add ResNet training

dcase2020_task2/experiments/classification_experiment.py

@@ -204,6 +204,7 @@ def configuration():
     batch_size = 32
     learning_rate = 1e-4
     weight_decay = 0
+    learning_rate_decay = 0.99
 
     normalize_raw = True
     normalize_spec = False
@@ -284,7 +285,7 @@ def configuration():
             '@optimizer',
         ],
         'kwargs': {
-            'gamma': 0.99
+            'gamma': learning_rate_decay
         }
     }
 

scripts/resnet_grid_search/gs.sh

@@ -2,11 +2,12 @@ epochs=100
 machine_type=3
 machine_id=1
 
-for rf in normal a_bit_smaller a_bit_larger
+for rf in a_bit_larger
 do
   for valid_types in loose
   do
-    python -m dcase2020_task2.experiments.classification_experiment with machine_type=$machine_type machine_id=$machine_id epochs=$epochs rf=$rf valid_types=$valid_types -m student2.cp.jku.at:27017:debug_
-
+    for learning_rate_decay in 0.98 0.97 0.96
+      python -m dcase2020_task2.experiments.classification_experiment with learning_rate_decay=$learning_rate_decay machine_type=$machine_type machine_id=$machine_id epochs=$epochs rf=$rf valid_types=$valid_types -m student2.cp.jku.at:27017:debug_
+    done
   done
 done