def load_data():
"""Loads CIFAR10 dataset.
Returns:
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
"""
dirname = 'cifar-10-batches-py'
origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
path = get_file(dirname, origin=origin, untar=True)
num_train_samples = 50000
x_train = np.zeros((num_train_samples, 3, 32, 32), dtype='uint8')
y_train = np.zeros((num_train_samples,), dtype='uint8')
for i in range(1, 6):
fpath = os.path.join(path, 'data_batch_' + str(i))
data, labels = load_batch(fpath)
x_train[(i - 1) * 10000:i * 10000, :, :, :] = data
y_train[(i - 1) * 10000:i * 10000] = labels
fpath = os.path.join(path, 'test_batch')
x_test, y_test = load_batch(fpath)
y_train = np.reshape(y_train, (len(y_train), 1))
y_test = np.reshape(y_test, (len(y_test), 1))
if K.image_data_format() == 'channels_last':
x_train = x_train.transpose(0, 2, 3, 1)
x_test = x_test.transpose(0, 2, 3, 1)
return (x_train, y_train), (x_test, y_test)
def load_data():
"""Loads CIFAR10 dataset.
Returns:
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
"""
dirname = 'data/'
# origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
path = dirname # get_file(dirname, origin=origin, untar=True)
num_train_samples = 50000
x_train = np.zeros((num_train_samples, 3, 32, 32), dtype='uint8')
y_train = np.zeros((num_train_samples,), dtype='uint8')
for i in range(1, 6):
fpath = os.path.join(path, 'data_batch_' + str(i))
data, labels = load_batch(fpath)
x_train[(i - 1) * 10000:i * 10000, :, :, :] = data
y_train[(i - 1) * 10000:i * 10000] = labels
fpath = os.path.join(path, 'test_batch')
x_test, y_test = load_batch(fpath)
y_train = np.reshape(y_train, (len(y_train), 1))
y_test = np.reshape(y_test, (len(y_test), 1))
if K.image_data_format() == 'channels_last':
x_train = x_train.transpose(0, 2, 3, 1)
x_test = x_test.transpose(0, 2, 3, 1)
x_test = 1.0 - x_test / 255.0
x_train = 1.0 - x_train / 255.0
return (x_train, y_train), (x_test, y_test)
def load_data(label_mode='fine'):
"""Loads CIFAR100 dataset.
Arguments:
label_mode: one of "fine", "coarse".
Returns:
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
Raises:
ValueError: in case of invalid `label_mode`.
"""
if label_mode not in ['fine', 'coarse']:
raise ValueError('`label_mode` must be one of `"fine"`, `"coarse"`.')
dirname = 'cifar-100-python'
origin = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
path = get_file(dirname, origin=origin, untar=True)
fpath = os.path.join(path, 'train')
x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels')
fpath = os.path.join(path, 'test')
x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels')
y_train = np.reshape(y_train, (len(y_train), 1))
y_test = np.reshape(y_test, (len(y_test), 1))
if K.image_data_format() == 'channels_last':
x_train = x_train.transpose(0, 2, 3, 1)
x_test = x_test.transpose(0, 2, 3, 1)
return (x_train, y_train), (x_test, y_test)
def load_data(label_mode='fine'):
"""Loads CIFAR100 dataset.
Arguments:
label_mode: one of "fine", "coarse".
Returns:
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
Raises:
ValueError: in case of invalid `label_mode`.
"""
if label_mode not in ['fine', 'coarse']:
raise ValueError('label_mode must be one of "fine" "coarse".')
dirname = 'cifar-100-python'
origin = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
path = get_file(dirname, origin=origin, untar=True)
fpath = os.path.join(path, 'train')
x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels')
fpath = os.path.join(path, 'test')
x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels')
y_train = np.reshape(y_train, (len(y_train), 1))
y_test = np.reshape(y_test, (len(y_test), 1))
if K.image_data_format() == 'channels_last':
x_train = x_train.transpose(0, 2, 3, 1)
x_test = x_test.transpose(0, 2, 3, 1)
return (x_train, y_train), (x_test, y_test)
def load_cifar100_data(label_mode='fine'):
"""loads cifar100 dataset.
arguments:
label_mode: one of "fine", "coarse".
returns:
tuple of numpy arrays: `(x_train, y_train), (x_test, y_test)`.
raises:
valueerror: in case of invalid `label_mode`.
"""
if label_mode not in ['fine', 'coarse']:
raise valueerror('label_mode must be one of "fine" "coarse".')
print('load cifar100 dataset')
dirname = 'cifar-100-python'
origin = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
path = get_file(dirname, origin=origin, untar=True)
fpath = os.path.join(path, 'train')
x_train, y_train = load_batch(fpath, label_key=label_mode + '_labels')
fpath = os.path.join(path, 'test')
x_test, y_test = load_batch(fpath, label_key=label_mode + '_labels')
y_train = np.reshape(y_train, (len(y_train), 1))
y_test = np.reshape(y_test, (len(y_test), 1))
if K.image_data_format() == 'channels_last':
x_train = x_train.transpose(0, 2, 3, 1)
x_test = x_test.transpose(0, 2, 3, 1)
y_test = map(one_hot_vec_100, y_test)
y_train = map(one_hot_vec_100, y_train)
return (x_train, y_train, x_test, y_test)
def load_data():
dirname = 'data/'
path = dirname
num_train_samples = 50000
x_train = np.zeros((num_train_samples, 3, 32, 32), dtype='uint8')
y_train = np.zeros((num_train_samples, ), dtype='uint8')
for i in range(1, 6):
fpath = os.path.join(path, 'data_batch_' + str(i))
data, labels = load_batch(fpath)
x_train[(i - 1) * 10000:i * 10000, :, :, :] = data
y_train[(i - 1) * 10000:i * 10000] = labels
fpath = os.path.join(path, 'test_batch')
x_test, y_test = load_batch(fpath)
y_train = np.reshape(y_train, (len(y_train), 1))
y_test = np.reshape(y_test, (len(y_test), 1))
if K.image_data_format() == 'channels_last':
x_train = x_train.transpose(0, 2, 3, 1)
x_test = x_test.transpose(0, 2, 3, 1)
x_test = 1.0 - x_test / 255.0
x_train = 1.0 - x_train / 255.0
return (x_train, y_train), (x_test, y_test) - x_train / 255.0
return (x_train, y_train), (x_test, y_test)