mnist_loader.py

"""
mnist_loader
~~~~~~~~~~~~
A library to load the MNIST image data.  For details of the data
structures that are returned, see the doc strings for ``load_data``
and ``load_data_wrapper``.  In practice, ``load_data_wrapper`` is the
function usually called by our neural network code.
"""

#### Libraries
# Standard library
import _pickle as cPickle
import gzip

# Third-party libraries
import numpy as np
from scipy import ndimage

def load_data():
    """Return the MNIST data as a tuple containing the training data,
    the validation data, and the test data.
    The ``training_data`` is returned as a tuple with two entries.
    The first entry contains the actual training images.  This is a
    numpy ndarray with 50,000 entries.  Each entry is, in turn, a
    numpy ndarray with 784 values, representing the 28 * 28 = 784
    pixels in a single MNIST image.
    The second entry in the ``training_data`` tuple is a numpy ndarray
    containing 50,000 entries.  Those entries are just the digit
    values (0...9) for the corresponding images contained in the first
    entry of the tuple.
    The ``validation_data`` and ``test_data`` are similar, except
    each contains only 10,000 images.
    This is a nice data format, but for use in neural networks it's
    helpful to modify the format of the ``training_data`` a little.
    That's done in the wrapper function ``load_data_wrapper()``, see
    below.
    """
    f = gzip.open('data/mnist.pkl.gz', 'rb')
    training_data, validation_data, test_data = cPickle.load(f, encoding='latin1')
    f.close()
    return (training_data, validation_data, test_data)

def load_data_wrapper(augmentation=False):
    """Return a tuple containing ``(training_data, validation_data,
    test_data)``. Based on ``load_data``, but the format is more
    convenient for use in our implementation of neural networks.
    In particular, ``training_data`` is a list containing 50,000
    2-tuples ``(x, y)``.  ``x`` is a 784-dimensional numpy.ndarray
    containing the input image.  ``y`` is a 10-dimensional
    numpy.ndarray representing the unit vector corresponding to the
    correct digit for ``x``.
    ``validation_data`` and ``test_data`` are lists containing 10,000
    2-tuples ``(x, y)``.  In each case, ``x`` is a 784-dimensional
    numpy.ndarry containing the input image, and ``y`` is the
    corresponding classification, i.e., the digit values (integers)
    corresponding to ``x``.
    Obviously, this means we're using slightly different formats for
    the training data and the validation / test data.  These formats
    turn out to be the most convenient for use in our neural network
    code."""
    tr_d, va_d, te_d = load_data()
    training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]
    training_results = [vectorized_result(y) for y in tr_d[1]]
    if augmentation: training_inputs, training_results = expend_training_data(training_inputs, training_results)
    training_data = list(zip(training_inputs, training_results))
    validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]
    validation_data = list(zip(validation_inputs, va_d[1]))
    test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]
    test_data = list(zip(test_inputs, te_d[1]))
    return (training_data, validation_data, test_data)


def vectorized_result(j):
    """Return a 10-dimensional unit vector with a 1.0 in the jth
    position and zeroes elsewhere.  This is used to convert a digit
    (0...9) into a corresponding desired output from the neural
    network."""
    e = np.zeros((10, 1))
    e[j] = 1.0
    return e

# 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,np.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 = np.median(x) # this is regarded as background's value
        image = np.reshape(x, (-1, 28))

        for i in range(4):

            ## Augmentation via Rotation
            # rotate the image with random degree
            angle = np.random.randint(-15,15,1)
            new_img = ndimage.rotate(image,angle,reshape=False, cval=bg_value)

            # shift the image with random distance
            shift = np.random.randint(-2, 2, 2)
            new_img_ = ndimage.shift(new_img,shift, cval=bg_value)

            # register new training data
            expanded_images.append(np.reshape(new_img_, [784,1]))
            expanded_labels.append(y)


    return expanded_images, expanded_labels