diff --git "a/train.ipynb" "b/train.ipynb"
--- "a/train.ipynb"
+++ "b/train.ipynb"
@@ -2,21 +2,10 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "da16a2e2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ CUDA 사용 가능: True\n",
-      "🔢 CUDA 장치 수: 1\n",
-      "🖥️ CUDA 장치 이름: NVIDIA GeForce RTX 3060 Laptop GPU\n",
-      "🧱 PyTorch 빌드된 CUDA 버전: 12.8\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# CUDA test\n",
     "import torch\n",
@@ -46,7 +35,7 @@
     "#device = torch.device(\"cpu\") # CPU 사용\n",
     "\n",
     "model = Vector2MIDI(25, 1024, 7).to(device)\n",
-    "criterion = HuberDTW_CrossEntropyLoss(device, -1)\n",
+    "criterion = HuberDTW_CrossEntropyLoss(device).to(device)\n",
     "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
    ]
   },
@@ -87,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "4e0ea127",
    "metadata": {},
    "outputs": [],
@@ -100,6 +89,21 @@
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "045aa710",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "EPOCH = 1000\n",
+    "\n",
+    "# 최고 성능 추적을 위한 변수들\n",
+    "best_loss = float('inf')  # 가장 좋은 loss 값\n",
+    "best_model_state = None   # 최고 성능 모델의 state_dict\n",
+    "best_loss = 45.9630"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -110,1006 +114,1088 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1, Loss: 107.2922, Best: 107.2922\n",
-      "Epoch 2, Loss: 100.4210, Best: 100.4210\n",
-      "Epoch 3, Loss: 78.6494, Best: 78.6494\n",
-      "Epoch 4, Loss: 89.9005, Best: 78.6494\n",
-      "Epoch 5, Loss: 83.1708, Best: 78.6494\n",
-      "Epoch 6, Loss: 77.8460, Best: 77.8460\n",
-      "Epoch 7, Loss: 74.0987, Best: 74.0987\n",
-      "Epoch 8, Loss: 86.8002, Best: 74.0987\n",
-      "Epoch 9, Loss: 89.5789, Best: 74.0987\n",
-      "Epoch 10, Loss: 74.4704, Best: 74.0987\n",
-      "Epoch 11, Loss: 95.0204, Best: 74.0987\n",
-      "Epoch 12, Loss: 104.7883, Best: 74.0987\n",
-      "Epoch 13, Loss: 108.1768, Best: 74.0987\n",
-      "Epoch 14, Loss: 90.1786, Best: 74.0987\n",
-      "Epoch 15, Loss: 103.4142, Best: 74.0987\n",
-      "Epoch 16, Loss: 109.4928, Best: 74.0987\n",
-      "Epoch 17, Loss: 82.5460, Best: 74.0987\n",
-      "Epoch 18, Loss: 73.2119, Best: 73.2119\n",
-      "Epoch 19, Loss: 98.1412, Best: 73.2119\n",
-      "Epoch 20, Loss: 100.4175, Best: 73.2119\n",
-      "Epoch 21, Loss: 102.2248, Best: 73.2119\n",
-      "Epoch 22, Loss: 110.3333, Best: 73.2119\n",
-      "Epoch 23, Loss: 97.0454, Best: 73.2119\n",
-      "Epoch 24, Loss: 92.5310, Best: 73.2119\n",
-      "Epoch 25, Loss: 105.4718, Best: 73.2119\n",
-      "Epoch 26, Loss: 78.1541, Best: 73.2119\n",
-      "Epoch 27, Loss: 86.3142, Best: 73.2119\n",
-      "Epoch 28, Loss: 79.0157, Best: 73.2119\n",
-      "Epoch 29, Loss: 96.6074, Best: 73.2119\n",
-      "Epoch 30, Loss: 76.9343, Best: 73.2119\n",
-      "Epoch 31, Loss: 76.0782, Best: 73.2119\n",
-      "Epoch 32, Loss: 91.0389, Best: 73.2119\n",
-      "Epoch 33, Loss: 100.6942, Best: 73.2119\n",
-      "Epoch 34, Loss: 100.4722, Best: 73.2119\n",
-      "Epoch 35, Loss: 92.5219, Best: 73.2119\n",
-      "Epoch 36, Loss: 81.5320, Best: 73.2119\n",
-      "Epoch 37, Loss: 105.4998, Best: 73.2119\n",
-      "Epoch 38, Loss: 78.6620, Best: 73.2119\n",
-      "Epoch 39, Loss: 85.2069, Best: 73.2119\n",
-      "Epoch 40, Loss: 85.5489, Best: 73.2119\n",
-      "Epoch 41, Loss: 81.8793, Best: 73.2119\n",
-      "Epoch 42, Loss: 95.8695, Best: 73.2119\n",
-      "Epoch 43, Loss: 82.4059, Best: 73.2119\n",
-      "Epoch 44, Loss: 109.7793, Best: 73.2119\n",
-      "Epoch 45, Loss: 93.8224, Best: 73.2119\n",
-      "Epoch 46, Loss: 97.8657, Best: 73.2119\n",
-      "Epoch 47, Loss: 80.0278, Best: 73.2119\n",
-      "Epoch 48, Loss: 87.0851, Best: 73.2119\n",
-      "Epoch 49, Loss: 108.8226, Best: 73.2119\n",
-      "Epoch 50, Loss: 98.6351, Best: 73.2119\n",
-      "Epoch 51, Loss: 94.2167, Best: 73.2119\n",
-      "Epoch 52, Loss: 88.5021, Best: 73.2119\n",
-      "Epoch 53, Loss: 85.8802, Best: 73.2119\n",
-      "Epoch 54, Loss: 102.8113, Best: 73.2119\n",
-      "Epoch 55, Loss: 79.5641, Best: 73.2119\n",
-      "Epoch 56, Loss: 89.3849, Best: 73.2119\n",
-      "Epoch 57, Loss: 123.8215, Best: 73.2119\n",
-      "Epoch 58, Loss: 95.9795, Best: 73.2119\n",
-      "Epoch 59, Loss: 86.8023, Best: 73.2119\n",
-      "Epoch 60, Loss: 115.9772, Best: 73.2119\n",
-      "Epoch 61, Loss: 85.0362, Best: 73.2119\n",
-      "Epoch 62, Loss: 108.5699, Best: 73.2119\n",
-      "Epoch 63, Loss: 99.0839, Best: 73.2119\n",
-      "Epoch 64, Loss: 80.0096, Best: 73.2119\n",
-      "Epoch 65, Loss: 133.7084, Best: 73.2119\n",
-      "Epoch 66, Loss: 88.1151, Best: 73.2119\n",
-      "Epoch 67, Loss: 111.4721, Best: 73.2119\n",
-      "Epoch 68, Loss: 113.0979, Best: 73.2119\n",
-      "Epoch 69, Loss: 100.9471, Best: 73.2119\n",
-      "Epoch 70, Loss: 95.4183, Best: 73.2119\n",
-      "Epoch 71, Loss: 94.6063, Best: 73.2119\n",
-      "Epoch 72, Loss: 86.1115, Best: 73.2119\n",
-      "Epoch 73, Loss: 106.8548, Best: 73.2119\n",
-      "Epoch 74, Loss: 98.1562, Best: 73.2119\n",
-      "Epoch 75, Loss: 105.8346, Best: 73.2119\n",
-      "Epoch 76, Loss: 107.9578, Best: 73.2119\n",
-      "Epoch 77, Loss: 89.1115, Best: 73.2119\n",
-      "Epoch 78, Loss: 98.6991, Best: 73.2119\n",
-      "Epoch 79, Loss: 108.0098, Best: 73.2119\n",
-      "Epoch 80, Loss: 89.3592, Best: 73.2119\n",
-      "Epoch 81, Loss: 90.7968, Best: 73.2119\n",
-      "Epoch 82, Loss: 97.5626, Best: 73.2119\n",
-      "Epoch 83, Loss: 90.8960, Best: 73.2119\n",
-      "Epoch 84, Loss: 97.9150, Best: 73.2119\n",
-      "Epoch 85, Loss: 100.4646, Best: 73.2119\n",
-      "Epoch 86, Loss: 90.7263, Best: 73.2119\n",
-      "Epoch 87, Loss: 85.6680, Best: 73.2119\n",
-      "Epoch 88, Loss: 74.9093, Best: 73.2119\n",
-      "Epoch 89, Loss: 79.9411, Best: 73.2119\n",
-      "Epoch 90, Loss: 73.4789, Best: 73.2119\n",
-      "Epoch 91, Loss: 88.0552, Best: 73.2119\n",
-      "Epoch 92, Loss: 85.4286, Best: 73.2119\n",
-      "Epoch 93, Loss: 101.5156, Best: 73.2119\n",
-      "Epoch 94, Loss: 83.3544, Best: 73.2119\n",
-      "Epoch 95, Loss: 81.6909, Best: 73.2119\n",
-      "Epoch 96, Loss: 94.8026, Best: 73.2119\n",
-      "Epoch 97, Loss: 84.8745, Best: 73.2119\n",
-      "Epoch 98, Loss: 98.1149, Best: 73.2119\n",
-      "Epoch 99, Loss: 79.0990, Best: 73.2119\n",
-      "Epoch 100, Loss: 106.6927, Best: 73.2119\n",
-      "Epoch 101, Loss: 83.5364, Best: 73.2119\n",
-      "Epoch 102, Loss: 89.9548, Best: 73.2119\n",
-      "Epoch 103, Loss: 82.0817, Best: 73.2119\n",
-      "Epoch 104, Loss: 104.5588, Best: 73.2119\n",
-      "Epoch 105, Loss: 108.7993, Best: 73.2119\n",
-      "Epoch 106, Loss: 86.1137, Best: 73.2119\n",
-      "Epoch 107, Loss: 82.2808, Best: 73.2119\n",
-      "Epoch 108, Loss: 103.6288, Best: 73.2119\n",
-      "Epoch 109, Loss: 104.6779, Best: 73.2119\n",
-      "Epoch 110, Loss: 103.8418, Best: 73.2119\n",
-      "Epoch 111, Loss: 83.9289, Best: 73.2119\n",
-      "Epoch 112, Loss: 102.3906, Best: 73.2119\n",
-      "Epoch 113, Loss: 81.7960, Best: 73.2119\n",
-      "Epoch 114, Loss: 93.9751, Best: 73.2119\n",
-      "Epoch 115, Loss: 78.8637, Best: 73.2119\n",
-      "Epoch 116, Loss: 103.6338, Best: 73.2119\n",
-      "Epoch 117, Loss: 74.3486, Best: 73.2119\n",
-      "Epoch 118, Loss: 98.7364, Best: 73.2119\n",
-      "Epoch 119, Loss: 89.4826, Best: 73.2119\n",
-      "Epoch 120, Loss: 87.1620, Best: 73.2119\n",
-      "Epoch 121, Loss: 94.0376, Best: 73.2119\n",
-      "Epoch 122, Loss: 95.4947, Best: 73.2119\n",
-      "Epoch 123, Loss: 87.6564, Best: 73.2119\n",
-      "Epoch 124, Loss: 86.4193, Best: 73.2119\n",
-      "Epoch 125, Loss: 79.5115, Best: 73.2119\n",
-      "Epoch 126, Loss: 102.3629, Best: 73.2119\n",
-      "Epoch 127, Loss: 87.8047, Best: 73.2119\n",
-      "Epoch 128, Loss: 91.9903, Best: 73.2119\n",
-      "Epoch 129, Loss: 82.7084, Best: 73.2119\n",
-      "Epoch 130, Loss: 99.0210, Best: 73.2119\n",
-      "Epoch 131, Loss: 78.5915, Best: 73.2119\n",
-      "Epoch 132, Loss: 84.7695, Best: 73.2119\n",
-      "Epoch 133, Loss: 80.9710, Best: 73.2119\n",
-      "Epoch 134, Loss: 75.0159, Best: 73.2119\n",
-      "Epoch 135, Loss: 94.2473, Best: 73.2119\n",
-      "Epoch 136, Loss: 86.1276, Best: 73.2119\n",
-      "Epoch 137, Loss: 81.6781, Best: 73.2119\n",
-      "Epoch 138, Loss: 91.6275, Best: 73.2119\n",
-      "Epoch 139, Loss: 87.6571, Best: 73.2119\n",
-      "Epoch 140, Loss: 94.8411, Best: 73.2119\n",
-      "Epoch 141, Loss: 88.9180, Best: 73.2119\n",
-      "Epoch 142, Loss: 76.1281, Best: 73.2119\n",
-      "Epoch 143, Loss: 95.6294, Best: 73.2119\n",
-      "Epoch 144, Loss: 104.6790, Best: 73.2119\n",
-      "Epoch 145, Loss: 90.2070, Best: 73.2119\n",
-      "Epoch 146, Loss: 83.9171, Best: 73.2119\n",
-      "Epoch 147, Loss: 83.4951, Best: 73.2119\n",
-      "Epoch 148, Loss: 83.3432, Best: 73.2119\n",
