User Tools

Site Tools

auto2d_tutorial

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision 		Previous revision
Next revision Both sides next revision
auto2d_tutorial [2019/07/10 13:46]
twagner [Classify] 		auto2d_tutorial [2019/08/15 15:36]
twagner [Classify]
Line 6: Line 6:
  
 I suppose you downloaded the latest classification model. I suppose you downloaded the latest classification model.
- 
-This is the corresponding configuration file: 
- 
-<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, 
-                "train_valid_thresh":   0.8, 
-                "max_valid_img_per_file": -1 
- } 
-} 
-</code> 
- 
-The fields have the following meaning:  
-  * **input_size**: Size to which the classes are internally downsampled.  
-  * **batch_size**: Number images that used in in one batch during training. 
-  * **good_classes**: Path to folder with good classes saved as stacks in .mrc or .hdf format 
-  * **bad_classes**: Path to folder with bad classes saved as stacks in .mrc or .hdf format 
-  * **pretrained_weights**: Path to a model that should be used to initialize the training. 
-  * **saved_weights_name**: Everytime the network improves in terms of validation loss, it will save the model into the file specified here. 
-  * **learning_rate**: Defines the step size during training. Default should be kept. 
-  * **nb_epoch**: Maximum number of epochs the network will train. It might not reach this number, as Cinderella stops training if it recognize that the validation loss is not improving anymore. 
-  * **nb_early_stop**: If the validation loss did not improve that number in a row, it will stop training. 
-  * **train_valid_thresh**: Fraction of images that are used for training from each stack file. The remaining images are used for validation. 
-  * **max_valid_img_per_file**: Maximum number of validation images per stack file that should be used. -1 means that it is not used. 
- 
-Copy this into a new file called ''config.json''. During classification, the options in the "train" section are ignored. 
  
 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. 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.
Line 50: Line 12:
  
 <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 75: 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 ยท Last modified: 2020/08/28 07:36 by twagner

Page Tools

Except where otherwise noted, content on this wiki is licensed under the following license: GNU Free Documentation License 1.3
GNU Free Documentation License 1.3 Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki