深度学习实战 cifar数据集预处理技术分析

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 = 'https://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.empty((num_train_samples, 3, 32, 32), dtype='uint8')

y_train = np.empty((num_train_samples,), dtype='uint8')

for i in range(1, 6):

fpath = os.path.join(path, 'data_batch_' + str(i))

(x_train[(i - 1) * 10000: i * 10000, :, :, :],

y_train[(i - 1) * 10000: i * 10000]) = load_batch(fpath)

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_batch(fpath, label_key='labels'):

"""Internal utility for parsing CIFAR data.

# Arguments

fpath: path the file to parse.

label_key: key for label data in the retrieve

dictionary.

# Returns

A tuple `(data, labels)`.

"""

with open(fpath, 'rb') as f:

if sys.version_info < (3,):

d = cPickle.load(f)

else:

d = cPickle.load(f, encoding='bytes')

# decode utf8

d_decoded = {}

for k, v in d.items():

d_decoded[k.decode('utf8')] = v

d = d_decoded

data = d['data']

labels = d[label_key]

data = data.reshape(data.shape[0], 3, 32, 32)

return data, labels

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