-      "Epoch 149, Loss: 103.1958, Best: 73.2119\n",
-      "Epoch 150, Loss: 86.6415, Best: 73.2119\n",
-      "Epoch 151, Loss: 89.6640, Best: 73.2119\n",
-      "Epoch 152, Loss: 95.3069, Best: 73.2119\n",
-      "Epoch 153, Loss: 85.0578, Best: 73.2119\n",
-      "Epoch 154, Loss: 103.6180, Best: 73.2119\n",
-      "Epoch 155, Loss: 113.7165, Best: 73.2119\n",
-      "Epoch 156, Loss: 99.8805, Best: 73.2119\n",
-      "Epoch 157, Loss: 119.8716, Best: 73.2119\n",
-      "Epoch 158, Loss: 77.4664, Best: 73.2119\n",
-      "Epoch 159, Loss: 90.5630, Best: 73.2119\n",
-      "Epoch 160, Loss: 90.6424, Best: 73.2119\n",
-      "Epoch 161, Loss: 99.8581, Best: 73.2119\n",
-      "Epoch 162, Loss: 91.2030, Best: 73.2119\n",
-      "Epoch 163, Loss: 103.2573, Best: 73.2119\n",
-      "Epoch 164, Loss: 88.3497, Best: 73.2119\n",
-      "Epoch 165, Loss: 90.9527, Best: 73.2119\n",
-      "Epoch 166, Loss: 86.9361, Best: 73.2119\n",
-      "Epoch 167, Loss: 91.9101, Best: 73.2119\n",
-      "Epoch 168, Loss: 91.9672, Best: 73.2119\n",
-      "Epoch 169, Loss: 78.3140, Best: 73.2119\n",
-      "Epoch 170, Loss: 81.5148, Best: 73.2119\n",
-      "Epoch 171, Loss: 74.1348, Best: 73.2119\n",
-      "Epoch 172, Loss: 111.8256, Best: 73.2119\n",
-      "Epoch 173, Loss: 85.5649, Best: 73.2119\n",
-      "Epoch 174, Loss: 103.8277, Best: 73.2119\n",
-      "Epoch 175, Loss: 94.1736, Best: 73.2119\n",
-      "Epoch 176, Loss: 89.9746, Best: 73.2119\n",
-      "Epoch 177, Loss: 90.9714, Best: 73.2119\n",
-      "Epoch 178, Loss: 107.0029, Best: 73.2119\n",
-      "Epoch 179, Loss: 83.7151, Best: 73.2119\n",
-      "Epoch 180, Loss: 87.8976, Best: 73.2119\n",
-      "Epoch 181, Loss: 99.7132, Best: 73.2119\n",
-      "Epoch 182, Loss: 79.9244, Best: 73.2119\n",
-      "Epoch 183, Loss: 81.1880, Best: 73.2119\n",
-      "Epoch 184, Loss: 101.9388, Best: 73.2119\n",
-      "Epoch 185, Loss: 76.3096, Best: 73.2119\n",
-      "Epoch 186, Loss: 79.0698, Best: 73.2119\n",
-      "Epoch 187, Loss: 74.3205, Best: 73.2119\n",
-      "Epoch 188, Loss: 88.6500, Best: 73.2119\n",
-      "Epoch 189, Loss: 87.9569, Best: 73.2119\n",
-      "Epoch 190, Loss: 77.7218, Best: 73.2119\n",
-      "Epoch 191, Loss: 86.3858, Best: 73.2119\n",
-      "Epoch 192, Loss: 103.6451, Best: 73.2119\n",
-      "Epoch 193, Loss: 78.1397, Best: 73.2119\n",
-      "Epoch 194, Loss: 105.9306, Best: 73.2119\n",
-      "Epoch 195, Loss: 83.4202, Best: 73.2119\n",
-      "Epoch 196, Loss: 92.8804, Best: 73.2119\n",
-      "Epoch 197, Loss: 83.7321, Best: 73.2119\n",
-      "Epoch 198, Loss: 84.0810, Best: 73.2119\n",
-      "Epoch 199, Loss: 128.0791, Best: 73.2119\n",
-      "Epoch 200, Loss: 99.0690, Best: 73.2119\n",
-      "Epoch 201, Loss: 108.3650, Best: 73.2119\n",
-      "Epoch 202, Loss: 103.0807, Best: 73.2119\n",
-      "Epoch 203, Loss: 83.6110, Best: 73.2119\n",
-      "Epoch 204, Loss: 86.8961, Best: 73.2119\n",
-      "Epoch 205, Loss: 96.2187, Best: 73.2119\n",
-      "Epoch 206, Loss: 77.8460, Best: 73.2119\n",
-      "Epoch 207, Loss: 89.4338, Best: 73.2119\n",
-      "Epoch 208, Loss: 87.9576, Best: 73.2119\n",
-      "Epoch 209, Loss: 86.0673, Best: 73.2119\n",
-      "Epoch 210, Loss: 87.7673, Best: 73.2119\n",
-      "Epoch 211, Loss: 88.3853, Best: 73.2119\n",
-      "Epoch 212, Loss: 97.4507, Best: 73.2119\n",
-      "Epoch 213, Loss: 80.5159, Best: 73.2119\n",
-      "Epoch 214, Loss: 101.1057, Best: 73.2119\n",
-      "Epoch 215, Loss: 76.8868, Best: 73.2119\n",
-      "Epoch 216, Loss: 91.0984, Best: 73.2119\n",
-      "Epoch 217, Loss: 85.9969, Best: 73.2119\n",
-      "Epoch 218, Loss: 90.3749, Best: 73.2119\n",
-      "Epoch 219, Loss: 77.9431, Best: 73.2119\n",
-      "Epoch 220, Loss: 97.5016, Best: 73.2119\n",
-      "Epoch 221, Loss: 80.3887, Best: 73.2119\n",
-      "Epoch 222, Loss: 80.0385, Best: 73.2119\n",
-      "Epoch 223, Loss: 77.1365, Best: 73.2119\n",
-      "Epoch 224, Loss: 71.6566, Best: 71.6566\n",
-      "Epoch 225, Loss: 78.4750, Best: 71.6566\n",
-      "Epoch 226, Loss: 93.8799, Best: 71.6566\n",
-      "Epoch 227, Loss: 85.7453, Best: 71.6566\n",
-      "Epoch 228, Loss: 90.9856, Best: 71.6566\n",
-      "Epoch 229, Loss: 80.1630, Best: 71.6566\n",
-      "Epoch 230, Loss: 79.4040, Best: 71.6566\n",
-      "Epoch 231, Loss: 84.4371, Best: 71.6566\n",
-      "Epoch 232, Loss: 85.9269, Best: 71.6566\n",
-      "Epoch 233, Loss: 78.6152, Best: 71.6566\n",
-      "Epoch 234, Loss: 81.0698, Best: 71.6566\n",
-      "Epoch 235, Loss: 87.0057, Best: 71.6566\n",
-      "Epoch 236, Loss: 94.6833, Best: 71.6566\n",
-      "Epoch 237, Loss: 107.2777, Best: 71.6566\n",
-      "Epoch 238, Loss: 87.8060, Best: 71.6566\n",
-      "Epoch 239, Loss: 79.9406, Best: 71.6566\n",
-      "Epoch 240, Loss: 84.3497, Best: 71.6566\n",
-      "Epoch 241, Loss: 76.8179, Best: 71.6566\n",
-      "Epoch 242, Loss: 125.5482, Best: 71.6566\n",
-      "Epoch 243, Loss: 86.2328, Best: 71.6566\n",
-      "Epoch 244, Loss: 89.3969, Best: 71.6566\n",
-      "Epoch 245, Loss: 75.1573, Best: 71.6566\n",
-      "Epoch 246, Loss: 94.1627, Best: 71.6566\n",
-      "Epoch 247, Loss: 94.2677, Best: 71.6566\n",
-      "Epoch 248, Loss: 114.7383, Best: 71.6566\n",
-      "Epoch 249, Loss: 103.6948, Best: 71.6566\n",
-      "Epoch 250, Loss: 90.7263, Best: 71.6566\n",
-      "Epoch 251, Loss: 87.7065, Best: 71.6566\n",
-      "Epoch 252, Loss: 82.0458, Best: 71.6566\n",
-      "Epoch 253, Loss: 74.6847, Best: 71.6566\n",
-      "Epoch 254, Loss: 83.1074, Best: 71.6566\n",
-      "Epoch 255, Loss: 86.2310, Best: 71.6566\n",
-      "Epoch 256, Loss: 93.6086, Best: 71.6566\n",
-      "Epoch 257, Loss: 98.1747, Best: 71.6566\n",
-      "Epoch 258, Loss: 91.1581, Best: 71.6566\n",
-      "Epoch 259, Loss: 117.6169, Best: 71.6566\n",
-      "Epoch 260, Loss: 78.0198, Best: 71.6566\n",
-      "Epoch 261, Loss: 213.6538, Best: 71.6566\n",
-      "Epoch 262, Loss: 126.8985, Best: 71.6566\n",
-      "Epoch 263, Loss: 85.4544, Best: 71.6566\n",
-      "Epoch 264, Loss: 122.8042, Best: 71.6566\n",
-      "Epoch 265, Loss: 112.4915, Best: 71.6566\n",
-      "Epoch 266, Loss: 122.1085, Best: 71.6566\n",
-      "Epoch 267, Loss: 90.7283, Best: 71.6566\n",
-      "Epoch 268, Loss: 83.9552, Best: 71.6566\n",
-      "Epoch 269, Loss: 81.7334, Best: 71.6566\n",
-      "Epoch 270, Loss: 101.5641, Best: 71.6566\n",
-      "Epoch 271, Loss: 88.7566, Best: 71.6566\n",
-      "Epoch 272, Loss: 97.7017, Best: 71.6566\n",
-      "Epoch 273, Loss: 85.4816, Best: 71.6566\n",
-      "Epoch 274, Loss: 102.6288, Best: 71.6566\n",
-      "Epoch 275, Loss: 104.0799, Best: 71.6566\n",
-      "Epoch 276, Loss: 78.0541, Best: 71.6566\n",
-      "Epoch 277, Loss: 82.6784, Best: 71.6566\n",
-      "Epoch 278, Loss: 78.5035, Best: 71.6566\n",
-      "Epoch 279, Loss: 86.2724, Best: 71.6566\n",
-      "Epoch 280, Loss: 86.1112, Best: 71.6566\n",
-      "Epoch 281, Loss: 85.5426, Best: 71.6566\n",
-      "Epoch 282, Loss: 89.5174, Best: 71.6566\n",
-      "Epoch 283, Loss: 102.6838, Best: 71.6566\n",
-      "Epoch 284, Loss: 91.6879, Best: 71.6566\n",
-      "Epoch 285, Loss: 82.0611, Best: 71.6566\n",
-      "Epoch 286, Loss: 96.4743, Best: 71.6566\n",
-      "Epoch 287, Loss: 89.6002, Best: 71.6566\n",
-      "Epoch 288, Loss: 93.8499, Best: 71.6566\n",
-      "Epoch 289, Loss: 85.4888, Best: 71.6566\n",
-      "Epoch 290, Loss: 87.0453, Best: 71.6566\n",
-      "Epoch 291, Loss: 92.4028, Best: 71.6566\n",
-      "Epoch 292, Loss: 91.2136, Best: 71.6566\n",
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-      "Epoch 404, Loss: 86.8400, Best: 71.6566\n",
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-      "Epoch 423, Loss: 128.4507, Best: 71.6566\n",
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-      "Epoch 426, Loss: 93.3403, Best: 71.6566\n",
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-      "Epoch 432, Loss: 89.5322, Best: 71.6566\n",
-      "Epoch 433, Loss: 78.2041, Best: 71.6566\n",
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-      "Epoch 435, Loss: 82.1013, Best: 71.6566\n",
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-      "Epoch 440, Loss: 90.0219, Best: 71.6566\n",
-      "Epoch 441, Loss: 98.4622, Best: 71.6566\n",
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-      "Epoch 443, Loss: 79.9524, Best: 71.6566\n",
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-      "Epoch 446, Loss: 96.8561, Best: 71.6566\n",
-      "Epoch 447, Loss: 74.5044, Best: 71.6566\n",
-      "Epoch 448, Loss: 73.4091, Best: 71.6566\n",
-      "Epoch 449, Loss: 98.8564, Best: 71.6566\n",
-      "Epoch 450, Loss: 76.9750, Best: 71.6566\n",
-      "Epoch 451, Loss: 106.7860, Best: 71.6566\n",
-      "Epoch 452, Loss: 79.2251, Best: 71.6566\n",
-      "Epoch 453, Loss: 98.8725, Best: 71.6566\n",
-      "Epoch 454, Loss: 100.7244, Best: 71.6566\n",
-      "Epoch 455, Loss: 88.6863, Best: 71.6566\n",
