from cs231n.data_utils import load_CIFAR10def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):    """    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare    it for the two-layer neural net classifier. These are the same steps as    we used for the SVM, but condensed to a single function.      """    # Load the raw CIFAR-10 data    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)            # Subsample the data    mask = range(num_training, num_training + num_validation)    X_val = X_train[mask]    y_val = y_train[mask]    mask = range(num_training)    X_train = X_train[mask]    y_train = y_train[mask]    mask = range(num_test)    X_test = X_test[mask]    y_test = y_test[mask]    # Normalize the data: subtract the mean image    mean_image = np.mean(X_train, axis=0)    X_train -= mean_image    X_val -= mean_image    X_test -= mean_image    # Reshape data to rows    X_train = X_train.reshape(num_training, -1)    X_val = X_val.reshape(num_validation, -1)    X_test = X_test.reshape(num_test, -1)    return X_train, y_train, X_val, y_val, X_test, y_test# Invoke the above function to get our data.X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()print 'Train data shape: ', X_train.shapeprint 'Train labels shape: ', y_train.shapeprint 'Validation data shape: ', X_val.shapeprint 'Validation labels shape: ', y_val.shapeprint 'Test data shape: ', X_test.shapeprint 'Test labels shape: ', y_test.shape

import cPickle as pickleimport numpy as npimport osdef load_CIFAR_batch(filename):  """ load single batch of cifar """  with open(filename, 'rb') as f:    datadict = pickle.load(f)    X = datadict['data']    Y = datadict['labels']    X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")    Y = np.array(Y)    return X, Ydef load_CIFAR10(ROOT):  """ load all of cifar """  xs = []  ys = []  for b in range(1,6):    f = os.path.join(ROOT, 'data_batch_%d' % (b, ))    X, Y = load_CIFAR_batch(f)    xs.append(X)    ys.append(Y)      Xtr = np.concatenate(xs)  Ytr = np.concatenate(ys)  del X, Y  Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))  return Xtr, Ytr, Xte, Yte