

def load_mnist():
    print('Loading training data...')
    import imp, urllib
    partial_flatten = (lambda x: np.reshape(partial_flatten, (x.shape[0], np.prod(x.shape[1:]))))
    one_hot = (lambda x, K: np.array((x[:, None] == np.arange(K)[None, :]), dtype=int))
    (source, _) = urllib.urlretrieve('https://raw.githubusercontent.com/HIPS/Kayak/master/examples/data.py')
    data = imp.load_source('data', source).mnist()
    (train_images, train_labels, test_images, test_labels) = data
    train_images = (partial_flatten(train_images) / 255.0)
    test_images = (partial_flatten(test_images) / 255.0)
    train_labels = one_hot(train_labels, 10)
    test_labels = one_hot(test_labels, 10)
    N_data = train_images.shape[0]
    return (N_data, train_images, train_labels, test_images, test_labels)

dataset/ETHPy150Open HIPS/autograd/examples/neural_net.py load_mnist/VarMisuse@29/40 `x`->`partial_flatten`