#!/usr/bin/python3
"""
mnist
~~~~~
Download and draw/plot images based on the MNIST data.
asa
Ponzoni Nelson
"""
import os
import struct
from array import array
## solo las funciones de plot utilizan estas librerias
import numpy
import matplotlib
import matplotlib.pyplot as plt
import sys
import time
import pickle
import bz2 # bzip2
import gzip # gzip
[documentos]class MNIST(object):
def __init__(self, path='.'):
self.path = path
self.test_img_fname = 't10k-images-idx3-ubyte'
self.test_lbl_fname = 't10k-labels-idx1-ubyte'
self.train_img_fname = 'train-images-idx3-ubyte'
self.train_lbl_fname = 'train-labels-idx1-ubyte'
self.test_images = []
self.test_labels = []
self.train_images = []
self.train_labels = []
self.validation_images = []
self.validation_labels = []
self.cantidad_validacion = 10000
self.load_data()
# @classmethod
[documentos] def load_data(self):
"""
carga los datos en los arrays
"""
#cargo los dos conjustos de disco
self.train_images, self.train_labels = self.load_training(numpy_array=True)
self.test_images, self.test_labels = self.load_testing(numpy_array=True)
# separo el conjunto de validacion y entrenamiento
self.validation_images = numpy.asarray(self.train_images)
self.validation_labels = numpy.asarray(self.train_labels)
self.validation_images = self.train_images[0:self.cantidad_validacion]
self.validation_labels = self.train_labels[0:self.cantidad_validacion]
# quito los elementos de validacion del conjunto de entrenamiento
self.train_images = self.train_images[self.cantidad_validacion:]
self.train_labels = self.train_labels[self.cantidad_validacion:]
return
[documentos] def get_training(self):
return self.train_images, self.train_labels
[documentos] def get_testing(self):
return self.test_images, self.test_labels
[documentos] def get_validation(self):
return self.validation_images, self.validation_labels
[documentos] def get_all(self):
"""
devuelve todos los datos en una lista de tuplas, [(img,lbl)..]
"""
return [(self.train_images, self.train_labels), (self.test_images, self.test_labels), (self.validation_images, self.validation_labels)]
[documentos] def load_testing(self, numpy_array=True):
"""
si numpy_array es False se devuelve una lista, sino un tipo numpy array
"""
ims, labels = self.load(os.path.join(self.path, self.test_img_fname),
os.path.join(self.path, self.test_lbl_fname))
self.test_images = ims
self.test_labels = labels
if numpy_array: ## la paso a un numpy_array
self.test_images = numpy.asarray(self.test_images)
self.test_labels = numpy.asarray(self.test_labels)
return self.test_images, self.test_labels
[documentos] def load_training(self, numpy_array=True):
"""
si numpy_array es False se devuelve una lista, sino un tipo numpy array
"""
ims, labels = self.load(os.path.join(self.path, self.train_img_fname),
os.path.join(self.path, self.train_lbl_fname))
self.train_images = ims
self.train_labels = labels
if numpy_array:
self.train_images = numpy.asarray(self.train_images)
self.train_labels = numpy.asarray(self.train_labels)
return self.train_images, self.train_labels
@classmethod
[documentos] def load(cls, path_img, path_lbl):
with open(path_lbl, 'rb') as file:
magic, size = struct.unpack(">II", file.read(8))
if magic != 2049:
raise ValueError('Magic number mismatch, expected 2049,'
'got {}'.format(magic))
labels = array("B", file.read())
with open(path_img, 'rb') as file:
magic, size, rows, cols = struct.unpack(">IIII", file.read(16))
if magic != 2051:
raise ValueError('Magic number mismatch, expected 2051,'
'got {}'.format(magic))
image_data = array("B", file.read())
images = []
for i in range(size):
images.append([0] * rows * cols)
for i in range(size):
images[i][:] = image_data[i * rows * cols:(i + 1) * rows * cols]
return images, labels
@classmethod
[documentos] def display(cls, img, width=28, threshold=200):
render = ''
for i in range(len(img)):
if i % width == 0:
render += '\n'
if img[i] > threshold:
render += '@'
else:
render += '.'
