keras模型的分割图像数据集.fit_generator [英] Split Image dataset for keras model.fit_generator
问题描述
我有单个目录,数据集,其中包含图像的子文件夹(标签/类).
这是数据集中动物图像的子文件夹:
我想将数据集分为model.fit_generotar()
的训练集和测试集.
我该怎么做?
使用glob
获取文件路径迭代器.
然后,您可以使用scikit-learn
的train-test拆分来获取火车和测试数据路径(使用stratify
参数来获得与整个数据集中的测试/火车相同的类分布).
结果将是两个路径列表,您可以将其写入适当的测试/培训文件夹,然后可以应用生成器的flow_from_directory
方法.
第二种方法是不使用flow_from_directory
,而是加载训练/测试集(加载所有内容并使用scikit-learn
方法或使用我之前描述的方法),然后使用生成器的flow
方法. /p>
还请注意,您可能不希望将生成器用于测试/验证数据,因为由于您没有固定的验证/测试集,这会使比较准确性变得困难.
>I have single directory, dataset, which contains sub-folders(labels/classes) of images.
Here's the Sub-folders of animal images in dataset:
I want to split the dataset into train and test set for model.fit_generotar()
.
How can I do that?
Use glob
to get file paths iterator.
You can then use scikit-learn
's train-test split to get train and test data paths (use stratify
parameter to get the same class distribution in test/train as in whole dataset).
The result would be two lists of paths, which you can write to appropriate test/train folders, and then you can apply generator's flow_from_directory
method.
EDIT:
The second way would be to not use flow_from_directory
, but load train/test sets (either load everything and use scikit-learn
method or use what I've described before) and then use generator's flow
method.
Also note that you might not want to use generators for test/validation data, since it would make comparing accuracy hard, since you won't have a fixed valid/test set.
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