-      "Epoch 456, Loss: 80.6855, Best: 71.6566\n",
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-      "Epoch 458, Loss: 97.6445, Best: 71.6566\n",
-      "Epoch 459, Loss: 105.2566, Best: 71.6566\n",
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-      "Epoch 461, Loss: 98.1648, Best: 71.6566\n",
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-      "Epoch 463, Loss: 83.7817, Best: 71.6566\n",
-      "Epoch 464, Loss: 112.8876, Best: 71.6566\n",
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-      "Epoch 466, Loss: 86.0228, Best: 71.6566\n",
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-      "Epoch 468, Loss: 81.0090, Best: 71.6566\n",
-      "Epoch 469, Loss: 94.2619, Best: 71.6566\n",
-      "Epoch 470, Loss: 86.6816, Best: 71.6566\n",
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-      "Epoch 473, Loss: 96.3000, Best: 71.6566\n",
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-      "Epoch 475, Loss: 80.7901, Best: 71.6566\n",
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-      "Epoch 478, Loss: 93.0542, Best: 71.6566\n",
-      "Epoch 479, Loss: 91.3991, Best: 71.6566\n",
-      "Epoch 480, Loss: 91.4462, Best: 71.6566\n",
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-      "Epoch 484, Loss: 91.7263, Best: 71.6566\n",
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-      "Epoch 486, Loss: 95.0507, Best: 71.6566\n",
-      "Epoch 487, Loss: 97.9931, Best: 71.6566\n",
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-      "Epoch 489, Loss: 114.3002, Best: 71.6566\n",
-      "Epoch 490, Loss: 101.7474, Best: 71.6566\n",
-      "Epoch 491, Loss: 89.2102, Best: 71.6566\n",
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-      "Epoch 493, Loss: 111.5289, Best: 71.6566\n",
-      "Epoch 494, Loss: 79.5843, Best: 71.6566\n",
-      "Epoch 495, Loss: 79.3871, Best: 71.6566\n",
-      "Epoch 496, Loss: 85.9388, Best: 71.6566\n",
-      "Epoch 497, Loss: 89.1858, Best: 71.6566\n",
-      "Epoch 498, Loss: 90.3574, Best: 71.6566\n",
-      "Epoch 499, Loss: 89.9115, Best: 71.6566\n",
-      "Epoch 500, Loss: 96.9875, Best: 71.6566\n",
-      "Epoch 501, Loss: 95.6944, Best: 71.6566\n",
-      "Epoch 502, Loss: 87.3376, Best: 71.6566\n",
-      "Epoch 503, Loss: 110.0442, Best: 71.6566\n",
-      "Epoch 504, Loss: 84.7448, Best: 71.6566\n",
-      "Epoch 505, Loss: 76.5012, Best: 71.6566\n",
-      "Epoch 506, Loss: 83.2097, Best: 71.6566\n",
-      "Epoch 507, Loss: 90.4945, Best: 71.6566\n",
-      "Epoch 508, Loss: 80.8260, Best: 71.6566\n",
-      "Epoch 509, Loss: 84.7994, Best: 71.6566\n",
-      "Epoch 510, Loss: 86.6034, Best: 71.6566\n",
-      "Epoch 511, Loss: 99.2810, Best: 71.6566\n",
-      "Epoch 512, Loss: 91.5139, Best: 71.6566\n",
-      "Epoch 513, Loss: 108.1034, Best: 71.6566\n",
-      "Epoch 514, Loss: 77.7861, Best: 71.6566\n",
-      "Epoch 515, Loss: 98.5245, Best: 71.6566\n",
-      "Epoch 516, Loss: 72.5038, Best: 71.6566\n",
-      "Epoch 517, Loss: 210.8491, Best: 71.6566\n",
-      "Epoch 518, Loss: 100.5975, Best: 71.6566\n",
-      "Epoch 519, Loss: 115.5219, Best: 71.6566\n",
-      "Epoch 520, Loss: 91.1577, Best: 71.6566\n",
-      "Epoch 521, Loss: 89.1902, Best: 71.6566\n",
-      "Epoch 522, Loss: 81.1619, Best: 71.6566\n",
-      "Epoch 523, Loss: 83.5617, Best: 71.6566\n",
-      "Epoch 524, Loss: 88.7066, Best: 71.6566\n",
-      "Epoch 525, Loss: 93.2217, Best: 71.6566\n",
-      "Epoch 526, Loss: 93.7433, Best: 71.6566\n",
-      "Epoch 527, Loss: 110.8036, Best: 71.6566\n",
-      "Epoch 528, Loss: 87.0887, Best: 71.6566\n",
-      "Epoch 529, Loss: 75.5715, Best: 71.6566\n",
-      "Epoch 530, Loss: 85.9647, Best: 71.6566\n",
-      "Epoch 531, Loss: 83.1962, Best: 71.6566\n",
-      "Epoch 532, Loss: 87.7155, Best: 71.6566\n",
-      "Epoch 533, Loss: 90.5912, Best: 71.6566\n",
-      "Epoch 534, Loss: 86.6129, Best: 71.6566\n",
-      "Epoch 535, Loss: 97.3870, Best: 71.6566\n",
-      "Epoch 536, Loss: 87.8612, Best: 71.6566\n",
-      "Epoch 537, Loss: 88.0676, Best: 71.6566\n",
-      "Epoch 538, Loss: 92.4046, Best: 71.6566\n",
-      "Epoch 539, Loss: 80.8935, Best: 71.6566\n",
-      "Epoch 540, Loss: 75.1593, Best: 71.6566\n",
-      "Epoch 541, Loss: 91.8944, Best: 71.6566\n",
-      "Epoch 542, Loss: 108.6663, Best: 71.6566\n",
-      "Epoch 543, Loss: 78.9852, Best: 71.6566\n",
-      "Epoch 544, Loss: 86.5010, Best: 71.6566\n",
-      "Epoch 545, Loss: 75.7874, Best: 71.6566\n",
-      "Epoch 546, Loss: 114.3061, Best: 71.6566\n",
-      "Epoch 547, Loss: 83.3077, Best: 71.6566\n",
-      "Epoch 548, Loss: 88.6152, Best: 71.6566\n",
-      "Epoch 549, Loss: 86.1964, Best: 71.6566\n",
-      "Epoch 550, Loss: 87.4269, Best: 71.6566\n",
-      "Epoch 551, Loss: 116.4616, Best: 71.6566\n",
-      "Epoch 552, Loss: 109.0549, Best: 71.6566\n",
-      "Epoch 553, Loss: 83.9701, Best: 71.6566\n",
-      "Epoch 554, Loss: 90.6430, Best: 71.6566\n",
-      "Epoch 555, Loss: 102.4403, Best: 71.6566\n",
-      "Epoch 556, Loss: 79.7428, Best: 71.6566\n",
-      "Epoch 557, Loss: 90.5097, Best: 71.6566\n",
-      "Epoch 558, Loss: 79.3528, Best: 71.6566\n",
-      "Epoch 559, Loss: 77.4585, Best: 71.6566\n",
-      "Epoch 560, Loss: 84.9884, Best: 71.6566\n",
-      "Epoch 561, Loss: 91.2550, Best: 71.6566\n",
-      "Epoch 562, Loss: 109.9776, Best: 71.6566\n",
-      "Epoch 563, Loss: 83.5097, Best: 71.6566\n",
-      "Epoch 564, Loss: 96.2035, Best: 71.6566\n",
-      "Epoch 565, Loss: 84.8147, Best: 71.6566\n",
-      "Epoch 566, Loss: 101.4509, Best: 71.6566\n",
-      "Epoch 567, Loss: 74.4018, Best: 71.6566\n",
-      "Epoch 568, Loss: 79.3904, Best: 71.6566\n",
-      "Epoch 569, Loss: 105.6787, Best: 71.6566\n",
-      "Epoch 570, Loss: 81.4836, Best: 71.6566\n",
-      "Epoch 571, Loss: 98.2589, Best: 71.6566\n",
-      "Epoch 572, Loss: 101.2248, Best: 71.6566\n",
-      "Epoch 573, Loss: 94.3687, Best: 71.6566\n",
-      "Epoch 574, Loss: 83.2000, Best: 71.6566\n",
-      "Epoch 575, Loss: 94.1261, Best: 71.6566\n",
-      "Epoch 576, Loss: 107.0014, Best: 71.6566\n",
-      "Epoch 577, Loss: 78.9843, Best: 71.6566\n",
-      "Epoch 578, Loss: 89.8427, Best: 71.6566\n",
-      "Epoch 579, Loss: 86.3054, Best: 71.6566\n",
-      "Epoch 580, Loss: 117.1487, Best: 71.6566\n",
-      "Epoch 581, Loss: 80.1376, Best: 71.6566\n",
-      "Epoch 582, Loss: 81.6411, Best: 71.6566\n",
-      "Epoch 583, Loss: 78.9459, Best: 71.6566\n",
-      "Epoch 584, Loss: 92.0992, Best: 71.6566\n",
-      "Epoch 585, Loss: 83.7009, Best: 71.6566\n",
-      "Epoch 586, Loss: 116.1223, Best: 71.6566\n",
-      "Epoch 587, Loss: 86.6355, Best: 71.6566\n",
-      "Epoch 588, Loss: 80.2416, Best: 71.6566\n",
-      "Epoch 589, Loss: 80.6633, Best: 71.6566\n",
-      "Epoch 590, Loss: 73.3696, Best: 71.6566\n",
-      "Epoch 591, Loss: 92.8108, Best: 71.6566\n",
-      "Epoch 592, Loss: 76.7860, Best: 71.6566\n",
-      "Epoch 593, Loss: 83.1468, Best: 71.6566\n",
-      "Epoch 594, Loss: 97.1735, Best: 71.6566\n",
-      "Epoch 595, Loss: 87.7678, Best: 71.6566\n",
-      "Epoch 596, Loss: 78.0777, Best: 71.6566\n",
-      "Epoch 597, Loss: 104.2475, Best: 71.6566\n",
-      "Epoch 598, Loss: 88.9909, Best: 71.6566\n",
-      "Epoch 599, Loss: 102.9690, Best: 71.6566\n",
-      "Epoch 600, Loss: 100.0092, Best: 71.6566\n",
-      "Epoch 601, Loss: 76.0658, Best: 71.6566\n",
-      "Epoch 602, Loss: 72.5593, Best: 71.6566\n",
-      "Epoch 603, Loss: 91.1090, Best: 71.6566\n",
-      "Epoch 604, Loss: 75.1826, Best: 71.6566\n",
-      "Epoch 605, Loss: 91.0820, Best: 71.6566\n",
-      "Epoch 606, Loss: 112.5631, Best: 71.6566\n",
-      "Epoch 607, Loss: 85.3402, Best: 71.6566\n",
-      "Epoch 608, Loss: 80.1007, Best: 71.6566\n",
-      "Epoch 609, Loss: 79.0761, Best: 71.6566\n",
-      "Epoch 610, Loss: 86.0141, Best: 71.6566\n",
-      "Epoch 611, Loss: 80.4485, Best: 71.6566\n",
-      "Epoch 612, Loss: 106.9632, Best: 71.6566\n",
-      "Epoch 613, Loss: 94.7631, Best: 71.6566\n",
-      "Epoch 614, Loss: 82.5797, Best: 71.6566\n",
-      "Epoch 615, Loss: 77.7724, Best: 71.6566\n",
-      "Epoch 616, Loss: 94.9914, Best: 71.6566\n",
-      "Epoch 617, Loss: 105.7957, Best: 71.6566\n",
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-      "Epoch 655, Loss: 91.8164, Best: 71.6566\n",
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-      "Epoch 666, Loss: 82.1714, Best: 71.6566\n",
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-      "Epoch 675, Loss: 91.3030, Best: 71.6566\n",
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-      "Epoch 700, Loss: 89.5030, Best: 71.6566\n",
-      "Epoch 701, Loss: 96.0979, Best: 71.6566\n",
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-      "Epoch 704, Loss: 87.0954, Best: 71.6566\n",
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-      "Epoch 720, Loss: 76.1101, Best: 71.6566\n",
