如何閱讀Tensorflow中的CIFAR-10數據集？

Question

任何人都可以給一個乾淨的代碼加載CIFAR-10張量？

我已經檢查的tensorflow的GitHub庫給出的例子。但我不想將圖像大小調整爲24x24。基本上，我正在尋找更簡單，更簡單的代碼。

Answer 1

請看看下面github頁面，在這裏我已經做到了這一點。如果上述鏈接失敗，請按照kgeorge.github.io的指示行事，並查看筆記本tf_cifar.ipynb。我試圖使用嬰兒步驟加載cifar-10數據。請查找函數load_and_preprocess_input

該代碼中的以下函數接受數據爲（nsamples，32x32x3）float32的np數組，並標記爲nsamples int32的np數組，並預處理要由tensorflow消耗的數據訓練。

image_depth=3 
image_height=32 
image_width=32 
#data = (nsamples, 32x32x3) float32 
#labels = (nsamples) int32 
def prepare_input(data=None, labels=None): 
    global image_height, image_width, image_depth 
    assert(data.shape[1] == image_height * image_width * image_depth) 
    assert(data.shape[0] == labels.shape[0]) 
    #do mean normaization across all samples 
    mu = np.mean(data, axis=0) 
    mu = mu.reshape(1,-1) 
    sigma = np.std(data, axis=0) 
    sigma = sigma.reshape(1, -1) 
    data = data - mu 
    data = data/sigma 
    is_nan = np.isnan(data) 
    is_inf = np.isinf(data) 
    if np.any(is_nan) or np.any(is_inf): 
     print('data is not well-formed : is_nan {n}, is_inf: {i}'.format(n= np.any(is_nan), i=np.any(is_inf))) 
    #data is transformed from (no_of_samples, 3072) to (no_of_samples , image_height, image_width, image_depth) 
    #make sure the type of the data is no.float32 
    data = data.reshape([-1,image_depth, image_height, image_width]) 
    data = data.transpose([0, 2, 3, 1]) 
    data = data.astype(np.float32) 
    return data, labels