如何在此代码中实现训练、测试、验证拆分？

Question

所以我被要求实现拆分函数参数：80% 训练，10% 验证和 10% 测试。而且我不明白如何在这里做。请帮忙。谢谢。

    def plot_example(x_raw, y_raw):
  fig, axes = plt.subplots(3, 3)
  i = 0
  for i in range(3):
    for j in range(3):
      imgplot = axes[i,j].imshow(x_raw[i*3 + j], cmap = 'bone')
      axes[i,j].set_title(y_raw[i*3 + j])
      axes[i,j].get_yaxis().set_visible(False)
      axes[i,j].get_xaxis().set_visible(False)
  fig.set_size_inches(18.5, 10.5, forward=True)

## TODO: Implement the split function parameter: 80% train, 10% validation, and 10% test.
(ds_train, ds_val, ds_test), ds_info = tfds.load("colorectal_histology", 
                                           split=[],
                                           as_supervised=True, with_info=True)
df = tfds.as_dataframe(ds_train.shuffle(1000).take(1000), ds_info)

plot_example(df['image'], df['label'])
print(ds_info)

请解释

Answer 1

也许您可以尝试 scikit learn 的实现。

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html