Add a directory iterator option to allow to work easily with autoencoders (issue #4260) #6510
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fchollet
reviewed
May 4, 2017
keras/preprocessing/image.py
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| @@ -972,7 +975,9 @@ def next(self): | |||
| format=self.save_format) | |||
| img.save(os.path.join(self.save_to_dir, fname)) | |||
| # build batch of labels | |||
| if self.class_mode == 'sparse': | |||
| if self.class_mode == 'identical': | |||
| batch_y = batch_x.copy() | |||
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Is the copy absolutely necessary? Wondering.
keras/preprocessing/image.py
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| @@ -838,6 +838,7 @@ class DirectoryIterator(Iterator): | |||
| `"binary"`: binary targets (if there are only two classes), | |||
| `"categorical"`: categorical targets, | |||
| `"sparse"`: integer targets, | |||
| `"identical"`: image targets identical to input images, | |||
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What would be some other keyword options? identity, input, autoregression?
Additionally, the explanation can be made clearer.
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I like About the copy, I think that not doing it can lead to pretty subtle bug to find in the code. I can imagine a usecase where someone uses flow_from_directory to read images from the disk, makes operations on the image and try to compare the original one and modified one. If the copy is not made, the changes made to the image will also affect the original. |
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LGTM |
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This patch adds a new class_mode to keras.preprocessing.image.ImageDataGenerator.flow_from_directory method.
This new class_mode is 'identical' and it allows to generate minibatches where the target values are identical to input values. It makes working with autoencoders trivial. This issue has been described in #4260. The patch also contains tests that ensure that the input images are not the same numpy array as target images.