PaddlePaddle在预测图像时出现错误EnforceNotMet: Conv intput should be 4-D or 5-D tensor

BUG:
Expected in_dims.size() == 4 || in_dims.size() == 5 == true, but received in_dims.size() == 4 || in_dims.size() == 5:0 != true:1.

paddle教程地址: https://www.paddlepaddle.org.cn/tutorials/projectdetail/2182025

在运行Paddle教程的时候,会出现在数据加载的时候,数据纬度错误维内托,导致在徐念阶段报错

Expected in_dims.size() == 4 || in_dims.size() == 5 == true, but received in_dims.size() == 4 || in_dims.size() == 5:0 != true:1.

只需要修改load_data函数第59行即可
元代码

img = np.array(imgs[i]).astype('float32')

修改为

img = np.array(imgs[i]).astype('float32').reshape(1, 28, 28)

问题来源于PaddlePaddle在预测图像时出现错误EnforceNotMet: Conv intput should be 4-D or 5-D tensor

完整代码如下:

# 定义数据集读取器
def load_data(mode='train'):

    # 加载数据
    datafile = 'data/mnist.json.gz'
    print('loading mnist dataset from {} ......'.format(datafile))
    data = json.load(gzip.open(datafile))
    print('mnist dataset load done')

    # 读取到的数据区分训练集，验证集，测试集
    train_set, val_set, eval_set = data

    # 数据集相关参数，图片高度IMG_ROWS, 图片宽度IMG_COLS
    IMG_ROWS = 28
    IMG_COLS = 28

    if mode == 'train':
        # 获得训练数据集
        imgs, labels = train_set[0], train_set[1]
    elif mode == 'valid':
        # 获得验证数据集
        imgs, labels = val_set[0], val_set[1]
    elif mode == 'eval':
        # 获得测试数据集
        imgs, labels = eval_set[0], eval_set[1]
    else:
        raise Exception("mode can only be one of ['train', 'valid', 'eval']")

    #校验数据
    imgs_length = len(imgs)
    assert len(imgs) == len(labels), 
          "length of train_imgs({}) should be the same as train_labels({})".format(
                  len(imgs), len(labels))

    # 定义数据集每个数据的序号， 根据序号读取数据
    index_list = list(range(imgs_length))
    # 读入数据时用到的batchsize
    BATCHSIZE = 100

    # 定义数据生成器
    def data_generator():
        if mode == 'train':
            random.shuffle(index_list)
        imgs_list = []
        labels_list = []
        for i in index_list:
            img = np.array(imgs[i]).astype('float32').reshape(1, 28, 28)
            label = np.array(labels[i]).astype('float32')
            # 在使用卷积神经网络结构时，uncomment 下面两行代码
            # img = np.reshape(imgs[i], [1, IMG_ROWS, IMG_COLS]).astype('float32')
            # label = np.reshape(labels[i], [1]).astype('float32')
            imgs_list.append(img)
            labels_list.append(label)
            if len(imgs_list) == BATCHSIZE:
                yield np.array(imgs_list), np.array(labels_list)
                imgs_list = []
                labels_list = []

        # 如果剩余数据的数目小于BATCHSIZE，
        # 则剩余数据一起构成一个大小为len(imgs_list)的mini-batch
        if len(imgs_list) > 0:
            yield np.array(imgs_list), np.array(labels_list)

    return data_generator