将 tf.keras.utils.image_dataset_from_directory 与标签列表一起使用

我有目录示例中相应数量的文件的标签列表：[1,2,3]

train_ds = tf.keras.utils.image_dataset_from_directory(
  train_path,
  label_mode='int',
  labels = train_labels,
#   validation_split=0.2,
#   subset="training",
  shuffle=False,
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

我收到错误：

ValueError: Expected the lengths of `labels` to match the number of files in the target directory. len(labels) is 51033 while we found 0 files in ../input/jpeg-happywhale-128x128/train_images-128-128/train_images-128-128.

我尝试定义父目录，但在这种情况下我得到 1 个类。

您的数据文件夹可能没有正确的结构。尝试这样的事情：

import numpy
from PIL import Image
import tensorflow as tf

samples = 10
for idx, c in enumerate(['/content/data/class1/', '/content/data/class2/']*samples):
  imarray = numpy.random.rand(100,100,3) * 255
  im = Image.fromarray(imarray.astype('uint8')).convert('RGB')
  im.save('{}result_image{}.png'.format(c, idx))

train_labels = [0]*samples + [1]*samples
train_ds = tf.keras.utils.image_dataset_from_directory(
  '/content/data',
  label_mode='int',
  labels = train_labels,
  shuffle=False,
  seed=123,
  image_size=(100, 100),
  batch_size=4)

for x, y in train_ds.take(1):
  print(x.shape, y)

Found 20 files belonging to 2 classes.
(4, 100, 100, 3) tf.Tensor([0 0 0 0], shape=(4,), dtype=int32)

您的文件夹结构应如下所示：

├── data
│   ├── class2
│   │   ├── result_image5.png
│   │   ├── result_image9.png
│   │   ├── result_image15.png
│   │   ├── result_image13.png
│   │   ├── result_image1.png
│   │   ├── result_image3.png
│   │   ├── result_image11.png
│   │   ├── result_image19.png
│   │   ├── result_image7.png
│   │   └── result_image17.png
│   └── class1
│       ├── result_image14.png
│       ├── result_image8.png
│       ├── result_image12.png
│       ├── result_image18.png
│       ├── result_image16.png
│       ├── result_image6.png
│       ├── result_image2.png
│       ├── result_image10.png
│       ├── result_image4.png
│       └── result_image0.png