diff --git a/notebook.py b/notebook.py
index 2894a8bfb..3fe64de2d 100644
--- a/notebook.py
+++ b/notebook.py
@@ -95,12 +95,15 @@ def show_iris(i=0, j=1, k=2):
 # MNIST
 
 
-def load_MNIST(path="aima-data/MNIST"):
+def load_MNIST(path="aima-data/MNIST/Digits", fashion=False):
     import os, struct
     import array
     import numpy as np
     from collections import Counter
 
+    if fashion:
+        path = "aima-data/MNIST/Fashion"
+
     plt.rcParams.update(plt.rcParamsDefault)
     plt.rcParams['figure.figsize'] = (10.0, 8.0)
     plt.rcParams['image.interpolation'] = 'nearest'
@@ -143,8 +146,17 @@ def load_MNIST(path="aima-data/MNIST"):
     return(train_img, train_lbl, test_img, test_lbl)
 
 
-def show_MNIST(labels, images, samples=8):
-    classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
+digit_classes = [str(i) for i in range(10)]
+fashion_classes = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
+                   "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
+
+
+def show_MNIST(labels, images, samples=8, fashion=False):
+    if not fashion:
+        classes = digit_classes
+    else:
+        classes = fashion_classes
+
     num_classes = len(classes)
 
     for y, cls in enumerate(classes):
@@ -161,13 +173,19 @@ def show_MNIST(labels, images, samples=8):
     plt.show()
 
 
-def show_ave_MNIST(labels, images):
-    classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
+def show_ave_MNIST(labels, images, fashion=False):
+    if not fashion:
+        item_type = "Digit"
+        classes = digit_classes
+    else:
+        item_type = "Apparel"
+        classes = fashion_classes
+
     num_classes = len(classes)
 
     for y, cls in enumerate(classes):
         idxs = np.nonzero([i == y for i in labels])
-        print("Digit", y, ":", len(idxs[0]), "images.")
+        print(item_type, y, ":", len(idxs[0]), "images.")
 
         ave_img = np.mean(np.vstack([images[i] for i in idxs[0]]), axis = 0)
         #print(ave_img.shape)