init lfs

stats/stats.py

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+#/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Simple script to show statistics a Neural Network training session.
+
+# - Imports
+from matplotlib import pyplot as plt
+
+# Read the data from the file
+def read_data(file_path: str) -> list:
+    with open(file_path, 'r') as file:
+        lines = file.readlines()
+    return [line.strip() for line in lines if line.strip()]
+
+# Parse the data from the file
+# This function takes the data read from the file and parses it.
+# Each line contains multiple values separated by commas.
+# The function extracts the values and returns them as a list of lists.
+def parse_data(data: list, numEntries: int) -> list[list]:
+    parsed_data = [[] for _ in range(numEntries)]
+    for line in data:
+        values = line.split(',')
+        for i in range(numEntries):
+            try:
+                parsed_data[i].append(float(values[i]))
+            except (ValueError, IndexError):
+                print(f"Error parsing line: {line}")
+                continue
+    return parsed_data
+
+# Plot the data using matplotlib
+def plot_data(
+        data: list,
+        title: str,
+        xlabel: str,
+        ylabel: str,
+        log_scale: bool = False
+    ) -> None:
+    # Plot the data
+    plt.figure(figsize=(10, 5))
+    plt.plot(data, label=title)
+    # Add labels and title
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.title(title)
+    plt.legend()
+    plt.grid()
+    # Show the plot
+    if log_scale:
+        plt.yscale('log')
+    plt.show()
+
+def plot_accuracies(
+        learn_rates: list,
+        train: list,
+        eval: list
+    ) -> None:
+    # Create 2 subplots, one for the learning rates in relation to the epochs
+    # and one for the training and evaluation accuracies
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 10))
+    # Plot the learning rates
+    ax1.plot(learn_rates, label='Learning Rate')
+    ax1.set_xlabel('Epochs')
+    ax1.set_ylabel('Learning Rate')
+    ax1.set_title('Learning Rate vs Epochs')
+    ax1.legend()
+    ax1.grid()
+    # Plot the accuracies
+    ax2.plot(train, label='Training Accuracy')
+    ax2.plot(eval, label='Evaluation Accuracy')
+    ax2.set_xlabel('Epochs')
+    ax2.set_ylabel('Accuracy')
+    ax2.set_title('Training and Evaluation Accuracy vs Epochs')
+    ax2.legend()
+    ax2.grid()
+    # Show the plots
+    plt.tight_layout()
+    plt.show()
+
+# Main function to execute the script
+def main():
+    # Read the data from the file
+    data = read_data('stats/stats_256_128.txt')
+    # Parse the data
+    learn_rates, data_accuracy, eval_accuracy = parse_data(data, 3)
+    # Plot the accuracies
+    plot_accuracies(learn_rates, data_accuracy, eval_accuracy)
+
+if __name__ == "__main__":
+    main()
+
+# - End of script