapters/introduction_and_motivation.tex
+++ b/latex/chapters/introduction_and_motivation.tex
@ -1,7 +1,7 @@
 \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.

--- a/lib/blt-with-graphics
+++ b/lib/blt-with-graphics
@ -1 +1 @@
-Subproject commit 29286e66daa724ef08692d9a65e9c88e5467d9b2
+Subproject commit c8c54ba39c67f7417cf574fdc7735048bab10491
--- a/part3/average_epochs.txt
+++ b/part3/average_epochs.txt
@ -0,0 +1 @@
+806,923
--- a/part3/deep_learning/mnist_network_deep_learning0.dat
+++ b/part3/deep_learning/mnist_network_deep_learning0.dat
--- a/part3/deep_learning/mnist_network_deep_learning1.dat
+++ b/part3/deep_learning/mnist_network_deep_learning1.dat
--- a/part3/deep_learning/mnist_network_deep_learning2.dat
+++ b/part3/deep_learning/mnist_network_deep_learning2.dat
--- a/part3/deep_learning/mnist_network_deep_learning3.dat
+++ b/part3/deep_learning/mnist_network_deep_learning3.dat
--- a/part3/deep_learning/mnist_network_deep_learning4.dat
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--- a/part3/deep_learning/mnist_network_deep_learning9.dat
+++ b/part3/deep_learning/mnist_network_deep_learning9.dat
--- a/part3/deep_learning/mnist_sync_deep_learning0
+++ b/part3/deep_learning/mnist_sync_deep_learning0
--- a/part3/deep_learning/mnist_sync_deep_learning0_
+++ b/part3/deep_learning/mnist_sync_deep_learning0_
--- a/part3/deep_learning/mnist_sync_deep_learning1
+++ b/part3/deep_learning/mnist_sync_deep_learning1
--- a/part3/deep_learning/mnist_sync_deep_learning1_
+++ b/part3/deep_learning/mnist_sync_deep_learning1_
--- a/part3/deep_learning/mnist_sync_deep_learning2
+++ b/part3/deep_learning/mnist_sync_deep_learning2
--- a/part3/deep_learning/mnist_sync_deep_learning2_
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+++ b/part3/deep_learning/mnist_sync_deep_learning3
--- a/part3/deep_learning/mnist_sync_deep_learning3_
+++ b/part3/deep_learning/mnist_sync_deep_learning3_
--- a/part3/deep_learning/mnist_sync_deep_learning4
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--- a/part3/deep_learning/mnist_sync_deep_learning5_
+++ b/part3/deep_learning/mnist_sync_deep_learning5_
--- a/part3/deep_learning/mnist_sync_deep_learning6
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--- a/part3/deep_learning/mnist_sync_deep_learning6_
+++ b/part3/deep_learning/mnist_sync_deep_learning6_
--- a/part3/deep_learning/mnist_sync_deep_learning7
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--- a/part3/deep_learning/mnist_sync_deep_learning7_
+++ b/part3/deep_learning/mnist_sync_deep_learning7_
--- a/part3/deep_learning/mnist_sync_deep_learning8
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--- a/part3/deep_learning/mnist_sync_deep_learning8_
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--- a/part3/deep_learning/mnist_sync_deep_learning9_
+++ b/part3/deep_learning/mnist_sync_deep_learning9_
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--- a/part3/deep_train_results.csv
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--- a/part3/feed_forward/epochs.png
+++ b/part3/feed_forward/epochs.png
--- a/part3/feed_forward/mnist_network_feed_forward0.dat
+++ b/part3/feed_forward/mnist_network_feed_forward0.dat
--- a/part3/feed_forward/mnist_network_feed_forward1.dat
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--- a/part3/feed_forward/mnist_network_feed_forward2.dat
+++ b/part3/feed_forward/mnist_network_feed_forward2.dat
--- a/part3/feed_forward/mnist_network_feed_forward3.dat
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--- a/part3/feed_forward/mnist_network_feed_forward4.dat
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--- a/part3/feed_forward/mnist_network_feed_forward5.dat
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--- a/part3/feed_forward/mnist_network_feed_forward6.dat
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--- a/part3/feed_forward/mnist_network_feed_forward7.dat
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--- a/part3/feed_forward/mnist_network_feed_forward8.dat
+++ b/part3/feed_forward/mnist_network_feed_forward8.dat
--- a/part3/feed_forward/mnist_network_feed_forward9.dat
+++ b/part3/feed_forward/mnist_network_feed_forward9.dat
--- a/part3/feed_forward/mnist_sync_feed_forward0
+++ b/part3/feed_forward/mnist_sync_feed_forward0
--- a/part3/feed_forward/mnist_sync_feed_forward1
+++ b/part3/feed_forward/mnist_sync_feed_forward1
--- a/part3/feed_forward/mnist_sync_feed_forward2
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--- a/part3/feed_forward/mnist_sync_feed_forward3
+++ b/part3/feed_forward/mnist_sync_feed_forward3
--- a/part3/feed_forward/mnist_sync_feed_forward4
+++ b/part3/feed_forward/mnist_sync_feed_forward4
--- a/part3/feed_forward/mnist_sync_feed_forward5
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--- a/part3/feed_forward/mnist_sync_feed_forward6
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--- a/part3/feed_forward/mnist_sync_feed_forward7
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--- a/part3/feed_forward/mnist_sync_feed_forward8
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--- a/part3/feed_forward/mnist_sync_feed_forward9
+++ b/part3/feed_forward/mnist_sync_feed_forward9
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--- a/part3/forward_train_results.csv
+++ b/part3/forward_train_results.csv
@ -0,0 +1,93 @@
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--- a/part3/silly3.png
+++ b/part3/silly3.png
--- a/part3/test_results_table.txt
+++ b/part3/test_results_table.txt
@ -0,0 +1,9 @@
+\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}
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