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-      "Epoch 727, Loss: 78.2522, Best: 71.6566\n",
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-      "Epoch 731, Loss: 91.3820, Best: 71.6566\n",
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-      "Epoch 733, Loss: 100.0004, Best: 71.6566\n",
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-      "Epoch 737, Loss: 106.4969, Best: 71.6566\n",
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-      "Epoch 748, Loss: 72.4540, Best: 71.6566\n",
-      "Epoch 749, Loss: 99.0123, Best: 71.6566\n",
-      "Epoch 750, Loss: 78.8688, Best: 71.6566\n",
-      "Epoch 751, Loss: 74.3567, Best: 71.6566\n",
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-      "Epoch 753, Loss: 94.8524, Best: 71.6566\n",
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-      "Epoch 756, Loss: 106.6027, Best: 71.6566\n",
-      "Epoch 757, Loss: 81.4866, Best: 71.6566\n",
-      "Epoch 758, Loss: 78.2483, Best: 71.6566\n",
-      "Epoch 759, Loss: 112.7803, Best: 71.6566\n",
-      "Epoch 760, Loss: 87.8018, Best: 71.6566\n",
-      "Epoch 761, Loss: 94.6195, Best: 71.6566\n",
-      "Epoch 762, Loss: 109.1360, Best: 71.6566\n",
-      "Epoch 763, Loss: 83.5569, Best: 71.6566\n",
-      "Epoch 764, Loss: 87.1927, Best: 71.6566\n",
-      "Epoch 765, Loss: 80.0392, Best: 71.6566\n",
-      "Epoch 766, Loss: 86.6863, Best: 71.6566\n",
-      "Epoch 767, Loss: 93.8454, Best: 71.6566\n",
-      "Epoch 768, Loss: 102.2791, Best: 71.6566\n",
-      "Epoch 769, Loss: 94.2684, Best: 71.6566\n",
-      "Epoch 770, Loss: 81.8275, Best: 71.6566\n",
-      "Epoch 771, Loss: 101.0727, Best: 71.6566\n",
-      "Epoch 772, Loss: 99.7235, Best: 71.6566\n",
-      "Epoch 773, Loss: 79.3227, Best: 71.6566\n",
-      "Epoch 774, Loss: 77.1319, Best: 71.6566\n",
-      "Epoch 775, Loss: 100.2469, Best: 71.6566\n",
-      "Epoch 776, Loss: 98.4926, Best: 71.6566\n",
-      "Epoch 777, Loss: 83.9000, Best: 71.6566\n",
-      "Epoch 778, Loss: 102.6204, Best: 71.6566\n",
-      "Epoch 779, Loss: 79.5829, Best: 71.6566\n",
-      "Epoch 780, Loss: 113.5730, Best: 71.6566\n",
-      "Epoch 781, Loss: 74.8816, Best: 71.6566\n",
-      "Epoch 782, Loss: 75.3641, Best: 71.6566\n",
-      "Epoch 783, Loss: 82.3128, Best: 71.6566\n",
-      "Epoch 784, Loss: 81.7736, Best: 71.6566\n",
-      "Epoch 785, Loss: 80.0778, Best: 71.6566\n",
-      "Epoch 786, Loss: 90.6963, Best: 71.6566\n",
-      "Epoch 787, Loss: 95.0663, Best: 71.6566\n",
-      "Epoch 788, Loss: 84.3224, Best: 71.6566\n",
-      "Epoch 789, Loss: 84.3400, Best: 71.6566\n",
-      "Epoch 790, Loss: 81.4791, Best: 71.6566\n",
-      "Epoch 791, Loss: 87.9800, Best: 71.6566\n",
-      "Epoch 792, Loss: 103.5832, Best: 71.6566\n",
-      "Epoch 793, Loss: 75.5513, Best: 71.6566\n",
-      "Epoch 794, Loss: 112.7070, Best: 71.6566\n",
-      "Epoch 795, Loss: 87.0739, Best: 71.6566\n",
-      "Epoch 796, Loss: 82.6713, Best: 71.6566\n",
-      "Epoch 797, Loss: 84.3592, Best: 71.6566\n",
-      "Epoch 798, Loss: 78.6968, Best: 71.6566\n",
-      "Epoch 799, Loss: 109.3717, Best: 71.6566\n",
-      "Epoch 800, Loss: 97.4668, Best: 71.6566\n",
-      "Epoch 801, Loss: 82.0647, Best: 71.6566\n",
-      "Epoch 802, Loss: 98.9612, Best: 71.6566\n",
-      "Epoch 803, Loss: 73.5553, Best: 71.6566\n",
-      "Epoch 804, Loss: 95.9085, Best: 71.6566\n",
-      "Epoch 805, Loss: 81.5829, Best: 71.6566\n",
-      "Epoch 806, Loss: 82.6034, Best: 71.6566\n",
-      "Epoch 807, Loss: 97.8360, Best: 71.6566\n",
-      "Epoch 808, Loss: 90.6062, Best: 71.6566\n",
-      "Epoch 809, Loss: 103.1622, Best: 71.6566\n",
-      "Epoch 810, Loss: 85.4759, Best: 71.6566\n",
-      "Epoch 811, Loss: 87.6264, Best: 71.6566\n",
-      "Epoch 812, Loss: 101.0745, Best: 71.6566\n",
-      "Epoch 813, Loss: 83.8179, Best: 71.6566\n",
-      "Epoch 814, Loss: 85.2580, Best: 71.6566\n",
-      "Epoch 815, Loss: 78.9194, Best: 71.6566\n",
-      "Epoch 816, Loss: 99.2257, Best: 71.6566\n",
-      "Epoch 817, Loss: 76.7189, Best: 71.6566\n",
-      "Epoch 818, Loss: 82.8305, Best: 71.6566\n",
-      "Epoch 819, Loss: 86.6021, Best: 71.6566\n",
-      "Epoch 820, Loss: 85.7525, Best: 71.6566\n",
-      "Epoch 821, Loss: 88.9553, Best: 71.6566\n",
-      "Epoch 822, Loss: 90.2106, Best: 71.6566\n",
-      "Epoch 823, Loss: 87.5560, Best: 71.6566\n",
-      "Epoch 824, Loss: 89.0272, Best: 71.6566\n",
-      "Epoch 825, Loss: 82.2854, Best: 71.6566\n",
-      "Epoch 826, Loss: 82.3681, Best: 71.6566\n",
-      "Epoch 827, Loss: 79.8801, Best: 71.6566\n",
-      "Epoch 828, Loss: 74.4743, Best: 71.6566\n",
-      "Epoch 829, Loss: 74.5726, Best: 71.6566\n",
-      "Epoch 830, Loss: 73.3426, Best: 71.6566\n",
-      "Epoch 831, Loss: 101.6914, Best: 71.6566\n",
-      "Epoch 832, Loss: 77.1363, Best: 71.6566\n",
-      "Epoch 833, Loss: 84.7797, Best: 71.6566\n",
-      "Epoch 834, Loss: 108.1420, Best: 71.6566\n",
-      "Epoch 835, Loss: 91.0490, Best: 71.6566\n",
-      "Epoch 836, Loss: 73.6595, Best: 71.6566\n",
-      "Epoch 837, Loss: 79.8500, Best: 71.6566\n",
-      "Epoch 838, Loss: 75.1661, Best: 71.6566\n",
-      "Epoch 839, Loss: 101.6622, Best: 71.6566\n",
-      "Epoch 840, Loss: 91.7469, Best: 71.6566\n",
-      "Epoch 841, Loss: 90.9470, Best: 71.6566\n",
-      "Epoch 842, Loss: 96.2127, Best: 71.6566\n",
-      "Epoch 843, Loss: 73.3989, Best: 71.6566\n",
-      "Epoch 844, Loss: 104.1774, Best: 71.6566\n",
-      "Epoch 845, Loss: 80.1789, Best: 71.6566\n",
-      "Epoch 846, Loss: 90.4103, Best: 71.6566\n",
-      "Epoch 847, Loss: 75.5598, Best: 71.6566\n",
-      "Epoch 848, Loss: 78.5539, Best: 71.6566\n",
-      "Epoch 849, Loss: 92.2811, Best: 71.6566\n",
-      "Epoch 850, Loss: 88.5127, Best: 71.6566\n",
-      "Epoch 851, Loss: 104.2279, Best: 71.6566\n",
-      "Epoch 852, Loss: 120.1542, Best: 71.6566\n",
-      "Epoch 853, Loss: 109.2860, Best: 71.6566\n",
-      "Epoch 854, Loss: 97.5891, Best: 71.6566\n",
-      "Epoch 855, Loss: 88.9089, Best: 71.6566\n",
-      "Epoch 856, Loss: 93.2710, Best: 71.6566\n",
-      "Epoch 857, Loss: 90.7053, Best: 71.6566\n",
-      "Epoch 858, Loss: 104.9319, Best: 71.6566\n",
-      "Epoch 859, Loss: 92.3869, Best: 71.6566\n",
-      "Epoch 860, Loss: 104.8260, Best: 71.6566\n",
-      "Epoch 861, Loss: 90.3588, Best: 71.6566\n",
-      "Epoch 862, Loss: 96.9233, Best: 71.6566\n",
-      "Epoch 863, Loss: 98.7391, Best: 71.6566\n",
-      "Epoch 864, Loss: 105.8905, Best: 71.6566\n",
-      "Epoch 865, Loss: 84.2122, Best: 71.6566\n",
-      "Epoch 866, Loss: 90.1286, Best: 71.6566\n",
-      "Epoch 867, Loss: 100.0739, Best: 71.6566\n",
-      "Epoch 868, Loss: 91.6090, Best: 71.6566\n",
-      "Epoch 869, Loss: 90.7653, Best: 71.6566\n",
-      "Epoch 870, Loss: 95.4327, Best: 71.6566\n",
-      "Epoch 871, Loss: 72.4551, Best: 71.6566\n",
-      "Epoch 872, Loss: 100.8727, Best: 71.6566\n",
-      "Epoch 873, Loss: 80.8452, Best: 71.6566\n",
-      "Epoch 874, Loss: 94.6424, Best: 71.6566\n",
-      "Epoch 875, Loss: 87.1226, Best: 71.6566\n",
-      "Epoch 876, Loss: 89.7530, Best: 71.6566\n",
-      "Epoch 877, Loss: 87.3747, Best: 71.6566\n",
-      "Epoch 878, Loss: 87.0089, Best: 71.6566\n",
-      "Epoch 879, Loss: 91.7773, Best: 71.6566\n",
-      "Epoch 880, Loss: 78.0094, Best: 71.6566\n",
-      "Epoch 881, Loss: 77.5190, Best: 71.6566\n",
-      "Epoch 882, Loss: 83.4223, Best: 71.6566\n",
-      "Epoch 883, Loss: 77.9126, Best: 71.6566\n",
-      "Epoch 884, Loss: 93.3256, Best: 71.6566\n",
-      "Epoch 885, Loss: 99.5105, Best: 71.6566\n",
-      "Epoch 886, Loss: 82.7231, Best: 71.6566\n",
-      "Epoch 887, Loss: 87.4226, Best: 71.6566\n",
-      "Epoch 888, Loss: 86.0051, Best: 71.6566\n",
-      "Epoch 889, Loss: 85.2889, Best: 71.6566\n",
-      "Epoch 890, Loss: 81.3050, Best: 71.6566\n",
-      "Epoch 891, Loss: 94.6063, Best: 71.6566\n",
-      "Epoch 892, Loss: 99.0845, Best: 71.6566\n",
-      "Epoch 893, Loss: 87.1077, Best: 71.6566\n",
-      "Epoch 894, Loss: 86.5395, Best: 71.6566\n",
-      "Epoch 895, Loss: 76.7924, Best: 71.6566\n",
-      "Epoch 896, Loss: 75.2303, Best: 71.6566\n",
-      "Epoch 897, Loss: 112.1759, Best: 71.6566\n",
-      "Epoch 898, Loss: 87.4383, Best: 71.6566\n",
-      "Epoch 899, Loss: 91.6965, Best: 71.6566\n",
-      "Epoch 900, Loss: 82.9723, Best: 71.6566\n",
-      "Epoch 901, Loss: 105.4059, Best: 71.6566\n",
-      "Epoch 902, Loss: 81.7274, Best: 71.6566\n",
-      "Epoch 903, Loss: 73.3230, Best: 71.6566\n",
-      "Epoch 904, Loss: 99.6853, Best: 71.6566\n",
-      "Epoch 905, Loss: 73.9141, Best: 71.6566\n",
-      "Epoch 906, Loss: 86.5259, Best: 71.6566\n",
-      "Epoch 907, Loss: 79.1633, Best: 71.6566\n",
-      "Epoch 908, Loss: 90.5851, Best: 71.6566\n",
-      "Epoch 909, Loss: 91.1909, Best: 71.6566\n",
-      "Epoch 910, Loss: 82.1601, Best: 71.6566\n",
-      "Epoch 911, Loss: 89.6630, Best: 71.6566\n",
