auto2d_tutorial

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auto2d_tutorial [2019/08/15 15:35]
twagner [Classify]
auto2d_tutorial [2019/08/15 15:36] (current)
twagner [Classify]
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 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.
  • auto2d_tutorial.txt
  • Last modified: 2019/08/15 15:36
  • by twagner