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)
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