-      "Epoch 912, Loss: 97.2128, Best: 71.6566\n",
-      "Epoch 913, Loss: 110.4815, Best: 71.6566\n",
-      "Epoch 914, Loss: 73.3690, Best: 71.6566\n",
-      "Epoch 915, Loss: 72.9327, Best: 71.6566\n",
-      "Epoch 916, Loss: 77.1212, Best: 71.6566\n",
-      "Epoch 917, Loss: 86.8338, Best: 71.6566\n",
-      "Epoch 918, Loss: 83.3899, Best: 71.6566\n",
-      "Epoch 919, Loss: 78.8511, Best: 71.6566\n",
-      "Epoch 920, Loss: 107.0881, Best: 71.6566\n",
-      "Epoch 921, Loss: 99.6135, Best: 71.6566\n",
-      "Epoch 922, Loss: 93.6679, Best: 71.6566\n",
-      "Epoch 923, Loss: 92.5431, Best: 71.6566\n",
-      "Epoch 924, Loss: 95.6670, Best: 71.6566\n",
-      "Epoch 925, Loss: 108.1537, Best: 71.6566\n",
-      "Epoch 926, Loss: 98.8890, Best: 71.6566\n",
-      "Epoch 927, Loss: 101.3259, Best: 71.6566\n",
-      "Epoch 928, Loss: 87.5771, Best: 71.6566\n",
-      "Epoch 929, Loss: 87.2225, Best: 71.6566\n",
-      "Epoch 930, Loss: 88.9933, Best: 71.6566\n",
-      "Epoch 931, Loss: 91.4359, Best: 71.6566\n",
-      "Epoch 932, Loss: 83.9828, Best: 71.6566\n",
-      "Epoch 933, Loss: 93.8722, Best: 71.6566\n",
-      "Epoch 934, Loss: 88.2050, Best: 71.6566\n",
-      "Epoch 935, Loss: 82.4957, Best: 71.6566\n",
-      "Epoch 936, Loss: 83.0290, Best: 71.6566\n",
-      "Epoch 937, Loss: 82.2928, Best: 71.6566\n",
-      "Epoch 938, Loss: 97.4214, Best: 71.6566\n",
-      "Epoch 939, Loss: 77.9610, Best: 71.6566\n",
-      "Epoch 940, Loss: 85.9096, Best: 71.6566\n",
-      "Epoch 941, Loss: 79.6753, Best: 71.6566\n",
-      "Epoch 942, Loss: 77.5916, Best: 71.6566\n",
-      "Epoch 943, Loss: 75.2132, Best: 71.6566\n",
-      "Epoch 944, Loss: 72.3892, Best: 71.6566\n",
-      "Epoch 945, Loss: 79.7935, Best: 71.6566\n",
-      "Epoch 946, Loss: 85.1703, Best: 71.6566\n",
-      "Epoch 947, Loss: 94.6266, Best: 71.6566\n",
-      "Epoch 948, Loss: 82.1760, Best: 71.6566\n",
-      "Epoch 949, Loss: 94.1843, Best: 71.6566\n",
-      "Epoch 950, Loss: 101.9082, Best: 71.6566\n",
-      "Epoch 951, Loss: 107.1570, Best: 71.6566\n",
-      "Epoch 952, Loss: 106.3434, Best: 71.6566\n",
-      "Epoch 953, Loss: 90.4657, Best: 71.6566\n",
-      "Epoch 954, Loss: 83.1249, Best: 71.6566\n",
-      "Epoch 955, Loss: 72.9930, Best: 71.6566\n",
-      "Epoch 956, Loss: 100.2402, Best: 71.6566\n",
-      "Epoch 957, Loss: 78.6722, Best: 71.6566\n",
-      "Epoch 958, Loss: 100.2998, Best: 71.6566\n",
-      "Epoch 959, Loss: 77.4600, Best: 71.6566\n",
-      "Epoch 960, Loss: 80.4046, Best: 71.6566\n",
-      "Epoch 961, Loss: 105.6686, Best: 71.6566\n",
-      "Epoch 962, Loss: 125.1452, Best: 71.6566\n",
-      "Epoch 963, Loss: 80.2376, Best: 71.6566\n",
-      "Epoch 964, Loss: 79.2270, Best: 71.6566\n",
-      "Epoch 965, Loss: 87.5838, Best: 71.6566\n",
-      "Epoch 966, Loss: 100.1205, Best: 71.6566\n",
-      "Epoch 967, Loss: 101.6591, Best: 71.6566\n",
-      "Epoch 968, Loss: 82.8933, Best: 71.6566\n",
-      "Epoch 969, Loss: 82.3027, Best: 71.6566\n",
-      "Epoch 970, Loss: 105.9710, Best: 71.6566\n",
-      "Epoch 971, Loss: 80.9257, Best: 71.6566\n",
-      "Epoch 972, Loss: 82.7084, Best: 71.6566\n",
-      "Epoch 973, Loss: 81.8160, Best: 71.6566\n",
-      "Epoch 974, Loss: 91.3733, Best: 71.6566\n",
-      "Epoch 975, Loss: 92.8716, Best: 71.6566\n",
-      "Epoch 976, Loss: 87.9596, Best: 71.6566\n",
-      "Epoch 977, Loss: 88.0108, Best: 71.6566\n",
-      "Epoch 978, Loss: 89.1949, Best: 71.6566\n",
-      "Epoch 979, Loss: 74.3420, Best: 71.6566\n",
-      "Epoch 980, Loss: 86.7933, Best: 71.6566\n",
-      "Epoch 981, Loss: 91.4234, Best: 71.6566\n",
-      "Epoch 982, Loss: 72.9057, Best: 71.6566\n",
-      "Epoch 983, Loss: 84.9100, Best: 71.6566\n",
-      "Epoch 984, Loss: 112.3243, Best: 71.6566\n",
-      "Epoch 985, Loss: 87.6508, Best: 71.6566\n",
-      "Epoch 986, Loss: 83.5459, Best: 71.6566\n",
-      "Epoch 987, Loss: 107.1587, Best: 71.6566\n",
-      "Epoch 988, Loss: 82.0884, Best: 71.6566\n",
-      "Epoch 989, Loss: 85.5340, Best: 71.6566\n",
-      "Epoch 990, Loss: 91.1993, Best: 71.6566\n",
-      "Epoch 991, Loss: 96.8115, Best: 71.6566\n",
-      "Epoch 992, Loss: 78.3539, Best: 71.6566\n",
-      "Epoch 993, Loss: 77.1025, Best: 71.6566\n",
-      "Epoch 994, Loss: 109.5030, Best: 71.6566\n",
-      "Epoch 995, Loss: 78.8584, Best: 71.6566\n",
-      "Epoch 996, Loss: 107.6427, Best: 71.6566\n",
-      "Epoch 997, Loss: 82.9101, Best: 71.6566\n",
-      "Epoch 998, Loss: 112.1848, Best: 71.6566\n",
-      "Epoch 999, Loss: 81.1938, Best: 71.6566\n",
-      "Epoch 1000, Loss: 91.3226, Best: 71.6566\n",
+      "GPU memory allocated: 49.4 MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\rrayy\\anaconda3\\envs\\diva\\Lib\\site-packages\\pysdtw\\sdtw_cuda.py:19: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\torch\\csrc\\tensor\\python_tensor.cpp:80.)\n",
+      "  gamma = torch.cuda.FloatTensor([gamma])\n",
+      "c:\\Users\\rrayy\\anaconda3\\envs\\diva\\Lib\\site-packages\\numba\\cuda\\dispatcher.py:536: NumbaPerformanceWarning: \u001b[1mGrid size 8 will likely result in GPU under-utilization due to low occupancy.\u001b[0m\n",
+      "  warn(NumbaPerformanceWarning(msg))\n",
+      "c:\\Users\\rrayy\\anaconda3\\envs\\diva\\Lib\\site-packages\\numba\\cuda\\dispatcher.py:536: NumbaPerformanceWarning: \u001b[1mGrid size 8 will likely result in GPU under-utilization due to low occupancy.\u001b[0m\n",
+      "  warn(NumbaPerformanceWarning(msg))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPU memory allocated: 215.7 MB\n",
+      "GPU memory allocated: 215.7 MB\n",
+      "GPU memory allocated: 215.7 MB\n",
+      "GPU memory allocated: 215.6 MB\n",
+      "Epoch 1, Loss: 31.8012, Best: 31.8012\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\rrayy\\anaconda3\\envs\\diva\\Lib\\site-packages\\numba\\cuda\\dispatcher.py:536: NumbaPerformanceWarning: \u001b[1mGrid size 2 will likely result in GPU under-utilization due to low occupancy.\u001b[0m\n",
+      "  warn(NumbaPerformanceWarning(msg))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2, Loss: 30.0876, Best: 30.0876\n",
+      "Epoch 3, Loss: 28.7601, Best: 28.7601\n",
+      "Epoch 4, Loss: 27.6987, Best: 27.6987\n",
+      "Epoch 5, Loss: 29.8720, Best: 27.6987\n",
+      "Epoch 6, Loss: 28.4903, Best: 27.6987\n",
+      "Epoch 7, Loss: 30.4468, Best: 27.6987\n",
+      "Epoch 8, Loss: 31.2474, Best: 27.6987\n",
+      "Epoch 9, Loss: 28.2623, Best: 27.6987\n",
+      "Epoch 10, Loss: 30.6561, Best: 27.6987\n",
+      "Epoch 11, Loss: 27.8391, Best: 27.6987\n",
+      "Epoch 12, Loss: 28.8397, Best: 27.6987\n",
+      "Epoch 13, Loss: 29.4137, Best: 27.6987\n",
+      "Epoch 14, Loss: 31.8415, Best: 27.6987\n",
+      "Epoch 15, Loss: 28.2142, Best: 27.6987\n",
+      "Epoch 16, Loss: 28.6523, Best: 27.6987\n",
+      "Epoch 17, Loss: 33.2484, Best: 27.6987\n",
+      "Epoch 18, Loss: 28.9170, Best: 27.6987\n",
+      "Epoch 19, Loss: 28.2520, Best: 27.6987\n",
+      "Epoch 20, Loss: 29.9245, Best: 27.6987\n",
+      "Epoch 21, Loss: 31.1418, Best: 27.6987\n",
+      "Epoch 22, Loss: 29.4461, Best: 27.6987\n",
+      "Epoch 23, Loss: 29.7949, Best: 27.6987\n",
+      "Epoch 24, Loss: 30.6738, Best: 27.6987\n",
+      "Epoch 25, Loss: 28.7226, Best: 27.6987\n",
+      "Epoch 26, Loss: 27.1757, Best: 27.1757\n",
+      "Epoch 27, Loss: 29.8865, Best: 27.1757\n",
+      "Epoch 28, Loss: 28.3069, Best: 27.1757\n",
+      "Epoch 29, Loss: 30.9018, Best: 27.1757\n",
+      "Epoch 30, Loss: 27.5814, Best: 27.1757\n",
+      "Epoch 31, Loss: 28.8841, Best: 27.1757\n",
+      "Epoch 32, Loss: 30.5673, Best: 27.1757\n",
+      "Epoch 33, Loss: 29.1065, Best: 27.1757\n",
+      "Epoch 34, Loss: 29.5791, Best: 27.1757\n",
+      "Epoch 35, Loss: 29.4460, Best: 27.1757\n",
+      "Epoch 36, Loss: 27.2594, Best: 27.1757\n",
+      "Epoch 37, Loss: 27.8146, Best: 27.1757\n",
+      "Epoch 38, Loss: 29.9280, Best: 27.1757\n",
+      "Epoch 39, Loss: 28.4564, Best: 27.1757\n",
+      "Epoch 40, Loss: 29.2735, Best: 27.1757\n",
+      "Epoch 41, Loss: 28.6287, Best: 27.1757\n",
+      "Epoch 42, Loss: 29.3652, Best: 27.1757\n",
+      "Epoch 43, Loss: 29.6544, Best: 27.1757\n",
+      "Epoch 44, Loss: 27.1365, Best: 27.1365\n",
+      "Epoch 45, Loss: 29.6310, Best: 27.1365\n",
+      "Epoch 46, Loss: 27.8881, Best: 27.1365\n",
+      "Epoch 47, Loss: 26.9177, Best: 26.9177\n",
+      "Epoch 48, Loss: 28.3944, Best: 26.9177\n",
+      "Epoch 49, Loss: 29.1931, Best: 26.9177\n",
+      "Epoch 50, Loss: 28.6958, Best: 26.9177\n",
+      "Epoch 51, Loss: 27.2254, Best: 26.9177\n",
+      "Epoch 52, Loss: 30.1898, Best: 26.9177\n",
+      "Epoch 53, Loss: 30.0429, Best: 26.9177\n",
+      "Epoch 54, Loss: 27.3671, Best: 26.9177\n",
+      "Epoch 55, Loss: 28.8588, Best: 26.9177\n",
+      "Epoch 56, Loss: 29.3659, Best: 26.9177\n",
+      "Epoch 57, Loss: 28.6679, Best: 26.9177\n",
