我一直在处理一个多类分类问题，需要创建一个函数来显示Fashion MNIST数据集中特定类别的图像并对其进行预测。例如，绘制3张T-shirt类别的图像及其预测结果。我尝试了不同的方法但尚未成功。我缺少一个条件语句，并且不知道如何以及在哪里在我的函数中实现它。
这是我目前想到的方案：
# Make function to plot imagedef plot_image(indx, predictions, true_labels, target_images): """ Picks an image, plots it and labels it with a predicted and truth label. Args: indx: index number to find the image and its true label. predictions: model predictions on test data (each array is a predicted probability of values between 0 to 1). true_labels: array of ground truth labels for images. target_images: images from the test data (in tensor form). Returns: A plot of an image from `target_images` with a predicted class label as well as the truth class label from `true_labels`. """ # Set target image target_image = target_images[indx] # Truth label true_label = true_labels[indx] # Predicted label predicted_label = np.argmax(predictions) # find the index of max value # Show image plt.imshow(target_image, cmap=plt.cm.binary) plt.xticks([]) plt.yticks([]) # Set colors for right or wrong predictions if predicted_label == true_label: color = 'green' else: color = 'red' # Labels appear on the x-axis along with accuracy % plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label], 100*np.max(predictions), class_names[true_label]), color=color)# Function to display image of a classdef display_image(class_indx): # Set figure size plt.figure(figsize=(10,10)) # Set class index class_indx = class_indx # Display 3 images for i in range(3): plt.subplot(1, 3, i+1) # plot_image function plot_image(indx=class_indx, predictions=y_probs[class_indx], true_labels=test_labels, target_images=test_images_norm)这些是类别名称 'T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'。当我调用显示函数display_image()并传递类别索引作为参数display_image(class_indx=15)时，我得到了相同的图像和相同的预测结果三次（*注意我的错误方法，我传递的是索引号而不是类别名称*）。我需要一个函数，该函数接受一个str（类别名称）并显示该类别的3个不同预测结果。例如，display_image('Dress')应该返回3张Dress类别的图像以及我的模型对其进行的3个不同预测结果，如Prediction#1 (65%)、Prediction#2 (100%)、Prediction#3 (87%)。谢谢！
回答：
我认为你已经非常接近解决你的问题了。你只需要从你感兴趣的类别中随机抽取三个样本。我猜你已经使用了le = LabelEncoder()来编码你的目标向量。如果是的，那么你将拥有这样的类别：labels = list(le.classes_)。然后我会这样做：
def display_image(class_of_interest: str, nb_samples: int=3): plt.figure(figsize=(10,10)) class_indx = class_names.index(class_of_interest) target_idx = np.where(true_labels==class_indx)[0] imgs_idx = np.random.choice(target_idx, nb_samples, replace=False) for i in range(nb_samples): plt.subplot(1, nb_samples, i+1) plot_image(indx=imgs_idx[i], predictions=y_probs[imgs_idx[i]], true_labels=test_labels, target_images=test_images_norm)