diff --git a/Codes/Code-3b.md b/Codes/Code-3b.md
index 34e62d9..ee7508c 100644
--- a/Codes/Code-3b.md
+++ b/Codes/Code-3b.md
@@ -38,34 +38,13 @@ from sklearn.metrics import confusion_matrix, classification_report
 # Fashion MNIST is built into Keras, downloads automatically on first run
 (X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
 
-'''
-import numpy as np
-import gzip
-import os
-
-def load_fashion_mnist(path):
-    """Load Fashion MNIST from local .gz files (Kaggle Zalando format)."""
-    files = {
-        'X_train': 'train-images-idx3-ubyte.gz',
-        'y_train': 'train-labels-idx1-ubyte.gz',
-        'X_test':  't10k-images-idx3-ubyte.gz',
-        'y_test':  't10k-labels-idx1-ubyte.gz',
-    }
-
-    with gzip.open(os.path.join(path, files['X_train'])) as f:
-        X_train = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)
-    with gzip.open(os.path.join(path, files['y_train'])) as f:
-        y_train = np.frombuffer(f.read(), np.uint8, offset=8)
-    with gzip.open(os.path.join(path, files['X_test'])) as f:
-        X_test = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)
-    with gzip.open(os.path.join(path, files['y_test'])) as f:
-        y_test = np.frombuffer(f.read(), np.uint8, offset=8)
-
-    return (X_train, y_train), (X_test, y_test)
-
-# Replace the Keras load line with:
-(X_train, y_train), (X_test, y_test) = load_fashion_mnist('./fashion-mnist/')
-'''
+# import pandas as pd
+# train_df = pd.read_csv('fashion-mnist_train.csv')
+# test_df  = pd.read_csv('fashion-mnist_test.csv')
+# y_train = train_df['label'].values
+# y_test  = test_df['label'].values
+# X_train = train_df.drop('label', axis=1).values.reshape(-1, 28, 28)  # unflatten pixels to 28x28
+# X_test  = test_df.drop('label', axis=1).values.reshape(-1, 28, 28)
 
 print("Training set shape:", X_train.shape)   # (60000, 28, 28)
 print("Test set shape:    ", X_test.shape)    # (10000, 28, 28)