return render
@property
def info(self):
"""
imprime por pantalla informacion relativa a la cantidad de cada tipo de datos en la bd
"""
# entrenamiento
contador = numpy.zeros((10,1),dtype=numpy.int16)
for i in range(0, len(self.train_labels)):
contador[self.train_labels[i]] = contador[self.train_labels[i]] + 1
total = len(self.train_labels)
print("E N T R E N A M I E N T O:")
for idx, val in enumerate(contador):
print("Imagenes '"+ str(idx) + "':\t", val, "\t %:" + str(numpy.round(val/total*100.0,2)) )
# testing
contador = numpy.zeros((10,1),dtype=numpy.int16)
for i in range(0, len(self.test_labels)):
contador[self.test_labels[i]] = contador[self.test_labels[i]] + 1
total = len(self.test_labels)
print("\nT E S T E O:")
for idx, val in enumerate(contador):
print("Imagenes '"+ str(idx) + "':\t", val, "\t %:" + str(numpy.round(val/total*100.0,2)) )
# validacion
contador = numpy.zeros((10,1),dtype=numpy.int16)
for i in range(0, len(self.validation_labels)):
contador[self.validation_labels[i]] = contador[self.validation_labels[i]] + 1
total = len(self.validation_labels)
print("\nV A L I D A C I O N:")
for idx, val in enumerate(contador):
print("Imagenes '"+ str(idx) + "':\t", val, "\t %:" + str(numpy.round(val/total*100.0,2)) )
return
@staticmethod
[documentos] def plot_one_digit(image, label=None, save=None):
"""
Plot a single MNIST image.
"""
if not type(image) == numpy.array:
image = numpy.asarray(image)
# convert a vector array (image) to matrix sample => (28,28)
if image.shape == (784,): # se recorre con un solo indice, i=#
image = numpy.reshape(image, (28,28))
elif image.shape == (784,1): # se recorre la imagen con dos indices, j=0
image = numpy.reshape(image, (28,28))
elif image.shape == (28,28):
pass
else:
sys.exit("No se reconoce la dimesion de la imagen")
# set de label if not yet
if label is None:
label = time.strftime('%Y-%m-%d_%H:%M:%S')
else:
label = str(label) + "__" + time.strftime('%Y-%m-%d_%H:%M:%S')
fig = plt.figure(label)
ax = fig.add_subplot(1, 1, 1)
ax.matshow(image, cmap = matplotlib.cm.binary)
plt.xticks(numpy.array([]))
plt.yticks(numpy.array([]))
#plt.title(label)
if save == 'png' or save is True:
plt.savefig(self.path + label + ".png", format='png')
elif save == 'eps':
plt.savefig(self.path + label + '.eps', format='eps', dpi=1000)
elif save == 'svg':
plt.savefig(self.path + label + '.svg', format='svg', dpi=1000)
else:
pass
plt.show()
return 1
@staticmethod
[documentos] def plot_ten_digits(images, save=None, crop=0):
"""
Plot a single image containing all six MNIST images, one after
the other.
if crop is true, Note that we crop the sides of the images so that they
appear reasonably close together.
"""
fig = plt.figure()
images = [numpy.reshape(f, (-1, 28)) for f in images]
if crop:
images = [image[:, 3:25] for image in images]
image = numpy.concatenate(images, axis=1)
ax = fig.add_subplot(1, 1, 1)
ax.matshow(image, cmap = matplotlib.cm.binary)
plt.xticks(numpy.array([]))
plt.yticks(numpy.array([]))
if save == 'png' or save is True:
plt.savefig(self.path + "tenDigits" + ".png", format='png')
elif save == 'eps':
plt.savefig(self.path + 'tenDigits' + '.eps', format='eps', dpi=1000)
elif save == 'svg':
plt.savefig(self.path + 'tenDigits' + '.svg', format='svg', dpi=1000)
else:
pass
plt.show()
return
@staticmethod
[documentos] def plot_ten_digits2(images, save=None, crop=0):
"""
Plot a single image containing all six MNIST images, one after
the other.
if crop is true, Note that we crop the sides of the images so that they
appear reasonably close together.
