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@@ -2,13 +2,13 @@
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mri_data = '/data/data_wnx1/_Data/AlzheimersDL/CNN+RNN-2class-1cnn+data/PET_volumes_customtemplate_float32/'
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xls_data = '/export/home/nschense/alzheimers/alzheimers_nn/LP_ADNIMERGE.csv'
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-#CHANGE THESE BEFORE RUNNING
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+# CHANGE THESE BEFORE RUNNING
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model_output = '/export/home/nschense/alzheimers/alzheimers_nn/saved_models/'
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[training]
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device = 'cuda:1'
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-runs = 10
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-max_epochs = 10
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+runs = 10 # Number of models
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+max_epochs = 10 # Number of epochs each model trains for
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[dataset]
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validation_split = 0.4 #Splits the dataset into the train and validation/test set, 50% each for validation and test
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@@ -16,9 +16,9 @@ validation_split = 0.4 #Splits the dataset into the train and validation/test se
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#|splt*0.5 | split*0.5 | 1-split |
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[model]
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-name = 'cnn-10x10'
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-image_channels = 1
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-clin_data_channels = 2
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+name = 'cnn-10x10' # Name that model set will be saved under
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+image_channels = 1 # DO NOT CHANGE UNLESS SURE
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+clin_data_channels = 2 # DO NOT CHANGE UNLESS SURE
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[hyperparameters]
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batch_size = 32
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@@ -26,8 +26,8 @@ learning_rate = 0.0001
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droprate = 0.5
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[operation]
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-silent = false
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+silent = false # Disables fancy bar graphs and reporting
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[ensemble]
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-name = 'cnn-10x10'
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+name = 'cnn-10x10' # Name that ensemble evaluator uses to load ensemble
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prune_threshold = 0.0 # Any models with accuracy below this threshold will be pruned, set to 0 to disable pruning
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