Question

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

    # Number of images in the test-set.
    num_test = len(test_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 + test_batch_size, num_test)

        # Get the images from the test-set between index i and j.
        images = test_images[i:j, :]

        # Get the associated labels.
        labels = test_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

    # Convenience variable for the true class-numbers of the test-set.
    cls_true = test_class_labels

    # 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 = sum(correct)

    # 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)

}

以上代码无法正常工作。每当我从数组中输入其中一个项目时，它就会转到其他部分。

Answer 1

function food(){
     var food_choice = document.getElementById('food-ch').value;
     console.log(food_choice);
     var food_arr = ['tuna','salmon','prawn','chicken'];
     if(food_arr.indexOf(food_choice) != -1){
                $('#display-food').html("You have chosen" +food_choice);
    } else {
                $('#display-food').html("Please try again");
    }
}

JavaScript如果其他方法无法正常工作

1 个答案: