From ed13aeb8220e8cfff74215f7bd3af12ba8992d2c Mon Sep 17 00:00:00 2001
From: Lars Bogner <larsbog@ml6.hpc.uio.no>
Date: Thu, 27 Nov 2025 08:34:08 +0100
Subject: [PATCH] Finish run 2

---
 Code/python/notebooks/hyperparameter_scan.py  |  8 +--
 .../hyperparameter_scan_results_mode2.csv     | 65 +++++++++++++++++++
 2 files changed, 69 insertions(+), 4 deletions(-)
 create mode 100644 Code/python/notebooks/hyperparameter_scan_results_mode2.csv

diff --git a/Code/python/notebooks/hyperparameter_scan.py b/Code/python/notebooks/hyperparameter_scan.py
index 21f1439..304e1d2 100644
--- a/Code/python/notebooks/hyperparameter_scan.py
+++ b/Code/python/notebooks/hyperparameter_scan.py
@@ -3,8 +3,8 @@ import pathlib
 import pandas as pd
 from aiRNN import dataloader, models, losses
 
-MODE = 1
-DEVICE = "cuda"
+MODE = 2
+DEVICE = "cuda:2"
 
 
 # ------------------------------------------------------------
@@ -181,7 +181,7 @@ def run_mode_1():
 def run_mode_2():
     results = []
 
-    step = 1
+    step = 10
     base_ds = dataloader.SaveDataset(torch.load("dataset.pt"), step=step)
     train_ds, val_ds = split_dataset(base_ds)
 
@@ -228,7 +228,7 @@ def run_mode_2():
 def run_mode_3():
     results = []
 
-    step = 1
+    step = 10
     base_name = "LSTM"
     cls = models.ThreeInputLSTM
     hidden_size = 16
diff --git a/Code/python/notebooks/hyperparameter_scan_results_mode2.csv b/Code/python/notebooks/hyperparameter_scan_results_mode2.csv
new file mode 100644
index 0000000..caf5f45
--- /dev/null
+++ b/Code/python/notebooks/hyperparameter_scan_results_mode2.csv
@@ -0,0 +1,65 @@
+Model,Hidden Layers,RNN Layers,Dropout,Final Loss
+LSTM,0,2,0.0,13.57315312304967
+LSTM,0,2,0.05,19.794176068104488
+LSTM,0,2,0.1,17.698180574766347
+LSTM,0,2,0.15,23.32098652611316
+LSTM,0,3,0.0,13.700146980688606
+LSTM,0,3,0.05,17.857194296071228
+LSTM,0,3,0.1,20.897490259627222
+LSTM,0,3,0.15,20.09943933218298
+LSTM,0,4,0.0,20.451897950239584
+LSTM,0,4,0.05,30.697136825239156
+LSTM,0,4,0.1,35.30218719428694
+LSTM,0,4,0.15,56.88415065281828
+LSTM,0,5,0.0,32.88772487640381
+LSTM,0,5,0.05,41.01707866829886
+LSTM,0,5,0.1,25.020204705251775
+LSTM,0,5,0.15,31.874271876375442
+LSTM,1,2,0.0,15.143101020598076
+LSTM,1,2,0.05,17.294107027456793
+LSTM,1,2,0.1,20.339507552939402
+LSTM,1,2,0.15,20.261145202206894
+LSTM,1,3,0.0,18.612104852434616
+LSTM,1,3,0.05,21.64210480031833
+LSTM,1,3,0.1,33.828577202810365
+LSTM,1,3,0.15,24.81157889836271
+LSTM,1,4,0.0,25.49259500100579
+LSTM,1,4,0.05,43.26106494581196
+LSTM,1,4,0.1,109.03877604847223
+LSTM,1,4,0.15,26.519190546492457
+LSTM,1,5,0.0,24.345332991908972
+LSTM,1,5,0.05,2160.6643607985807
+LSTM,1,5,0.1,2160.6641690966107
+LSTM,1,5,0.15,70.0674764337674
+LSTM,2,2,0.0,96.50166923899046
+LSTM,2,2,0.05,22.740719331821925
+LSTM,2,2,0.1,27.11947537811709
+LSTM,2,2,0.15,26.377998459507044
+LSTM,2,3,0.0,29.63593423870248
+LSTM,2,3,0.05,40.430933052385356
+LSTM,2,3,0.1,102.70769267015054
+LSTM,2,3,0.15,39.901821069314444
+LSTM,2,4,0.0,16.42642393246503
+LSTM,2,4,0.05,103.43423487434924
+LSTM,2,4,0.1,132.6539063252194
+LSTM,2,4,0.15,109.47043222776601
+LSTM,2,5,0.0,26.904185483153437
+LSTM,2,5,0.05,2160.663507166043
+LSTM,2,5,0.1,2160.6676799075703
+LSTM,2,5,0.15,2160.6770200057767
+LSTM,4,2,0.0,96.90064337555792
+LSTM,4,2,0.05,44.17875827198297
+LSTM,4,2,0.1,107.32435410459277
+LSTM,4,2,0.15,36.28804776366328
+LSTM,4,3,0.0,100.72823489551813
+LSTM,4,3,0.05,114.38032405477175
+LSTM,4,3,0.1,220.88371201636087
+LSTM,4,3,0.15,38.124560718805014
+LSTM,4,4,0.0,50.08498817766216
+LSTM,4,4,0.05,2160.668174206371
+LSTM,4,4,0.1,317.8842515542474
+LSTM,4,4,0.15,380.57344377544564
+LSTM,4,5,0.0,2160.663039516395
+LSTM,4,5,0.05,2160.6706989986797
+LSTM,4,5,0.1,158.177048481686
+LSTM,4,5,0.15,704.9843010700924