+      "Epoch 58, Loss: 30.0693, Best: 26.9177\n",
+      "Epoch 59, Loss: 27.9074, Best: 26.9177\n",
+      "Epoch 60, Loss: 29.9090, Best: 26.9177\n",
+      "Epoch 61, Loss: 27.4494, Best: 26.9177\n",
+      "Epoch 62, Loss: 27.0492, Best: 26.9177\n",
+      "Epoch 63, Loss: 29.9826, Best: 26.9177\n",
+      "Epoch 64, Loss: 29.9345, Best: 26.9177\n",
+      "Epoch 65, Loss: 27.6784, Best: 26.9177\n",
+      "Epoch 66, Loss: 27.1073, Best: 26.9177\n",
+      "Epoch 67, Loss: 27.7139, Best: 26.9177\n",
+      "Epoch 68, Loss: 28.4931, Best: 26.9177\n",
+      "Epoch 69, Loss: 27.0494, Best: 26.9177\n",
+      "Epoch 70, Loss: 28.5949, Best: 26.9177\n",
+      "Epoch 71, Loss: 28.4369, Best: 26.9177\n",
+      "Epoch 72, Loss: 29.9060, Best: 26.9177\n",
+      "Epoch 73, Loss: 31.0239, Best: 26.9177\n",
+      "Epoch 74, Loss: 31.3072, Best: 26.9177\n",
+      "Epoch 75, Loss: 27.7725, Best: 26.9177\n",
+      "Epoch 76, Loss: 29.5615, Best: 26.9177\n",
+      "Epoch 77, Loss: 33.0933, Best: 26.9177\n",
+      "Epoch 78, Loss: 29.6956, Best: 26.9177\n",
+      "Epoch 79, Loss: 29.1576, Best: 26.9177\n",
+      "Epoch 80, Loss: 29.9281, Best: 26.9177\n",
+      "Epoch 81, Loss: 29.2404, Best: 26.9177\n",
+      "Epoch 82, Loss: 30.7085, Best: 26.9177\n",
+      "Epoch 83, Loss: 29.0995, Best: 26.9177\n",
+      "Epoch 84, Loss: 28.9291, Best: 26.9177\n",
+      "Epoch 85, Loss: 29.6897, Best: 26.9177\n",
+      "Epoch 86, Loss: 28.8987, Best: 26.9177\n",
+      "Epoch 87, Loss: 29.9157, Best: 26.9177\n",
+      "Epoch 88, Loss: 30.2631, Best: 26.9177\n",
+      "Epoch 89, Loss: 28.9839, Best: 26.9177\n",
+      "Epoch 90, Loss: 31.0453, Best: 26.9177\n",
+      "Epoch 91, Loss: 31.0822, Best: 26.9177\n",
+      "Epoch 92, Loss: 29.5392, Best: 26.9177\n",
+      "Epoch 93, Loss: 27.1469, Best: 26.9177\n",
+      "Epoch 94, Loss: 29.3529, Best: 26.9177\n",
+      "Epoch 95, Loss: 27.1888, Best: 26.9177\n",
+      "Epoch 96, Loss: 28.5152, Best: 26.9177\n",
+      "Epoch 97, Loss: 29.7311, Best: 26.9177\n",
+      "Epoch 98, Loss: 27.6600, Best: 26.9177\n",
+      "Epoch 99, Loss: 28.8791, Best: 26.9177\n",
+      "Epoch 100, Loss: 28.9265, Best: 26.9177\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "Epoch 101, Loss: 29.6929, Best: 26.9177\n",
+      "Epoch 102, Loss: 28.0040, Best: 26.9177\n",
+      "Epoch 103, Loss: 27.8151, Best: 26.9177\n",
+      "Epoch 104, Loss: 30.3495, Best: 26.9177\n",
+      "Epoch 105, Loss: 27.0506, Best: 26.9177\n",
+      "Epoch 106, Loss: 27.3353, Best: 26.9177\n",
+      "Epoch 107, Loss: 29.9350, Best: 26.9177\n",
+      "Epoch 108, Loss: 27.3507, Best: 26.9177\n",
+      "Epoch 109, Loss: 29.2653, Best: 26.9177\n",
+      "Epoch 110, Loss: 27.6020, Best: 26.9177\n",
+      "Epoch 111, Loss: 29.2162, Best: 26.9177\n",
+      "Epoch 112, Loss: 27.0391, Best: 26.9177\n",
+      "Epoch 113, Loss: 31.1836, Best: 26.9177\n",
+      "Epoch 114, Loss: 28.4509, Best: 26.9177\n",
+      "Epoch 115, Loss: 26.6623, Best: 26.6623\n",
+      "Epoch 116, Loss: 30.8701, Best: 26.6623\n",
+      "Epoch 117, Loss: 30.0671, Best: 26.6623\n",
+      "Epoch 118, Loss: 31.6903, Best: 26.6623\n",
+      "Epoch 119, Loss: 27.4570, Best: 26.6623\n",
+      "Epoch 120, Loss: 27.3424, Best: 26.6623\n",
+      "Epoch 121, Loss: 31.5764, Best: 26.6623\n",
+      "Epoch 122, Loss: 30.1690, Best: 26.6623\n",
+      "Epoch 123, Loss: 30.1641, Best: 26.6623\n",
+      "Epoch 124, Loss: 29.0480, Best: 26.6623\n",
+      "Epoch 125, Loss: 28.7665, Best: 26.6623\n",
+      "Epoch 126, Loss: 30.5343, Best: 26.6623\n",
+      "Epoch 127, Loss: 28.7441, Best: 26.6623\n",
+      "Epoch 128, Loss: 29.7405, Best: 26.6623\n",
+      "Epoch 129, Loss: 30.8572, Best: 26.6623\n",
+      "Epoch 130, Loss: 29.2414, Best: 26.6623\n",
+      "Epoch 131, Loss: 27.8902, Best: 26.6623\n",
+      "Epoch 132, Loss: 31.4098, Best: 26.6623\n",
+      "Epoch 133, Loss: 29.3528, Best: 26.6623\n",
+      "Epoch 134, Loss: 29.3522, Best: 26.6623\n",
+      "Epoch 135, Loss: 28.2317, Best: 26.6623\n",
+      "Epoch 136, Loss: 28.6176, Best: 26.6623\n",
+      "Epoch 137, Loss: 29.3111, Best: 26.6623\n",
+      "Epoch 138, Loss: 30.2151, Best: 26.6623\n",
+      "Epoch 139, Loss: 28.6543, Best: 26.6623\n",
+      "Epoch 140, Loss: 29.5226, Best: 26.6623\n",
+      "Epoch 141, Loss: 29.9806, Best: 26.6623\n",
+      "Epoch 142, Loss: 29.5061, Best: 26.6623\n",
+      "Epoch 143, Loss: 30.7945, Best: 26.6623\n",
+      "Epoch 144, Loss: 28.8175, Best: 26.6623\n",
+      "Epoch 145, Loss: 29.2005, Best: 26.6623\n",
+      "Epoch 146, Loss: 32.4261, Best: 26.6623\n",
+      "Epoch 147, Loss: 28.4491, Best: 26.6623\n",
+      "Epoch 148, Loss: 29.0147, Best: 26.6623\n",
+      "Epoch 149, Loss: 29.0668, Best: 26.6623\n",
+      "Epoch 150, Loss: 30.1709, Best: 26.6623\n",
+      "Epoch 151, Loss: 31.1682, Best: 26.6623\n",
+      "Epoch 152, Loss: 32.2273, Best: 26.6623\n",
+      "Epoch 153, Loss: 28.2924, Best: 26.6623\n",
+      "Epoch 154, Loss: 29.4674, Best: 26.6623\n",
+      "Epoch 155, Loss: 28.5601, Best: 26.6623\n",
+      "Epoch 156, Loss: 29.0775, Best: 26.6623\n",
+      "Epoch 157, Loss: 30.7644, Best: 26.6623\n",
+      "Epoch 158, Loss: 29.9207, Best: 26.6623\n",
+      "Epoch 159, Loss: 30.1474, Best: 26.6623\n",
+      "Epoch 160, Loss: 30.5713, Best: 26.6623\n",
+      "Epoch 161, Loss: 28.7393, Best: 26.6623\n",
+      "Epoch 162, Loss: 29.2621, Best: 26.6623\n",
+      "Epoch 163, Loss: 29.8973, Best: 26.6623\n",
+      "Epoch 164, Loss: 26.8581, Best: 26.6623\n",
+      "Epoch 165, Loss: 29.5431, Best: 26.6623\n",
+      "Epoch 166, Loss: 29.7803, Best: 26.6623\n",
+      "Epoch 167, Loss: 29.3116, Best: 26.6623\n",
+      "Epoch 168, Loss: 27.5453, Best: 26.6623\n",
+      "Epoch 169, Loss: 28.9759, Best: 26.6623\n",
+      "Epoch 170, Loss: 30.6053, Best: 26.6623\n",
+      "Epoch 171, Loss: 26.9713, Best: 26.6623\n",
+      "Epoch 172, Loss: 31.1701, Best: 26.6623\n",
+      "Epoch 173, Loss: 29.2016, Best: 26.6623\n",
+      "Epoch 174, Loss: 28.3404, Best: 26.6623\n",
+      "Epoch 175, Loss: 30.6270, Best: 26.6623\n",
+      "Epoch 176, Loss: 28.7779, Best: 26.6623\n",
+      "Epoch 177, Loss: 29.2232, Best: 26.6623\n",
+      "Epoch 178, Loss: 31.0153, Best: 26.6623\n",
+      "Epoch 179, Loss: 27.4407, Best: 26.6623\n",
+      "Epoch 180, Loss: 29.8161, Best: 26.6623\n",
+      "Epoch 181, Loss: 30.8416, Best: 26.6623\n",
+      "Epoch 182, Loss: 30.5665, Best: 26.6623\n",
+      "Epoch 183, Loss: 27.3942, Best: 26.6623\n",
+      "Epoch 184, Loss: 28.2484, Best: 26.6623\n",
+      "Epoch 185, Loss: 29.6721, Best: 26.6623\n",
+      "Epoch 186, Loss: 28.7535, Best: 26.6623\n",
+      "Epoch 187, Loss: 27.2549, Best: 26.6623\n",
+      "Epoch 188, Loss: 29.4187, Best: 26.6623\n",
+      "Epoch 189, Loss: 29.4577, Best: 26.6623\n",
+      "Epoch 190, Loss: 31.1131, Best: 26.6623\n",
+      "Epoch 191, Loss: 31.7229, Best: 26.6623\n",
+      "Epoch 192, Loss: 29.6869, Best: 26.6623\n",
+      "Epoch 193, Loss: 29.2898, Best: 26.6623\n",
+      "Epoch 194, Loss: 29.2527, Best: 26.6623\n",
+      "Epoch 195, Loss: 27.5062, Best: 26.6623\n",
+      "Epoch 196, Loss: 26.9031, Best: 26.6623\n",
+      "Epoch 197, Loss: 28.8727, Best: 26.6623\n",
+      "Epoch 198, Loss: 27.0563, Best: 26.6623\n",
+      "Epoch 199, Loss: 28.5650, Best: 26.6623\n",
+      "Epoch 200, Loss: 26.7787, Best: 26.6623\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.1 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "Epoch 201, Loss: 29.7210, Best: 26.6623\n",
+      "Epoch 202, Loss: 27.3175, Best: 26.6623\n",
+      "Epoch 203, Loss: 26.6148, Best: 26.6148\n",
+      "Epoch 204, Loss: 29.5147, Best: 26.6148\n",
+      "Epoch 205, Loss: 28.6690, Best: 26.6148\n",
+      "Epoch 206, Loss: 28.9819, Best: 26.6148\n",
+      "Epoch 207, Loss: 29.4928, Best: 26.6148\n",
+      "Epoch 208, Loss: 27.0178, Best: 26.6148\n",
+      "Epoch 209, Loss: 26.9453, Best: 26.6148\n",
+      "Epoch 210, Loss: 26.8151, Best: 26.6148\n",
+      "Epoch 211, Loss: 35.5324, Best: 26.6148\n",
+      "Epoch 212, Loss: 28.5299, Best: 26.6148\n",
+      "Epoch 213, Loss: 27.9985, Best: 26.6148\n",
+      "Epoch 214, Loss: 27.9034, Best: 26.6148\n",
+      "Epoch 215, Loss: 28.1414, Best: 26.6148\n",
+      "Epoch 216, Loss: 29.2126, Best: 26.6148\n",
+      "Epoch 217, Loss: 27.7169, Best: 26.6148\n",
+      "Epoch 218, Loss: 29.8495, Best: 26.6148\n",
+      "Epoch 219, Loss: 27.7849, Best: 26.6148\n",
+      "Epoch 220, Loss: 27.0340, Best: 26.6148\n",
+      "Epoch 221, Loss: 31.2737, Best: 26.6148\n",
+      "Epoch 222, Loss: 26.2974, Best: 26.2974\n",
+      "Epoch 223, Loss: 26.3569, Best: 26.2974\n",
+      "Epoch 224, Loss: 28.9875, Best: 26.2974\n",
+      "Epoch 225, Loss: 27.8762, Best: 26.2974\n",
+      "Epoch 226, Loss: 31.1670, Best: 26.2974\n",
+      "Epoch 227, Loss: 27.7513, Best: 26.2974\n",
