
--- Split 1/10 ---
Calculating normalization statistics from training set...
Vel Mean (local): [43.34461   6.775136], Vel Std (local): [340.67813 289.79938]

Split 1 Data Sizes:
  Train: 65846 samples
  Validation: 14104 samples
  Test: 14145 samples

Training model for Split 1...
Using device: cuda
Epoch [1/50], Train Loss: 0.9580, Val Loss: 0.9016, Val Corr X: 0.4715, Val Corr Y: 0.4705
Epoch [2/50], Train Loss: 0.7727, Val Loss: 0.6433, Val Corr X: 0.6738, Val Corr Y: 0.5656
Epoch [3/50], Train Loss: 0.6026, Val Loss: 0.5405, Val Corr X: 0.7427, Val Corr Y: 0.6504
Epoch [4/50], Train Loss: 0.5069, Val Loss: 0.4809, Val Corr X: 0.7816, Val Corr Y: 0.6807
Epoch [5/50], Train Loss: 0.4472, Val Loss: 0.4473, Val Corr X: 0.7970, Val Corr Y: 0.7123
Epoch [6/50], Train Loss: 0.4192, Val Loss: 0.4059, Val Corr X: 0.8122, Val Corr Y: 0.7340
Epoch [7/50], Train Loss: 0.3783, Val Loss: 0.3841, Val Corr X: 0.8232, Val Corr Y: 0.7565
Epoch [8/50], Train Loss: 0.3506, Val Loss: 0.3703, Val Corr X: 0.8316, Val Corr Y: 0.7668
Epoch [9/50], Train Loss: 0.3390, Val Loss: 0.3450, Val Corr X: 0.8455, Val Corr Y: 0.7761
Epoch [10/50], Train Loss: 0.3173, Val Loss: 0.3316, Val Corr X: 0.8519, Val Corr Y: 0.7879
Epoch [11/50], Train Loss: 0.3007, Val Loss: 0.3231, Val Corr X: 0.8572, Val Corr Y: 0.7967
Epoch [12/50], Train Loss: 0.2904, Val Loss: 0.3094, Val Corr X: 0.8629, Val Corr Y: 0.8035
Epoch [13/50], Train Loss: 0.2764, Val Loss: 0.3024, Val Corr X: 0.8659, Val Corr Y: 0.8105
Epoch [14/50], Train Loss: 0.2644, Val Loss: 0.2949, Val Corr X: 0.8706, Val Corr Y: 0.8139
Epoch [15/50], Train Loss: 0.2564, Val Loss: 0.2861, Val Corr X: 0.8747, Val Corr Y: 0.8201
Epoch [16/50], Train Loss: 0.2576, Val Loss: 0.3109, Val Corr X: 0.8761, Val Corr Y: 0.8145
Epoch [17/50], Train Loss: 0.2635, Val Loss: 0.2964, Val Corr X: 0.8774, Val Corr Y: 0.8209
Epoch [18/50], Train Loss: 0.2466, Val Loss: 0.2894, Val Corr X: 0.8798, Val Corr Y: 0.8288
Epoch [19/50], Train Loss: 0.2326, Val Loss: 0.2732, Val Corr X: 0.8814, Val Corr Y: 0.8320
Epoch [20/50], Train Loss: 0.2287, Val Loss: 0.2697, Val Corr X: 0.8849, Val Corr Y: 0.8331
Epoch [21/50], Train Loss: 0.2218, Val Loss: 0.2619, Val Corr X: 0.8870, Val Corr Y: 0.8350
Epoch [22/50], Train Loss: 0.2146, Val Loss: 0.2630, Val Corr X: 0.8876, Val Corr Y: 0.8374
Epoch [23/50], Train Loss: 0.2162, Val Loss: 0.2528, Val Corr X: 0.8917, Val Corr Y: 0.8389
Epoch [24/50], Train Loss: 0.2077, Val Loss: 0.2535, Val Corr X: 0.8915, Val Corr Y: 0.8417
Epoch [25/50], Train Loss: 0.2007, Val Loss: 0.2600, Val Corr X: 0.8928, Val Corr Y: 0.8410
