调整CIFAR10图像大小时获取内存错误

Question

def load_cifar10_data(img_rows, img_cols):

    # Load cifar10 training and validation sets
    (X_train, Y_train), (X_valid, Y_valid) = cifar10.load_data()

    # Resize training images
    X_train = np.array([cv2.resize(img, (img_rows,img_cols)) for img in X_train[:,:,:,:]])
    X_valid = np.array([cv2.resize(img, (img_rows,img_cols)) for img in X_valid[:,:,:,:]])

    # Transform targets to keras compatible format
    Y_train = np_utils.to_categorical(Y_train, num_classes)
    Y_valid = np_utils.to_categorical(Y_valid, num_classes)

    X_train = X_train.astype('float32')
    X_valid = X_valid.astype('float32')

    # preprocess data
    X_train = X_train / 255.0
    X_valid = X_valid / 255.0

    return X_train, Y_train, X_valid, Y_valid
X_train, y_train, X_test, y_test = load_cifar10_data(224, 224)

获取内存错误 如果我在google colab中运行这个程序，内存就会增加，笔记本就会崩溃

Answer 1

这是因为你使用的内存超过了GoogleColab的可用内存限制。CIFAR-10大约有60000个图像。这大约相当于（60000 x 8（浮点=8字节）x 224 x 224 x 3（如果图像为RGB格式））=7225344000字节=67.29 GB。GoogleColab上的内存限制为12GB。您可以将图像大小调整为较小的大小，也可以减少图像的数量