+      "Epoch 228, Loss: 28.3689, Best: 26.2974\n",
+      "Epoch 229, Loss: 29.8629, Best: 26.2974\n",
+      "Epoch 230, Loss: 27.0494, Best: 26.2974\n",
+      "Epoch 231, Loss: 27.7816, Best: 26.2974\n",
+      "Epoch 232, Loss: 27.3619, Best: 26.2974\n",
+      "Epoch 233, Loss: 28.3660, Best: 26.2974\n",
+      "Epoch 234, Loss: 30.1278, Best: 26.2974\n",
+      "Epoch 235, Loss: 31.3614, Best: 26.2974\n",
+      "Epoch 236, Loss: 28.3427, Best: 26.2974\n",
+      "Epoch 237, Loss: 28.4336, Best: 26.2974\n",
+      "Epoch 238, Loss: 27.3987, Best: 26.2974\n",
+      "Epoch 239, Loss: 28.5629, Best: 26.2974\n",
+      "Epoch 240, Loss: 26.9778, Best: 26.2974\n",
+      "Epoch 241, Loss: 28.2873, Best: 26.2974\n",
+      "Epoch 242, Loss: 27.4711, Best: 26.2974\n",
+      "Epoch 243, Loss: 29.5235, Best: 26.2974\n",
+      "Epoch 244, Loss: 27.9350, Best: 26.2974\n",
+      "Epoch 245, Loss: 28.5220, Best: 26.2974\n",
+      "Epoch 246, Loss: 29.9928, Best: 26.2974\n",
+      "Epoch 247, Loss: 26.8966, Best: 26.2974\n",
+      "Epoch 248, Loss: 26.9412, Best: 26.2974\n",
+      "Epoch 249, Loss: 30.0742, Best: 26.2974\n",
+      "Epoch 250, Loss: 28.5568, Best: 26.2974\n",
+      "Epoch 251, Loss: 29.4369, Best: 26.2974\n",
+      "Epoch 252, Loss: 28.7565, Best: 26.2974\n",
+      "Epoch 253, Loss: 28.1473, Best: 26.2974\n",
+      "Epoch 254, Loss: 28.4172, Best: 26.2974\n",
+      "Epoch 255, Loss: 28.8571, Best: 26.2974\n",
+      "Epoch 256, Loss: 29.0544, Best: 26.2974\n",
+      "Epoch 257, Loss: 29.5724, Best: 26.2974\n",
+      "Epoch 258, Loss: 30.1543, Best: 26.2974\n",
+      "Epoch 259, Loss: 29.9750, Best: 26.2974\n",
+      "Epoch 260, Loss: 28.3353, Best: 26.2974\n",
+      "Epoch 261, Loss: 28.5232, Best: 26.2974\n",
+      "Epoch 262, Loss: 29.9572, Best: 26.2974\n",
+      "Epoch 263, Loss: 29.8520, Best: 26.2974\n",
+      "Epoch 264, Loss: 29.3342, Best: 26.2974\n",
+      "Epoch 265, Loss: 28.2360, Best: 26.2974\n",
+      "Epoch 266, Loss: 27.9042, Best: 26.2974\n",
+      "Epoch 267, Loss: 28.7870, Best: 26.2974\n",
+      "Epoch 268, Loss: 29.0824, Best: 26.2974\n",
+      "Epoch 269, Loss: 29.8514, Best: 26.2974\n",
+      "Epoch 270, Loss: 27.0119, Best: 26.2974\n",
+      "Epoch 271, Loss: 29.5305, Best: 26.2974\n",
+      "Epoch 272, Loss: 29.6169, Best: 26.2974\n",
+      "Epoch 273, Loss: 29.6177, Best: 26.2974\n",
+      "Epoch 274, Loss: 28.2944, Best: 26.2974\n",
+      "Epoch 275, Loss: 29.8649, Best: 26.2974\n",
+      "Epoch 276, Loss: 28.5229, Best: 26.2974\n",
+      "Epoch 277, Loss: 28.8354, Best: 26.2974\n",
+      "Epoch 278, Loss: 27.9979, Best: 26.2974\n",
+      "Epoch 279, Loss: 27.8305, Best: 26.2974\n",
+      "Epoch 280, Loss: 26.8716, Best: 26.2974\n",
+      "Epoch 281, Loss: 28.9723, Best: 26.2974\n",
+      "Epoch 282, Loss: 28.1049, Best: 26.2974\n",
+      "Epoch 283, Loss: 27.9843, Best: 26.2974\n",
+      "Epoch 284, Loss: 27.6997, Best: 26.2974\n",
+      "Epoch 285, Loss: 28.1606, Best: 26.2974\n",
+      "Epoch 286, Loss: 26.7980, Best: 26.2974\n",
+      "Epoch 287, Loss: 29.1750, Best: 26.2974\n",
+      "Epoch 288, Loss: 29.2934, Best: 26.2974\n",
+      "Epoch 289, Loss: 28.0557, Best: 26.2974\n",
+      "Epoch 290, Loss: 28.8948, Best: 26.2974\n",
+      "Epoch 291, Loss: 27.0046, Best: 26.2974\n",
+      "Epoch 292, Loss: 26.7565, Best: 26.2974\n",
+      "Epoch 293, Loss: 29.1353, Best: 26.2974\n",
+      "Epoch 294, Loss: 29.7675, Best: 26.2974\n",
+      "Epoch 295, Loss: 28.5674, Best: 26.2974\n",
+      "Epoch 296, Loss: 30.0449, Best: 26.2974\n",
+      "Epoch 297, Loss: 28.6396, Best: 26.2974\n",
+      "Epoch 298, Loss: 29.3259, Best: 26.2974\n",
+      "Epoch 299, Loss: 31.8421, Best: 26.2974\n",
+      "Epoch 300, Loss: 28.3888, Best: 26.2974\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.1 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "Epoch 301, Loss: 29.1755, Best: 26.2974\n",
+      "Epoch 302, Loss: 30.8517, Best: 26.2974\n",
+      "Epoch 303, Loss: 27.7275, Best: 26.2974\n",
+      "Epoch 304, Loss: 27.2906, Best: 26.2974\n",
+      "Epoch 305, Loss: 26.6815, Best: 26.2974\n",
+      "Epoch 306, Loss: 28.4974, Best: 26.2974\n",
+      "Epoch 307, Loss: 27.7027, Best: 26.2974\n",
+      "Epoch 308, Loss: 28.6464, Best: 26.2974\n",
+      "Epoch 309, Loss: 27.9241, Best: 26.2974\n",
+      "Epoch 310, Loss: 27.1855, Best: 26.2974\n",
+      "Epoch 311, Loss: 28.9896, Best: 26.2974\n",
+      "Epoch 312, Loss: 28.8053, Best: 26.2974\n",
+      "Epoch 313, Loss: 29.2392, Best: 26.2974\n",
+      "Epoch 314, Loss: 29.1783, Best: 26.2974\n",
+      "Epoch 315, Loss: 28.9865, Best: 26.2974\n",
+      "Epoch 316, Loss: 31.4181, Best: 26.2974\n",
+      "Epoch 317, Loss: 28.0380, Best: 26.2974\n",
+      "Epoch 318, Loss: 26.9537, Best: 26.2974\n",
+      "Epoch 319, Loss: 27.8282, Best: 26.2974\n",
+      "Epoch 320, Loss: 28.0028, Best: 26.2974\n",
+      "Epoch 321, Loss: 28.2196, Best: 26.2974\n",
+      "Epoch 322, Loss: 29.6047, Best: 26.2974\n",
+      "Epoch 323, Loss: 27.3408, Best: 26.2974\n",
+      "Epoch 324, Loss: 28.6485, Best: 26.2974\n",
+      "Epoch 325, Loss: 28.7351, Best: 26.2974\n",
+      "Epoch 326, Loss: 29.4523, Best: 26.2974\n",
+      "Epoch 327, Loss: 29.2071, Best: 26.2974\n",
+      "Epoch 328, Loss: 30.0995, Best: 26.2974\n",
+      "Epoch 329, Loss: 29.9623, Best: 26.2974\n",
+      "Epoch 330, Loss: 27.6807, Best: 26.2974\n",
+      "Epoch 331, Loss: 28.0651, Best: 26.2974\n",
+      "Epoch 332, Loss: 26.3264, Best: 26.2974\n",
+      "Epoch 333, Loss: 29.0311, Best: 26.2974\n",
+      "Epoch 334, Loss: 28.5887, Best: 26.2974\n",
+      "Epoch 335, Loss: 29.6303, Best: 26.2974\n",
+      "Epoch 336, Loss: 26.5456, Best: 26.2974\n",
+      "Epoch 337, Loss: 30.5902, Best: 26.2974\n",
+      "Epoch 338, Loss: 28.2330, Best: 26.2974\n",
+      "Epoch 339, Loss: 28.8031, Best: 26.2974\n",
+      "Epoch 340, Loss: 29.6393, Best: 26.2974\n",
+      "Epoch 341, Loss: 28.4888, Best: 26.2974\n",
+      "Epoch 342, Loss: 29.0216, Best: 26.2974\n",
+      "Epoch 343, Loss: 30.0775, Best: 26.2974\n",
+      "Epoch 344, Loss: 27.7574, Best: 26.2974\n",
+      "Epoch 345, Loss: 31.9062, Best: 26.2974\n",
+      "Epoch 346, Loss: 27.2619, Best: 26.2974\n",
+      "Epoch 347, Loss: 27.9609, Best: 26.2974\n",
+      "Epoch 348, Loss: 32.7782, Best: 26.2974\n",
+      "Epoch 349, Loss: 27.2767, Best: 26.2974\n",
+      "Epoch 350, Loss: 30.5955, Best: 26.2974\n",
+      "Epoch 351, Loss: 31.2037, Best: 26.2974\n",
+      "Epoch 352, Loss: 26.9652, Best: 26.2974\n",
+      "Epoch 353, Loss: 30.0296, Best: 26.2974\n",
+      "Epoch 354, Loss: 27.0905, Best: 26.2974\n",
+      "Epoch 355, Loss: 26.6038, Best: 26.2974\n",
+      "Epoch 356, Loss: 31.9522, Best: 26.2974\n",
+      "Epoch 357, Loss: 28.6838, Best: 26.2974\n",
+      "Epoch 358, Loss: 29.4844, Best: 26.2974\n",
+      "Epoch 359, Loss: 29.3129, Best: 26.2974\n",
+      "Epoch 360, Loss: 28.1667, Best: 26.2974\n",
+      "Epoch 361, Loss: 28.8430, Best: 26.2974\n",
+      "Epoch 362, Loss: 26.6799, Best: 26.2974\n",
+      "Epoch 363, Loss: 28.2084, Best: 26.2974\n",
+      "Epoch 364, Loss: 27.0525, Best: 26.2974\n",
+      "Epoch 365, Loss: 28.3422, Best: 26.2974\n",
+      "Epoch 366, Loss: 28.6641, Best: 26.2974\n",
+      "Epoch 367, Loss: 27.9526, Best: 26.2974\n",
+      "Epoch 368, Loss: 27.5966, Best: 26.2974\n",
+      "Epoch 369, Loss: 27.3468, Best: 26.2974\n",
+      "Epoch 370, Loss: 28.0271, Best: 26.2974\n",
+      "Epoch 371, Loss: 27.7969, Best: 26.2974\n",
+      "Epoch 372, Loss: 29.4534, Best: 26.2974\n",
+      "Epoch 373, Loss: 29.0776, Best: 26.2974\n",
+      "Epoch 374, Loss: 30.7250, Best: 26.2974\n",
+      "Epoch 375, Loss: 27.1190, Best: 26.2974\n",
+      "Epoch 376, Loss: 30.6744, Best: 26.2974\n",
+      "Epoch 377, Loss: 28.1574, Best: 26.2974\n",
+      "Epoch 378, Loss: 26.8106, Best: 26.2974\n",
+      "Epoch 379, Loss: 29.1756, Best: 26.2974\n",
+      "Epoch 380, Loss: 28.2380, Best: 26.2974\n",
+      "Epoch 381, Loss: 26.9705, Best: 26.2974\n",
+      "Epoch 382, Loss: 30.2938, Best: 26.2974\n",
+      "Epoch 383, Loss: 28.8758, Best: 26.2974\n",
+      "Epoch 384, Loss: 28.5729, Best: 26.2974\n",
+      "Epoch 385, Loss: 29.3058, Best: 26.2974\n",
+      "Epoch 386, Loss: 29.2550, Best: 26.2974\n",
+      "Epoch 387, Loss: 28.0977, Best: 26.2974\n",
+      "Epoch 388, Loss: 33.0097, Best: 26.2974\n",
+      "Epoch 389, Loss: 29.4558, Best: 26.2974\n",
+      "Epoch 390, Loss: 29.2799, Best: 26.2974\n",
+      "Epoch 391, Loss: 28.4858, Best: 26.2974\n",
+      "Epoch 392, Loss: 28.9924, Best: 26.2974\n",
+      "Epoch 393, Loss: 28.2397, Best: 26.2974\n",
+      "Epoch 394, Loss: 27.3247, Best: 26.2974\n",
+      "Epoch 395, Loss: 29.1494, Best: 26.2974\n",
+      "Epoch 396, Loss: 30.7843, Best: 26.2974\n",
+      "Epoch 397, Loss: 29.8110, Best: 26.2974\n",
