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auto2d_tutorial [2019/05/28 22:13]
twagner [Training] 		auto2d_tutorial [2019/08/15 15:36]
twagner [Classify]
Line 7: Line 7:
 I suppose you downloaded the latest classification model. I suppose you downloaded the latest classification model.
  
-This is the corresponding configuration file: +To run the classification I suppose you want to separate good and bad classes in classes_after_isac.hdf (or any other .mrcs / .hdf file with classes) and you want to save your new .hdf (.mrcs) files into the folder ''output_folder''. Furthermore you want to use the model ''model.h5'' and the GPU with ID=1. Classes with a confidence bigger than 0.7 should be classified as good class.
- +
-<code json config.json> +
-{ +
- "model": { +
- "input_size": [75,75] +
- }, +
- +
- "train": { +
- "batch_size": 32, +
- "good_classes": "GOOD_CLASSES/", +
- "bad_classes": "BAD_CLASSES/", +
- "pretrained_weights": "", +
- "saved_weights_name": "my_model.h5", +
- "learning_rate": 1e-4, +
- "nb_epoch": 100, +
- "nb_early_stop": 5 +
- } +
-} +
-</code> +
- +
-Copy this into a new file called ''config.json''.  +
- +
-To run the classification I suppose you want to seperate good and bad classes in classes_after_isac.hdf and you want to save your new .hdf files into the folder ''output_folder''. Furthermore you want to use the model ''model.h5'' and the GPU with ID=1. Classes with a confidence bigger than 0.7 should be classified as good class.+
  
 This is the command to run: This is the command to run:
  
 <code> <code>
-sp_cinderella_predict.py -i path/to/classes_after_isac.hdf -w model.h5 -o output_folder/ -c config.json -t 0.7 --gpu 1+sp_cinderella_predict.py -i path/to/classes_after_isac.hdf -w model.h5 -o output_folder/ -t 0.7 --gpu 1
 </code> </code>
  
Line 60: Line 37:
  "learning_rate": 1e-4,  "learning_rate": 1e-4,
  "nb_epoch": 100,  "nb_epoch": 100,
- "nb_early_stop": 5+ "nb_early_stop": 15
  }  }
 } }
auto2d_tutorial.txt · Last modified: 2020/08/28 07:36 by twagner

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