Added a plot of the really bad MNIST images

Author: mnielsen

fig/mnist.py

@@ -17,10 +17,10 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-def main(j):
+def main():
     training_set, validation_set, test_set = load_data()
-    images = get_images(training_set)[:100]
-    plot_mnist_digit(images[j])
+    images = get_images(test_set)
+    plot_really_bad_images(images)
 
 #### Plotting
 def plot_images_together(images):
@@ -94,6 +94,40 @@ def plot_top_left(image):
     plt.yticks(np.array([]))
     plt.show()
 
+def plot_bad_images(images):
+    """This takes a list of images misclassified by a pretty good
+    neural network --- one achieving over 93 percent accuracy --- and
+    turns them into a figure."""
+    bad_image_indices = [8, 18, 33, 92, 119, 124, 149, 151, 193, 233, 241, 247, 259, 300, 313, 321, 324, 341, 349, 352, 359, 362, 381, 412, 435, 445, 449, 478, 479, 495, 502, 511, 528, 531, 547, 571, 578, 582, 597, 610, 619, 628, 629, 659, 667, 691, 707, 717, 726, 740, 791, 810, 844, 846, 898, 938, 939, 947, 956, 959, 965, 982, 1014, 1033, 1039, 1044, 1050, 1055, 1107, 1112, 1124, 1147, 1181, 1191, 1192, 1198, 1202, 1204, 1206, 1224, 1226, 1232, 1242, 1243, 1247, 1256, 1260, 1263, 1283, 1289, 1299, 1310, 1319, 1326, 1328, 1357, 1378, 1393, 1413, 1422, 1435, 1467, 1469, 1494, 1500, 1522, 1523, 1525, 1527, 1530, 1549, 1553, 1609, 1611, 1634, 1641, 1676, 1678, 1681, 1709, 1717, 1722, 1730, 1732, 1737, 1741, 1754, 1759, 1772, 1773, 1790, 1808, 1813, 1823, 1843, 1850, 1857, 1868, 1878, 1880, 1883, 1901, 1913, 1930, 1938, 1940, 1952, 1969, 1970, 1984, 2001, 2009, 2016, 2018, 2035, 2040, 2043, 2044, 2053, 2063, 2098, 2105, 2109, 2118, 2129, 2130, 2135, 2148, 2161, 2168, 2174, 2182, 2185, 2186, 2189, 2224, 2229, 2237, 2266, 2272, 2293, 2299, 2319, 2325, 2326, 2334, 2369, 2371, 2380, 2381, 2387, 2393, 2395, 2406, 2408, 2414, 2422, 2433, 2450, 2488, 2514, 2526, 2548, 2574, 2589, 2598, 2607, 2610, 2631, 2648, 2654, 2695, 2713, 2720, 2721, 2730, 2770, 2771, 2780, 2863, 2866, 2896, 2907, 2925, 2927, 2939, 2995, 3005, 3023, 3030, 3060, 3073, 3102, 3108, 3110, 3114, 3115, 3117, 3130, 3132, 3157, 3160, 3167, 3183, 3189, 3206, 3240, 3254, 3260, 3280, 3329, 3330, 3333, 3383, 3384, 3475, 3490, 3503, 3520, 3525, 3559, 3567, 3573, 3597, 3598, 3604, 3629, 3664, 3702, 3716, 3718, 3725, 3726, 3727, 3751, 3752, 3757, 3763, 3766, 3767, 3769, 3776, 3780, 3798, 3806, 3808, 3811, 3817, 3821, 3838, 3848, 3853, 3855, 3869, 3876, 3902, 3906, 3926, 3941, 3943, 3951, 3954, 3962, 3976, 3985, 3995, 4000, 4002, 4007, 4017, 4018, 4065, 4075, 4078, 4093, 4102, 4139, 4140, 4152, 4154, 4163, 4165, 4176, 4199, 4201, 4205, 4207, 4212, 4224, 4238, 4248, 4256, 4284, 4289, 4297, 4300, 4306, 4344, 4355, 4356, 4359, 4360, 4369, 4405, 4425, 4433, 4435, 4449, 4487, 4497, 4498, 4500, 4521, 4536, 4548, 4563, 4571, 4575, 4601, 4615, 4620, 4633, 4639, 4662, 4690, 4722, 4731, 4735, 4737, 4739, 4740, 4761, 4798, 4807, 4814, 4823, 4833, 4837, 4874, 4876, 4879, 4880, 4886, 4890, 4910, 4950, 4951, 4952, 4956, 4963, 4966, 4968, 4978, 4990, 5001, 5020, 5054, 5067, 5068, 5078, 5135, 5140, 5143, 5176, 5183, 5201, 5210, 5331, 5409, 5457, 5495, 5600, 5601, 5617, 5623, 5634, 5642, 5677, 5678, 5718, 5734, 5735, 5749, 5752, 5771, 5787, 5835, 5842, 5845, 5858, 5887, 5888, 5891, 5906, 5913, 5936, 5937, 