Simple implementation of Generative Adversarial Nets using chainer

import gzip import os

import numpy as np import six from six.moves.urllib import request

parent = 'http://yann.lecun.com/exdb/mnist' train_images = 'train-images-idx3-ubyte.gz' train_labels = 'train-labels-idx1-ubyte.gz' test_images = 't10k-images-idx3-ubyte.gz' test_labels = 't10k-labels-idx1-ubyte.gz' num_train = 60000 num_test = 10000 dim = 784

def load_mnist(images, labels, num): data = np.zeros(num * dim, dtype=np.uint8).reshape((num, dim)) target = np.zeros(num, dtype=np.uint8).reshape((num, ))

with gzip.open(images, 'rb') as f_images,\
        gzip.open(labels, 'rb') as f_labels:
    f_images.read(16)
    f_labels.read(8)
    for i in six.moves.range(num):
        target[i] = ord(f_labels.read(1))
        for j in six.moves.range(dim):
            data[i, j] = ord(f_images.read(1))

return data, target

def download_mnist_data(): print('Downloading {:s}...'.format(train_images)) request.urlretrieve('{:s}/{:s}'.format(parent, train_images), train_images) print('Done') print('Downloading {:s}...'.format(train_labels)) request.urlretrieve('{:s}/{:s}'.format(parent, train_labels), train_labels) print('Done') print('Downloading {:s}...'.format(test_images)) request.urlretrieve('{:s}/{:s}'.format(parent, test_images), test_images) print('Done') print('Downloading {:s}...'.format(test_labels)) request.urlretrieve('{:s}/{:s}'.format(parent, test_labels), test_labels) print('Done')

print('Converting training data...')
data_train, target_train = load_mnist(train_images, train_labels,
                                      num_train)
print('Done')
print('Converting test data...')
data_test, target_test = load_mnist(test_images, test_labels, num_test)
mnist = {}
mnist['data'] = np.append(data_train, data_test, axis=0)
mnist['target'] = np.append(target_train, target_test, axis=0)

print('Done')
print('Save output...')
with open('mnist.pkl', 'wb') as output:
    six.moves.cPickle.dump(mnist, output, -1)
print('Done')
print('Convert completed')

def load_mnist_data(): if not os.path.exists('mnist.pkl'): download_mnist_data() with open('mnist.pkl', 'rb') as mnist_pickle: mnist = six.moves.cPickle.load(mnist_pickle) return mnist