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--- auto2d_tutorial [2019/05/29 09:45]
twagner [Classify]
+++ auto2d_tutorial [2019/08/15 15:36]
twagner [Classify]
@@ Line 6: / Line 6: @@
 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
-	}
-}
-</code>
-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.
@@ Line 35: / Line 12: @@
 <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>
@@ Line 60: / Line 37: @@
 		"learning_rate": 1e-4,
 		"nb_epoch": 100,
-		"nb_early_stop": 5
+		"nb_early_stop": 15
 	}
 }