5945, 5955, 5957, 5972, 5973, 5985, 5987, 5997, 6035, 6042, 6043, 6045, 6053, 6059, 6065, 6071, 6081, 6091, 6112, 6124, 6157, 6166, 6168, 6172, 6173, 6347, 6370, 6386, 6390, 6391, 6392, 6421, 6426, 6428, 6505, 6542, 6555, 6556, 6560, 6564, 6568, 6571, 6572, 6597, 6598, 6603, 6608, 6625, 6651, 6694, 6706, 6721, 6725, 6740, 6746, 6768, 6783, 6785, 6796, 6817, 6827, 6847, 6870, 6872, 6926, 6945, 7002, 7035, 7043, 7089, 7121, 7130, 7198, 7216, 7233, 7248, 7265, 7426, 7432, 7434, 7494, 7498, 7691, 7777, 7779, 7797, 7800, 7809, 7812, 7821, 7849, 7876, 7886, 7897, 7902, 7905, 7917, 7921, 7945, 7999, 8020, 8059, 8081, 8094, 8095, 8115, 8246, 8256, 8262, 8272, 8273, 8278, 8279, 8293, 8322, 8339, 8353, 8408, 8453, 8456, 8502, 8520, 8522, 8607, 9009, 9010, 9013, 9015, 9019, 9022, 9024, 9026, 9036, 9045, 9046, 9128, 9214, 9280, 9316, 9342, 9382, 9433, 9446, 9506, 9540, 9544, 9587, 9614, 9634, 9642, 9645, 9700, 9716, 9719, 9729, 9732, 9738, 9740, 9741, 9742, 9744, 9745, 9749, 9752, 9768, 9770, 9777, 9779, 9792, 9808, 9831, 9839, 9856, 9858, 9867, 9879, 9883, 9888, 9890, 9893, 9905, 9944, 9970, 9982]
+    n = len(bad_image_indices)
+    bad_images = [images[j] for j in bad_image_indices]
+    fig = plt.figure(figsize=(10, 15))
+    for j in xrange(1, n+1):
+        ax = fig.add_subplot(25, 125, j)
+        ax.matshow(bad_images[j-1], cmap = matplotlib.cm.binary)
+        ax.set_title(str(bad_image_indices[j-1]))
+        plt.xticks(np.array([]))
+        plt.yticks(np.array([]))
+    plt.subplots_adjust(hspace = 1.2)
+    plt.show()
+
+def plot_really_bad_images(images):
+    """This takes a list of the worst images from plot_bad_images and
+    turns them into a figure."""
+    really_bad_image_indices = [
+        324, 582, 659, 726, 846, 956, 1124, 1393,
+        1773, 1868, 2018, 2109, 2654, 4199, 4201, 4620, 5457, 5642]
+    n = len(really_bad_image_indices)
+    really_bad_images = [images[j] for j in really_bad_image_indices]
+    fig = plt.figure(figsize=(10, 2))
+    for j in xrange(1, n+1):
+        ax = fig.add_subplot(2, 9, j)
+        ax.matshow(really_bad_images[j-1], cmap = matplotlib.cm.binary)
+        #ax.set_title(str(really_bad_image_indices[j-1]))
+        plt.xticks(np.array([]))
+        plt.yticks(np.array([]))
+    plt.show()
+
 def plot_features(image):
     "Plot the top right, bottom left, and bottom right of ``image``."
     image_1, image_2, image_3 = np.copy(image), np.copy(image), np.copy(image)
@@ -122,15 +156,15 @@ def plot_features(image):
 def load_data():
     """ Return the MNIST data as a tuple containing the training data,
     the validation data, and the test data."""
-    f = open('../../data/mnist.pkl', 'rb')
+    f = open('../data/mnist.pkl', 'rb')
     training_set, validation_set, test_set = cPickle.load(f)
     f.close()
     return (training_set, validation_set, test_set)
 
 def get_images(training_set):
-    """ Return a list containing the first 100 images from the MNIST
-    data set. Each image is represented as a 2-d numpy array."""
-    flattened_images = training_set[0][:100]
+    """ Return a list containing the images from the MNIST data
+    set. Each image is represented as a 2-d numpy array."""
+    flattened_images = training_set[0]
     return [np.reshape(f, (-1, 28)) for f in flattened_images]
 
 #### Main