Initial commit

net/modules.py

@@ -0,0 +1,77 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def load_data(file_path):
+    data = pd.read_csv(file_path)
+    data = np.array(data)
+    m, n = data.shape
+    
+    data_train = data[1000:m].T  
+    Y_train = data_train[0].astype(int)
+    X_train = data_train[1:n]
+    
+    data_test = data[0:1000].T
+    Y_test = data_test[0].astype(int)
+    X_test = data_test[1:n]
+    
+    return X_train, Y_train, X_test, Y_test
+
+def plot_accuracy(acc_store, save_path=None):
+    """
+    Plot training and validation accuracy over iterations.
+
+    Parameters:
+    acc_store (list of tuples): Each tuple contains (training_accuracy, validation_accuracy).
+    save_path (str, optional): If provided, saves the plot to the specified path.
+    """
+    # Unzip the accuracy data
+    training_accuracy, validation_accuracy = zip(*acc_store)
+
+    # Plot
+    plt.figure(figsize=(10, 6))
+    plt.plot(training_accuracy, label='Training Accuracy')
+    plt.plot(validation_accuracy, label='Validation Accuracy')
+    plt.title('Training and Validation Accuracy Over Iterations')
+    plt.xlabel('Iterations (in steps of 10)')
+    plt.ylabel('Accuracy')
+    plt.legend()
+    plt.grid(True)
+
+    # Save the plot if a path is provided
+    if save_path:
+        plt.savefig(save_path)
+        print(f"Accuracy plot saved to {save_path}")
+    
+    # Show the plot
+    plt.show()
+
+
+def plot_loss(loss_store, save_path=None):
+    """
+    Plot training and validation loss over iterations.
+
+    Parameters:
+    loss_store (list of tuples): Each tuple contains (training_loss, validation_loss).
+    save_path (str, optional): If provided, saves the plot to the specified path.
+    """
+    # Unzip the loss data
+    training_loss, validation_loss = zip(*loss_store)
+
+    # Plot
+    plt.figure(figsize=(10, 6))
+    plt.plot(training_loss, label='Training Loss')
+    plt.plot(validation_loss, label='Validation Loss')
+    plt.title('Training and Validation Loss Over Iterations')
+    plt.xlabel('Iterations (in steps of 10)')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.grid(True)
+
+    # Save the plot if a path is provided
+    if save_path:
+        plt.savefig(save_path)
+        print(f"Loss plot saved to {save_path}")
+    
+    # Show the plot
+    plt.show()