BatchDataset 显示图像和标签

Question

我有一个Train 和Validation 批处理数据集：

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    train_path,
    label_mode = 'categorical', #it is used for multiclass classification. It is one hot encoded labels for each class
    validation_split = 0.2,     #percentage of dataset to be considered for validation
    subset = "training",        #this subset is used for training
    seed = 1337,                # seed is set so that same results are reproduced
    image_size = img_size,      # shape of input images
    batch_size = batch_size,    # This should match with model batch size
)

valid_ds = tf.keras.preprocessing.image_dataset_from_directory(
    train_path,
    label_mode ='categorical',
    validation_split = 0.2,
    subset = "validation",      #this subset is used for validation
    seed = 1337,
    image_size = img_size,
    batch_size = batch_size,
)

我试图显示 9 张图像以显示它们的外观，我设法做到了，但我似乎无法绘制它们各自的标签。

代码如下：

class_names = train_ds.class_names


plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
  for i in range(9):
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(images[i].numpy().astype("uint8"))
    plt.axis("off")

显示这个：

如果我尝试通过添加来获取标签：plt.title(class_names[labels[i]])

我收到以下错误：TypeError: only integer scalar arrays can be converted to a scalar index

我已经尝试过其他帖子的解决方案，例如以下plt.title(class_names[labels[i][0]])，但没有任何成功。

当我打印标签[i]时，我得到标签的一个热编码......也许这就是为什么？

结果代码：

plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
  for i in range(9):
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(images[i].numpy().astype("uint8"))
    plt.title(class_names[np.argmax(labels[i], axis=None, out=None)])
    plt.axis("off")

Answer 1

根据您上次的评论，您是否尝试过使用 argmax？

numpy.argmax(a, axis=None, out=None)

这将返回沿轴的最大值的索引。