Python script for working with MNIST dataset. Minor edits to work with python3 and ascii_show function
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import os | |
import struct | |
import numpy as np | |
""" | |
Source: https://gist.github.com/akesling/5358964 | |
Loosely inspired by http://abel.ee.ucla.edu/cvxopt/_downloads/mnist.py | |
which is GPL licensed. | |
""" | |
def read(dataset = "training", path = "."): | |
""" | |
Python function for importing the MNIST data set. It returns an iterator | |
of 2-tuples with the first element being the label and the second element | |
being a numpy.uint8 2D array of pixel data for the given image. | |
""" | |
if dataset is "training": | |
fname_img = os.path.join(path, 'train-images.idx3-ubyte') | |
fname_lbl = os.path.join(path, 'train-labels.idx1-ubyte') | |
elif dataset is "testing": | |
fname_img = os.path.join(path, 't10k-images.idx3-ubyte') | |
fname_lbl = os.path.join(path, 't10k-labels.idx1-ubyte') | |
else: | |
raise(ValueError, "dataset must be 'testing' or 'training'") | |
# Load everything in some numpy arrays | |
with open(fname_lbl, 'rb') as flbl: | |
magic, num = struct.unpack(">II", flbl.read(8)) | |
lbl = np.fromfile(flbl, dtype=np.int8) | |
with open(fname_img, 'rb') as fimg: | |
magic, num, rows, cols = struct.unpack(">IIII", fimg.read(16)) | |
img = np.fromfile(fimg, dtype=np.uint8).reshape(len(lbl), rows, cols) | |
get_img = lambda idx: (lbl[idx], img[idx]) | |
# Create an iterator which returns each image in turn | |
for i in range(len(lbl)): | |
yield get_img(i) | |
def show(image): | |
""" | |
Render a given numpy.uint8 2D array of pixel data. | |
""" | |
from matplotlib import pyplot | |
import matplotlib as mpl | |
fig = pyplot.figure() | |
ax = fig.add_subplot(1,1,1) | |
imgplot = ax.imshow(image, cmap=mpl.cm.Greys) | |
imgplot.set_interpolation('nearest') | |
ax.xaxis.set_ticks_position('top') | |
ax.yaxis.set_ticks_position('left') | |
pyplot.show() | |
def ascii_show(image): | |
for y in image[1]: | |
row = "" | |
for x in y: | |
row += '{0: <4}'.format(x) | |
print(row) |
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