"""
fig = plt.figure()
images = [numpy.reshape(f, (-1, 28)) for f in images]
if crop:
images = [image[:, 3:25] for image in images]
image = numpy.concatenate(images, axis=1)
ax = fig.add_subplot(1, 1, 1)
ax.matshow(image, cmap = matplotlib.cm.binary)
plt.xticks(numpy.array([]))
plt.yticks(numpy.array([]))
if save == 'png' or save is True:
plt.savefig(self.path + "tenDigits" + ".png", format='png')
elif save == 'eps':
plt.savefig(self.path + 'tenDigits' + '.eps', format='eps', dpi=1000)
elif save == 'svg':
plt.savefig(self.path + 'tenDigits' + '.svg', format='svg', dpi=1000)
else:
pass
plt.show()
return
@staticmethod
[documentos] def prepare(directorio, nombre='mnist', compresion='bzip2'):
"""
:param path: ruta donde se encuentran los archivos desde internet
"""
handler = MNIST(path=directorio)
# guardar el archivo en el directorio en un unico binario
dirr = os.getcwd()
os.chdir(directorio)
result = find(nombre+'*',".") # pregunto si existe un archivo igual para no recomprimir
if not result:
save2disk(handler, filename=nombre, compression=compresion)
#else:
#print('El archivo ' + nombre + ' en ' + directorio + ' ya existe, saliendo...')
os.chdir(dirr)
return 1
[documentos]def find(pattern, path):
import os, fnmatch
result = []
for root, dirs, files in os.walk(path):
for name in files:
if fnmatch.fnmatch(name, pattern):
result.append(os.path.join(root, name))
return result
[documentos]def save2disk(mnist, filename='mnist', compression='gzip'):
if compression is None:
with open(filename + '.pkl','wb') as f:
pickle.dump(mnist,f)
f.close()
elif compression == 'gzip':
with gzip.GzipFile(filename + '.pgz', 'w') as f:
pickle.dump(mnist,f)
f.close()
elif compression == 'bzip2':
with bz2.BZ2File(filename + '.pbz2', 'w') as f:
pickle.dump(mnist,f)
f.close()
else:
sys.exit("Parametro de compresion no se reconoce")
return
[documentos]def open4disk(filename='mnist', compression='gzip'):
if compression is None:
with open(filename + '.pkl', "rb") as f:
mnist = pickle.load(f)
f.close()
elif compression == 'gzip':
with gzip.open(filename + '.pgz', "rb") as f:
mnist = pickle.load(f)
f.close()
elif compression == 'bzip2':
with bz2.open(filename + '.pbz2', 'rb') as f:
mnist = pickle.load(f)
f.close()
else:
sys.exit("Parametro de compresion no se reconoce")
return mnist
"""
#### Miscellanea
def get_images(training_set):
### 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]
TODO
#### Plotting
def plot_images_together(images):
### Plot a single image containing all six MNIST images, one after
##the other. Note that we crop the sides of the images so that they
##appear reasonably close together.
