finetuning (#1)

Reviewed-on: #1
Extended Phasenet finetuning options

experiments/sweep_basicphase_ae.yaml

@@ -1,3 +1,4 @@
+name: BasicPhaseAE
 method: bayes
 metric:
   goal: minimize
@@ -7,13 +8,9 @@ parameters:
     value:
       - BasicPhaseAE
   batch_size:
-    distribution: int_uniform
-    max: 1024
-    min: 256
+    values: [64, 128, 256]
   max_epochs:
     value:
-      - 20
+      - 30
   learning_rate:
-    distribution: uniform
-    max: 0.02
-    min: 0.001
+    values: [0.01, 0.005, 0.001]

experiments/sweep_eqtransformer.yaml

@@ -8,13 +8,9 @@ parameters:
     value:
       - EQTransformer
   batch_size:
-    distribution: int_uniform
-    max: 1024
-    min: 256
+    values: [64, 128, 256]
   max_epochs:
     value:
       - 30
   learning_rate:
-    distribution: uniform
-    max: 0.02
-    min: 0.005
+    values: [0.01, 0.005, 0.001]

experiments/sweep_gpd.yaml

@@ -1,4 +1,4 @@
-name: GPD_fixed_highpass:2-10
+name: GPD
 method: bayes
 metric:
   goal: minimize
@@ -8,16 +8,12 @@ parameters:
     value:
       - GPD
   batch_size:
-    distribution: int_uniform
-    max: 1024
-    min: 256
+    values: [64, 128, 256]
   max_epochs:
     value:
       - 30
   learning_rate:
-    distribution: uniform
-    max: 0.02
-    min: 0.005
+    values: [0.01, 0.005, 0.001]
   highpass:
     value:
       - 1