reshapeエラー/ ValueError：新しい配列の合計サイズは変更しないでください

Question

私はCNNを使って画像を分類するコードを持っていますので、トレーニングデータセットとテストデータセットがあります。私はシステムを実行すると、私はこのエラーを持っている：

ValueError        Traceback (most recent call last) 
<ipython-input-44-cb7ec1a13881> in <module>() 
     1 optimize(num_iterations=1) 
     2 
----> 3 print_validation_accuracy() 

<ipython-input-43-7f1a17e48e41> in print_validation_accuracy(show_example_errors, show_confusion_matrix) 
    21 
    22   # Get the images from the test-set between index i and j. 
---> 23   images = data.valid.images[i:j, :].reshape(batch_size, img_size_flat) 
    24   #images = data.valid.images[i:j, :].reshape(1, 128) 
    25 

ValueError: total size of new array must be unchanged 

と、このエラーを歳差運動コードの手順は次のとおりです。

def print_validation_accuracy(show_example_errors=False, 
         show_confusion_matrix=False): 

    # Number of images in the test-set. 
    num_test = len(data.valid.images) 

    # Allocate an array for the predicted classes which 
    # will be calculated in batches and filled into this array. 
    cls_pred = np.zeros(shape=num_test, dtype=np.int) 

    # Now calculate the predicted classes for the batches. 
    # We will just iterate through all the batches. 
    # There might be a more clever and Pythonic way of doing this. 

    # The starting index for the next batch is denoted i. 
    i = 0 

    while i < num_test: 
     # The ending index for the next batch is denoted j. 
     j = min(i + batch_size, num_test) 

     # Get the images from the test-set between index i and j. 
     images = data.valid.images[i:j, :].reshape(batch_size, img_size_flat) 

     # Get the associated labels. 
     labels = data.valid.labels[i:j, :] 

     # Create a feed-dict with these images and labels. 
     feed_dict = {x: images, 
        y_true: labels} 

     # Calculate the predicted class using TensorFlow. 
     cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict) 

     # Set the start-index for the next batch to the 
     # end-index of the current batch. 
     i = j 

    cls_true = np.array(data.valid.cls) 
    cls_pred = np.array([classes[x] for x in cls_pred]) 

    # Create a boolean array whether each image is correctly classified. 
    correct = (cls_true == cls_pred) 

    # Calculate the number of correctly classified images. 
    # When summing a boolean array, False means 0 and True means 1. 
    correct_sum = correct.sum() 

    # Classification accuracy is the number of correctly classified 
    # images divided by the total number of images in the test-set. 
    acc = float(correct_sum)/num_test 

    # Print the accuracy. 
    msg = "Accuracy on Test-Set: {0:.1%} ({1}/{2})" 
    print(msg.format(acc, correct_sum, num_test)) 

    # Plot some examples of mis-classifications, if desired. 
    if show_example_errors: 
     print("Example errors:") 
     plot_example_errors(cls_pred=cls_pred, correct=correct) 

    # Plot the confusion matrix, if desired. 
    if show_confusion_matrix: 
     print("Confusion Matrix:") 
     plot_confusion_matrix(cls_pred=cls_pred) 

誰も私を助けてくださいことはできますか？

Answer 1

エラーメッセージに示されているように、このステートメントでは、あなたの形状に不一致があります。 *（data.valid.imagesのCOLUMN_SIZE）* img_size_flatをBATCH_SIZEと等しくない - 何が起こっている

images = data.valid.images[i:j, :].reshape(batch_size, img_size_flat) 

は、この式が等しいすなわち （I〜J）ではないです。 同じにして、問題を解決します。