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cinderella_tomograms [2019/12/13 15:52]
twagner 		cinderella_tomograms [2019/12/13 16:13]
twagner [Prediction]
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 After your created your training data, you can start the training :-)  After your created your training data, you can start the training :-) 
  
-You need to specifiy all settings into one config file. To do so, create an empty file with+You need to specify all settings into one config file. To do so, create an empty file using
 <code> <code>
 touch config.json touch config.json
 </code> </code>
  
-Copy the following configuration into the file and adapt it to your needs. The only entries you might want to change are the //input_size//, //good_path//, //bad_path// and //pretrained_weights//.+Copy the following configuration into the new file and adapt it to your needs. The only entries you might want to change are the //input_size//, //good_path//, //bad_path// and //pretrained_weights//.
  
 <code json config.json> <code json config.json>
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 </code> </code>
 The fields have the following meaning: The fields have the following meaning:
-  * **input_size**: This is the image size to which each central slice is resized to. +  * **input_size**: Each central slice is resized to these dimensions. 
-  * **batch_size**: How many images are in one mini-batch. If you have memory problems, you can try to reduce this value. +  * **batch_size**: The number of images in one mini-batch. If you have memory problems you can try to reduce this value. 
-  * **good_path**: Path to folder with good central slices.. +  * **good_path**: Path of folder containing good central slices. 
-  * **bad_path**: Path to folder with bad central slices.+  * **bad_path**: Path of folder containing bad central slices.
   * **pretrained_weights**: Path to weights that are used to initialize the network. It can be empty. As Cinderella is using the same network architecture as crYOLO, we are typically using the [[downloads:cryolo_1#general_phosaurusnet_models|general network of crYOLO]] as pretrained weights.   * **pretrained_weights**: Path to weights that are used to initialize the network. It can be empty. As Cinderella is using the same network architecture as crYOLO, we are typically using the [[downloads:cryolo_1#general_phosaurusnet_models|general network of crYOLO]] as pretrained weights.
-  * **saved_weights_name**: Final model filename+  * **saved_weights_name**: Final model filename.
   * **learning_rate**: Learning rate, should not be changed.   * **learning_rate**: Learning rate, should not be changed.
   * **nb_epoch**: Maximum number of epochs to train. However, it will stop earlier (see nb_early_stop).   * **nb_epoch**: Maximum number of epochs to train. However, it will stop earlier (see nb_early_stop).
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 </code> </code>
  
-This will train a classification network on the GPU with ID=1. After the training finishes, you get a ''my_model.h5'' file. This can then be used to classify subtomograms into good / bad categories.+This will train a classification network on the GPU with ID=1. Once the training finishes, you get a ''my_model.h5'' file. This can then be used to classify subtomograms into good / bad categories.
  
  
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 </code> </code>
  
-You will find two new mrcs files with the classified subtomograms. To check the results with e2display, you have to extract the central slices again (see [[cinderella_tomograms#extract_central_slices|Extract central slices]]).+In the output folder you will find two new mrcs files with the classified subtomograms. To check the results with e2display, you have to extract the central slices again (see [[cinderella_tomograms#extract_central_slices|Extract central slices]]).
  
  
  
cinderella_tomograms.txt · Last modified: 2019/12/13 16:13 by twagner

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