Epoch [26/50], Train Loss: 0.2088, Val Loss: 0.2434, Val Corr X: 0.8949, Val Corr Y: 0.8457
Epoch [27/50], Train Loss: 0.1987, Val Loss: 0.2442, Val Corr X: 0.8949, Val Corr Y: 0.8456
Epoch [28/50], Train Loss: 0.1923, Val Loss: 0.2501, Val Corr X: 0.8962, Val Corr Y: 0.8454
Epoch [29/50], Train Loss: 0.1892, Val Loss: 0.2490, Val Corr X: 0.8974, Val Corr Y: 0.8451
Epoch [30/50], Train Loss: 0.1853, Val Loss: 0.2357, Val Corr X: 0.8981, Val Corr Y: 0.8525
Epoch [31/50], Train Loss: 0.1807, Val Loss: 0.2383, Val Corr X: 0.8994, Val Corr Y: 0.8522
Epoch [32/50], Train Loss: 0.1821, Val Loss: 0.2337, Val Corr X: 0.8995, Val Corr Y: 0.8530
Epoch [33/50], Train Loss: 0.1750, Val Loss: 0.2557, Val Corr X: 0.8986, Val Corr Y: 0.8478
Epoch [34/50], Train Loss: 0.2081, Val Loss: 0.2411, Val Corr X: 0.9003, Val Corr Y: 0.8522
Epoch [35/50], Train Loss: 0.1900, Val Loss: 0.2457, Val Corr X: 0.9015, Val Corr Y: 0.8507
Epoch [36/50], Train Loss: 0.1866, Val Loss: 0.2791, Val Corr X: 0.8968, Val Corr Y: 0.8434
Epoch [37/50], Train Loss: 0.1915, Val Loss: 0.2361, Val Corr X: 0.9001, Val Corr Y: 0.8548
Epoch [38/50], Train Loss: 0.1807, Val Loss: 0.2362, Val Corr X: 0.8996, Val Corr Y: 0.8534
Epoch [39/50], Train Loss: 0.1669, Val Loss: 0.2341, Val Corr X: 0.9018, Val Corr Y: 0.8576
Epoch [40/50], Train Loss: 0.1651, Val Loss: 0.2304, Val Corr X: 0.9039, Val Corr Y: 0.8544
Epoch [41/50], Train Loss: 0.1629, Val Loss: 0.2312, Val Corr X: 0.9046, Val Corr Y: 0.8563
Epoch [42/50], Train Loss: 0.1638, Val Loss: 0.2301, Val Corr X: 0.9039, Val Corr Y: 0.8583
Epoch [43/50], Train Loss: 0.1598, Val Loss: 0.2215, Val Corr X: 0.9070, Val Corr Y: 0.8592
Epoch [44/50], Train Loss: 0.1592, Val Loss: 0.2332, Val Corr X: 0.9061, Val Corr Y: 0.8571
Epoch [45/50], Train Loss: 0.1660, Val Loss: 0.2308, Val Corr X: 0.9070, Val Corr Y: 0.8560
Epoch [46/50], Train Loss: 0.1542, Val Loss: 0.2204, Val Corr X: 0.9082, Val Corr Y: 0.8611
Epoch [47/50], Train Loss: 0.1553, Val Loss: 0.2368, Val Corr X: 0.9066, Val Corr Y: 0.8556
Epoch [48/50], Train Loss: 0.1640, Val Loss: 0.2266, Val Corr X: 0.9083, Val Corr Y: 0.8621
Epoch [49/50], Train Loss: 0.1476, Val Loss: 0.2188, Val Corr X: 0.9101, Val Corr Y: 0.8639
Epoch [50/50], Train Loss: 0.1510, Val Loss: 0.2178, Val Corr X: 0.9109, Val Corr Y: 0.8610
Model saved at snn_mcmaze_split_1.pth. Exiting training loop.

--- Evaluating on Test Set for Split 1 ---
Loading model from snn_mcmaze_split_1.pth for testing...

Test Set Results (Split 1):
  Loss: 0.2225
  Correlation X: 0.9128
  Correlation Y: 0.8587
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