old_contents: from __future__ import absolute_import
  from .cifar import load_batch
  from ..utils.data_utils import get_file
  from .. import backend as K
  import numpy as np
  import os
  
  
  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.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)
  
new_contents: from __future__ import absolute_import
  from .cifar import load_batch
  from ..utils.data_utils import get_file
  from .. import backend as K
  import numpy as np
  import os
  
  
  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)
  
subject: Load data directly. (#8399)