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import matplotlib.pyplot as pltfrom six.moves import cPickle as pickleimport platformimport osimport numpy as npdef load_pickle(f):    version = platform.python_version_tuple()    if version[0] == '2':        return pickle.load(f)    elif version[0] == '3':        return pickle.load(f, encoding='latin1')    raise ValueError("invalid python version: {}".format(version))def load_CIFAR_batch(filename):    """ load single batch of cifar """    with open(filename, 'rb') as f:        datadict = load_pickle(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, Ytedef get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000,                     subtract_mean=True):    """    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare    it for classifiers. 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 = list(range(num_training, num_training + num_validation))    X_val = X_train[mask]    y_val = y_train[mask]    mask = list(range(num_training))    X_train = X_train[mask]    y_train = y_train[mask]    mask = list(range(num_test))    X_test = X_test[mask]    y_test = y_test[mask]    # Normalize the data: subtract the mean image    if subtract_mean:        mean_image = np.mean(X_train, axis=0)        X_train -= mean_image        X_val -= mean_image        X_test -= mean_image    # Transpose so that channels come first    X_train = X_train.transpose(0, 3, 1, 2).copy()    X_val = X_val.transpose(0, 3, 1, 2).copy()    X_test = X_test.transpose(0, 3, 1, 2).copy()    # Package data into a dictionary    return {        'X_train': X_train, 'y_train': y_train,        'X_val': X_val, 'y_val': y_val,        'X_test': X_test, 'y_test': y_test,    }# Load the raw CIFAR-10 data.cifar10_dir = 'dataset/cifar-10-batches-py'X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)# As a sanity check, we print out the size of the training and test data.print('Training data shape: ', X_train.shape)print('Training labels shape: ', y_train.shape)print('Test data shape: ', X_test.shape)print('Test labels shape: ', y_test.shape)# Visualize some examples from the dataset.# We show a few examples of training images from each class.classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']num_classes = len(classes)samples_per_class = 7for y, cls in enumerate(classes):    idxs = np.flatnonzero(y_train == y)    idxs = np.random.choice(idxs, samples_per_class, replace=False)    for i, idx in enumerate(idxs):        plt_idx = i * num_classes + y + 1        plt.subplot(samples_per_class, num_classes, plt_idx)        plt.imshow(X_train[idx].astype('uint8'))        plt.axis('off')        if i == 0:            plt.title(cls)plt.show()

 

参考cs231n代码

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