fig = plt.figure()
images = [image[:, 3:25] for image in images]
image = np.concatenate(images, axis=1)
ax = fig.add_subplot(1, 1, 1)
ax.matshow(image, cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show()
def plot_10_by_10_images(images):
### Plot 100 MNIST images in a 10 by 10 table. Note that we crop
the images so that they appear reasonably close together. The
image is post-processed to give the appearance of being continued.###
fig = plt.figure()
images = [image[3:25, 3:25] for image in images]
#image = np.concatenate(images, axis=1)
for x in range(10):
for y in range(10):
ax = fig.add_subplot(10, 10, 10*y+x)
ax.matshow(images[10*y+x], cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show()
def plot_images_separately(images):
###Plot the six MNIST images separately.###
fig = plt.figure()
for j in xrange(1, 7):
ax = fig.add_subplot(1, 6, j)
ax.matshow(images[j-1], cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show()
def plot_mnist_digit(image):
### Plot a single MNIST image.###
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.matshow(image, cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show()
def plot_2_and_1(images):
###Plot a 2 and a 1 image from the MNIST set.###
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.matshow(images[5], cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
ax = fig.add_subplot(1, 2, 2)
ax.matshow(images[3], cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show()
def plot_top_left(image):
###Plot the top left of ``image``.###
image[14:,:] = np.zeros((14,28))
image[:,14:] = np.zeros((28,14))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.matshow(image, cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
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)
image_1[:,:14] = np.zeros((28,14))
image_1[14:,:] = np.zeros((14,28))
image_2[:,14:] = np.zeros((28,14))
image_2[:14,:] = np.zeros((14,28))
image_3[:14,:] = np.zeros((14,28))
image_3[:,:14] = np.zeros((28,14))
fig = plt.figure()
ax = fig.add_subplot(1, 3, 1)
ax.matshow(image_1, cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
ax = fig.add_subplot(1, 3, 2)
ax.matshow(image_2, cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
ax = fig.add_subplot(1, 3, 3)
ax.matshow(image_3, cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show()
def plot_rotated_image(image):
###Plot an MNIST digit and a version rotated by 10 degrees.###
# Do the initial plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.matshow(image, cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show()
# Set up the rotated image. There are fast matrix techniques
# for doing this, but we'll do a pedestrian approach
rot_image = np.zeros((28,28))
theta = 15*np.pi/180 # 15 degrees
def to_xy(j, k):
# Converts from matrix indices to x, y co-ords, using the
# 13, 14 matrix entry as the origin
return (k-13, -j+14) # x range: -13..14, y range: -13..14
def to_jk(x, y):
# Converts from x, y co-ords to matrix indices
return (-y+14, x+13)
def image_value(image, x, y):
# returns the value of the image at co-ordinate x, y
# (Note that this would be better done as a closure, if Pythong
# supported closures, so that image didn't need to be passed)
j, k = to_jk(x, y)
return image[j, k]
# Element by element, figure out what should be in the rotated
# image. We simply take each matrix entry, figure out the
# corresponding x, y co-ordinates, rotate backward, and then
# average the nearby matrix elements. It's not perfect, and it's
# not fast, but it works okay.
for j in range(28):
for k in range(28):
x, y = to_xy(j, k)
# rotate by -theta
x1 = np.cos(theta)*x + np.sin(theta)*y
y1 = -np.sin(theta)*x + np.cos(theta)*y
# Nearest integer x entries are x2 and x2+1. delta_x
# measures how to interpolate
x2 = np.floor(x1)
delta_x = x1-x2
# Similarly for y
y2 = np.floor(y1)
delta_y = y1-y2
# Check if we're out of bounds, and if so continue to next entry
# This will miss a boundary row and layer, but that's okay,
# MNIST digits usually don't go that near the boundary.
if x2 < -13 or x2 > 13 or y2 < -13 or y2 > 13: continue
# If we're in bounds, average the nearby entries.
value \
= (1-delta_x)*(1-delta_y)*image_value(image, x2, y2)+\
(1-delta_x)*delta_y*image_value(image, x2, y2+1)+\
delta_x*(1-delta_y)*image_value(image, x2+1, y2)+\
delta_x*delta_y*image_value(image, x2+1, y2+1)
# Rescale the value by a hand-set fudge factor. This
# seems to be necessary because the averaging doesn't
# quite work right. The fudge-factor should probably be
# theta-dependent, but I've set it by hand.
rot_image[j, k] = 1.3*value
plot_mnist_digit(rot_image)
"""
