def load_mnist(shuffle_data=True, randseed='default'):
train_path = get_file("mnist_train.libsvm", origin=remote_data_dir() + "/mnist_train.libsvm",
cache_subdir="demo")
test_path = get_file("mnist_test.libsvm", origin=remote_data_dir() + "/mnist_test.libsvm",
cache_subdir="demo")
x_train, y_train = load_svmlight_file(train_path, n_features=784)
x_test, y_test = load_svmlight_file(test_path, n_features=784)
x_train = x_train.toarray() / 255.0
x_test = x_test.toarray() / 255.0
if shuffle_data:
shuffle(x_train, y_train, randseed=randseed)
return (x_train, y_train), (x_test, y_test)
def load_cifar10(shuffle_data=True, randseed='default'):
train_path = get_file("cifar10_5k_train.pkl",
origin=remote_data_dir() + "/cifar10_5k_train.pkl",
cache_subdir="demo")
test_path = get_file("cifar10_1k_test.pkl",
origin=remote_data_dir() + "/cifar10_1k_test.pkl",
cache_subdir="demo")
tmp = pickle.load(open(train_path, "rb"))
x_train, y_train = tmp['data'] / 255.0, tmp['labels']
tmp = pickle.load(open(test_path, "rb"))
x_test, y_test = tmp['data'] / 255.0, tmp['labels']
if shuffle_data:
shuffle(x_train, y_train, randseed=randseed)
return (x_train, y_train), (x_test, y_test)
def load_housing(shuffle_data=True, randseed='default'):
train_path = get_file("housing_scale_train.libsvm",
origin=remote_data_dir() + "/housing_scale_train.libsvm",
cache_subdir="demo")
test_path = get_file("housing_scale_test.libsvm",
origin=remote_data_dir() + "/housing_scale_test.libsvm",
cache_subdir="demo")
x_train, y_train = load_svmlight_file(train_path, n_features=13)
x_test, y_test = load_svmlight_file(test_path, n_features=13)
x_train = x_train.toarray()
x_test = x_test.toarray()
if shuffle_data:
shuffle(x_train, y_train, randseed=randseed)
return (x_train, y_train), (x_test, y_test)