diff --git a/.idea/workspace (conflicted copy 2025-01-08 170848).xml b/.idea/workspace (conflicted copy 2025-01-08 170848).xml
new file mode 100644
index 0000000..9ca0d8d
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diff --git a/CMakeLists.txt b/CMakeLists.txt
index 77f103a..7a5222b 100644
--- 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 0.0.27)
option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)
option(ENABLE_UBSAN "Enable the ub sanitizer" OFF)
diff --git a/graph2.py b/graph2.py
new file mode 100644
index 0000000..9c35b0d
--- /dev/null
+++ 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 <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]))
diff --git a/latex/chapters/introduction_and_motivation.tex b/latex/chapters/introduction_and_motivation.tex
index 359e6ce..5bf0ce2 100644
--- 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.
diff --git a/lib/blt-with-graphics b/lib/blt-with-graphics
index 29286e6..c8c54ba 160000
--- a/lib/blt-with-graphics
+++ b/lib/blt-with-graphics
@@ -1 +1 @@
-Subproject commit 29286e66daa724ef08692d9a65e9c88e5467d9b2
+Subproject commit c8c54ba39c67f7417cf574fdc7735048bab10491
diff --git a/part3/average_epochs.txt b/part3/average_epochs.txt
new file mode 100644
index 0000000..454efd5
--- /dev/null
+++ b/part3/average_epochs.txt
@@ -0,0 +1 @@
+806,923
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diff --git a/part3/deep_learning/state.bin b/part3/deep_learning/state.bin
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diff --git a/part3/forward_train_results.csv b/part3/forward_train_results.csv
new file mode 100644
index 0000000..3cd386b
--- /dev/null
+++ b/part3/forward_train_results.csv
@@ -0,0 +1,93 @@
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diff --git a/part3/silly3.png b/part3/silly3.png
new file mode 100644
index 0000000..79e9e71
Binary files /dev/null and b/part3/silly3.png differ
diff --git a/part3/test_results_table.txt b/part3/test_results_table.txt
new file mode 100644
index 0000000..64d71ba
--- /dev/null
+++ 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}