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mnist_data.py
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mnist_data.py
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# Some code was borrowed from https://github.com/petewarden/tensorflow_makefile/blob/master/tensorflow/models/image/mnist/convolutional.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import numpy
from scipy import ndimage
from six.moves import urllib
import tensorflow as tf
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
DATA_DIRECTORY = "data"
# Params for MNIST
IMAGE_SIZE = 28
NUM_CHANNELS = 1
PIXEL_DEPTH = 255
NUM_LABELS = 10
VALIDATION_SIZE = 5000 # Size of the validation set.
# Download MNIST data
def maybe_download(filename):
"""Download the data from Yann's website, unless it's already here."""
if not tf.gfile.Exists(DATA_DIRECTORY):
tf.gfile.MakeDirs(DATA_DIRECTORY)
filepath = os.path.join(DATA_DIRECTORY, filename)
if not tf.gfile.Exists(filepath):
filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
with tf.gfile.GFile(filepath) as f:
size = f.size()
print('Successfully downloaded', filename, size, 'bytes.')
return filepath
# Extract the images
def extract_data(filename, num_images, norm_shift=False, norm_scale=True):
"""Extract the images into a 4D tensor [image index, y, x, channels].
Values are rescaled from [0, 255] down to [-0.5, 0.5].
"""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
bytestream.read(16)
buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)
data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)
if norm_shift:
data = data - (PIXEL_DEPTH / 2.0)
if norm_scale:
data = data / PIXEL_DEPTH
data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)
data = numpy.reshape(data, [num_images, -1])
return data
# Extract the labels
def extract_labels(filename, num_images):
"""Extract the labels into a vector of int64 label IDs."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
bytestream.read(8)
buf = bytestream.read(1 * num_images)
labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)
num_labels_data = len(labels)
one_hot_encoding = numpy.zeros((num_labels_data,NUM_LABELS))
one_hot_encoding[numpy.arange(num_labels_data),labels] = 1
one_hot_encoding = numpy.reshape(one_hot_encoding, [-1, NUM_LABELS])
return one_hot_encoding
# Augment training data
def expend_training_data(images, labels):
expanded_images = []
expanded_labels = []
j = 0 # counter
for x, y in zip(images, labels):
j = j+1
if j%100==0:
print ('expanding data : %03d / %03d' % (j,numpy.size(images,0)))
# register original data
expanded_images.append(x)
expanded_labels.append(y)
# get a value for the background
# zero is the expected value, but median() is used to estimate background's value
bg_value = numpy.median(x) # this is regarded as background's value
image = numpy.reshape(x, (-1, 28))
for i in range(4):
# rotate the image with random degree
angle = numpy.random.randint(-15,15,1)
new_img = ndimage.rotate(image,angle,reshape=False, cval=bg_value)
# shift the image with random distance
shift = numpy.random.randint(-2, 2, 2)
new_img_ = ndimage.shift(new_img,shift, cval=bg_value)
# register new training data
expanded_images.append(numpy.reshape(new_img_, 784))
expanded_labels.append(y)
# images and labels are concatenated for random-shuffle at each epoch
# notice that pair of image and label should not be broken
expanded_train_total_data = numpy.concatenate((expanded_images, expanded_labels), axis=1)
numpy.random.shuffle(expanded_train_total_data)
return expanded_train_total_data
# Prepare MNISt data
def prepare_MNIST_data(use_norm_shift=False, use_norm_scale=True, use_data_augmentation=False):
# Get the data.
train_data_filename = maybe_download('train-images-idx3-ubyte.gz')
train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')
test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')
test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')
# Extract it into numpy arrays.
train_data = extract_data(train_data_filename, 60000, use_norm_shift, use_norm_scale)
train_labels = extract_labels(train_labels_filename, 60000)
test_data = extract_data(test_data_filename, 10000, use_norm_shift, use_norm_scale)
test_labels = extract_labels(test_labels_filename, 10000)
# Generate a validation set.
validation_data = train_data[:VALIDATION_SIZE, :]
validation_labels = train_labels[:VALIDATION_SIZE,:]
train_data = train_data[VALIDATION_SIZE:, :]
train_labels = train_labels[VALIDATION_SIZE:,:]
# Concatenate train_data & train_labels for random shuffle
if use_data_augmentation:
train_total_data = expend_training_data(train_data, train_labels)
else:
train_total_data = numpy.concatenate((train_data, train_labels), axis=1)
train_size = train_total_data.shape[0]
return train_total_data, train_size, validation_data, validation_labels, test_data, test_labels