+      "Epoch 398, Loss: 30.4618, Best: 26.2974\n",
+      "Epoch 399, Loss: 28.2280, Best: 26.2974\n",
+      "Epoch 400, Loss: 28.1647, Best: 26.2974\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.1 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "Epoch 401, Loss: 28.1335, Best: 26.2974\n",
+      "Epoch 402, Loss: 29.0610, Best: 26.2974\n",
+      "Epoch 403, Loss: 33.7110, Best: 26.2974\n",
+      "Epoch 404, Loss: 27.4068, Best: 26.2974\n",
+      "Epoch 405, Loss: 28.3020, Best: 26.2974\n",
+      "Epoch 406, Loss: 28.1328, Best: 26.2974\n",
+      "Epoch 407, Loss: 30.1292, Best: 26.2974\n",
+      "Epoch 408, Loss: 26.9151, Best: 26.2974\n",
+      "Epoch 409, Loss: 26.3449, Best: 26.2974\n",
+      "Epoch 410, Loss: 28.6095, Best: 26.2974\n",
+      "Epoch 411, Loss: 29.2883, Best: 26.2974\n",
+      "Epoch 412, Loss: 29.2946, Best: 26.2974\n",
+      "Epoch 413, Loss: 31.5181, Best: 26.2974\n",
+      "Epoch 414, Loss: 26.5550, Best: 26.2974\n",
+      "Epoch 415, Loss: 28.7754, Best: 26.2974\n",
+      "Epoch 416, Loss: 29.7268, Best: 26.2974\n",
+      "Epoch 417, Loss: 29.4501, Best: 26.2974\n",
+      "Epoch 418, Loss: 28.4703, Best: 26.2974\n",
+      "Epoch 419, Loss: 28.6272, Best: 26.2974\n",
+      "Epoch 420, Loss: 28.9566, Best: 26.2974\n",
+      "Epoch 421, Loss: 27.6237, Best: 26.2974\n",
+      "Epoch 422, Loss: 30.1225, Best: 26.2974\n",
+      "Epoch 423, Loss: 29.5927, Best: 26.2974\n",
+      "Epoch 424, Loss: 28.0836, Best: 26.2974\n",
+      "Epoch 425, Loss: 28.7985, Best: 26.2974\n",
+      "Epoch 426, Loss: 30.4647, Best: 26.2974\n",
+      "Epoch 427, Loss: 27.2976, Best: 26.2974\n",
+      "Epoch 428, Loss: 28.8990, Best: 26.2974\n",
+      "Epoch 429, Loss: 28.1289, Best: 26.2974\n",
+      "Epoch 430, Loss: 28.7319, Best: 26.2974\n",
+      "Epoch 431, Loss: 27.6648, Best: 26.2974\n",
+      "Epoch 432, Loss: 28.4591, Best: 26.2974\n",
+      "Epoch 433, Loss: 30.4511, Best: 26.2974\n",
+      "Epoch 434, Loss: 29.2330, Best: 26.2974\n",
+      "Epoch 435, Loss: 27.4748, Best: 26.2974\n",
+      "Epoch 436, Loss: 30.5044, Best: 26.2974\n",
+      "Epoch 437, Loss: 26.9294, Best: 26.2974\n",
+      "Epoch 438, Loss: 27.1098, Best: 26.2974\n",
+      "Epoch 439, Loss: 27.9401, Best: 26.2974\n",
+      "Epoch 440, Loss: 29.8764, Best: 26.2974\n",
+      "Epoch 441, Loss: 26.8976, Best: 26.2974\n",
+      "Epoch 442, Loss: 27.2944, Best: 26.2974\n",
+      "Epoch 443, Loss: 30.4767, Best: 26.2974\n",
+      "Epoch 444, Loss: 30.3551, Best: 26.2974\n",
+      "Epoch 445, Loss: 28.5074, Best: 26.2974\n",
+      "Epoch 446, Loss: 29.6269, Best: 26.2974\n",
+      "Epoch 447, Loss: 28.8815, Best: 26.2974\n",
+      "Epoch 448, Loss: 28.0546, Best: 26.2974\n",
+      "Epoch 449, Loss: 27.9440, Best: 26.2974\n",
+      "Epoch 450, Loss: 30.9552, Best: 26.2974\n",
+      "Epoch 451, Loss: 27.9607, Best: 26.2974\n",
+      "Epoch 452, Loss: 26.3986, Best: 26.2974\n",
+      "Epoch 453, Loss: 30.0413, Best: 26.2974\n",
+      "Epoch 454, Loss: 32.2158, Best: 26.2974\n",
+      "Epoch 455, Loss: 29.0320, Best: 26.2974\n",
+      "Epoch 456, Loss: 29.2409, Best: 26.2974\n",
+      "Epoch 457, Loss: 29.2684, Best: 26.2974\n",
+      "Epoch 458, Loss: 30.2327, Best: 26.2974\n",
+      "Epoch 459, Loss: 29.8057, Best: 26.2974\n",
+      "Epoch 460, Loss: 31.0874, Best: 26.2974\n",
+      "Epoch 461, Loss: 29.3415, Best: 26.2974\n",
+      "Epoch 462, Loss: 30.9539, Best: 26.2974\n",
+      "Epoch 463, Loss: 27.9871, Best: 26.2974\n",
+      "Epoch 464, Loss: 30.3431, Best: 26.2974\n",
+      "Epoch 465, Loss: 29.4605, Best: 26.2974\n",
+      "Epoch 466, Loss: 27.4578, Best: 26.2974\n",
+      "Epoch 467, Loss: 28.2167, Best: 26.2974\n",
+      "Epoch 468, Loss: 26.8172, Best: 26.2974\n",
+      "Epoch 469, Loss: 26.5373, Best: 26.2974\n",
+      "Epoch 470, Loss: 27.6894, Best: 26.2974\n",
+      "Epoch 471, Loss: 28.5604, Best: 26.2974\n",
+      "Epoch 472, Loss: 27.3005, Best: 26.2974\n",
+      "Epoch 473, Loss: 31.0004, Best: 26.2974\n",
+      "Epoch 474, Loss: 29.3304, Best: 26.2974\n",
+      "Epoch 475, Loss: 29.7066, Best: 26.2974\n",
+      "Epoch 476, Loss: 28.1716, Best: 26.2974\n",
+      "Epoch 477, Loss: 26.7920, Best: 26.2974\n",
+      "Epoch 478, Loss: 29.0000, Best: 26.2974\n",
+      "Epoch 479, Loss: 27.4513, Best: 26.2974\n",
+      "Epoch 480, Loss: 27.8060, Best: 26.2974\n",
+      "Epoch 481, Loss: 30.0667, Best: 26.2974\n",
+      "Epoch 482, Loss: 30.6492, Best: 26.2974\n",
+      "Epoch 483, Loss: 30.2238, Best: 26.2974\n",
+      "Epoch 484, Loss: 28.9799, Best: 26.2974\n",
+      "Epoch 485, Loss: 30.3214, Best: 26.2974\n",
+      "Epoch 486, Loss: 29.8073, Best: 26.2974\n",
+      "Epoch 487, Loss: 29.8247, Best: 26.2974\n",
+      "Epoch 488, Loss: 28.6795, Best: 26.2974\n",
+      "Epoch 489, Loss: 26.8349, Best: 26.2974\n",
+      "Epoch 490, Loss: 30.0222, Best: 26.2974\n",
+      "Epoch 491, Loss: 29.4161, Best: 26.2974\n",
+      "Epoch 492, Loss: 29.1449, Best: 26.2974\n",
+      "Epoch 493, Loss: 31.2091, Best: 26.2974\n",
+      "Epoch 494, Loss: 28.9447, Best: 26.2974\n",
+      "Epoch 495, Loss: 29.4342, Best: 26.2974\n",
+      "Epoch 496, Loss: 29.0431, Best: 26.2974\n",
+      "Epoch 497, Loss: 33.3198, Best: 26.2974\n",
+      "Epoch 498, Loss: 27.2591, Best: 26.2974\n",
+      "Epoch 499, Loss: 28.3465, Best: 26.2974\n",
+      "Epoch 500, Loss: 30.0654, Best: 26.2974\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.1 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "Epoch 501, Loss: 31.0597, Best: 26.2974\n",
+      "Epoch 502, Loss: 29.5932, Best: 26.2974\n",
+      "Epoch 503, Loss: 29.2255, Best: 26.2974\n",
+      "Epoch 504, Loss: 26.7908, Best: 26.2974\n",
+      "Epoch 505, Loss: 26.6399, Best: 26.2974\n",
+      "Epoch 506, Loss: 28.5791, Best: 26.2974\n",
+      "Epoch 507, Loss: 28.5000, Best: 26.2974\n",
+      "Epoch 508, Loss: 28.3087, Best: 26.2974\n",
+      "Epoch 509, Loss: 27.2041, Best: 26.2974\n",
+      "Epoch 510, Loss: 29.1446, Best: 26.2974\n",
+      "Epoch 511, Loss: 30.9034, Best: 26.2974\n",
+      "Epoch 512, Loss: 26.9096, Best: 26.2974\n",
+      "Epoch 513, Loss: 30.6518, Best: 26.2974\n",
+      "Epoch 514, Loss: 29.6513, Best: 26.2974\n",
+      "Epoch 515, Loss: 27.1668, Best: 26.2974\n",
+      "Epoch 516, Loss: 28.2894, Best: 26.2974\n",
+      "Epoch 517, Loss: 31.2760, Best: 26.2974\n",
+      "Epoch 518, Loss: 27.1558, Best: 26.2974\n",
+      "Epoch 519, Loss: 29.4691, Best: 26.2974\n",
+      "Epoch 520, Loss: 31.3967, Best: 26.2974\n",
+      "Epoch 521, Loss: 30.8060, Best: 26.2974\n",
+      "Epoch 522, Loss: 29.0319, Best: 26.2974\n",
+      "Epoch 523, Loss: 26.9820, Best: 26.2974\n",
+      "Epoch 524, Loss: 28.0867, Best: 26.2974\n",
+      "Epoch 525, Loss: 28.5052, Best: 26.2974\n",
+      "Epoch 526, Loss: 30.7656, Best: 26.2974\n",
+      "Epoch 527, Loss: 27.8213, Best: 26.2974\n",
+      "Epoch 528, Loss: 29.0378, Best: 26.2974\n",
+      "Epoch 529, Loss: 27.2114, Best: 26.2974\n",
+      "Epoch 530, Loss: 27.9103, Best: 26.2974\n",
+      "Epoch 531, Loss: 29.0601, Best: 26.2974\n",
+      "Epoch 532, Loss: 28.7611, Best: 26.2974\n",
+      "Epoch 533, Loss: 27.4994, Best: 26.2974\n",
+      "Epoch 534, Loss: 30.5620, Best: 26.2974\n",
+      "Epoch 535, Loss: 28.7551, Best: 26.2974\n",
+      "Epoch 536, Loss: 28.1054, Best: 26.2974\n",
+      "Epoch 537, Loss: 28.0965, Best: 26.2974\n",
+      "Epoch 538, Loss: 29.6116, Best: 26.2974\n",
+      "Epoch 539, Loss: 31.0111, Best: 26.2974\n",
+      "Epoch 540, Loss: 30.4819, Best: 26.2974\n",
+      "Epoch 541, Loss: 28.6775, Best: 26.2974\n",
+      "Epoch 542, Loss: 28.9889, Best: 26.2974\n",
+      "Epoch 543, Loss: 26.6147, Best: 26.2974\n",
+      "Epoch 544, Loss: 27.0294, Best: 26.2974\n",
+      "Epoch 545, Loss: 29.5821, Best: 26.2974\n",
+      "Epoch 546, Loss: 29.2236, Best: 26.2974\n",
+      "Epoch 547, Loss: 26.7650, Best: 26.2974\n",
+      "Epoch 548, Loss: 29.2818, Best: 26.2974\n",
+      "Epoch 549, Loss: 27.5251, Best: 26.2974\n",
+      "Epoch 550, Loss: 28.0341, Best: 26.2974\n",
+      "Epoch 551, Loss: 28.1823, Best: 26.2974\n",
+      "Epoch 552, Loss: 27.8440, Best: 26.2974\n",
+      "Epoch 553, Loss: 26.2601, Best: 26.2601\n",
+      "Epoch 554, Loss: 30.3567, Best: 26.2601\n",
+      "Epoch 555, Loss: 32.9466, Best: 26.2601\n",
+      "Epoch 556, Loss: 28.1245, Best: 26.2601\n",
+      "Epoch 557, Loss: 28.4624, Best: 26.2601\n",
+      "Epoch 558, Loss: 53.4079, Best: 26.2601\n",
+      "Epoch 559, Loss: 31.7774, Best: 26.2601\n",
+      "Epoch 560, Loss: 32.3934, Best: 26.2601\n",
+      "Epoch 561, Loss: 31.4556, Best: 26.2601\n",
+      "Epoch 562, Loss: 28.3715, Best: 26.2601\n",
+      "Epoch 563, Loss: 29.4982, Best: 26.2601\n",
+      "Epoch 564, Loss: 29.4086, Best: 26.2601\n",
+      "Epoch 565, Loss: 29.8694, Best: 26.2601\n",
+      "Epoch 566, Loss: 27.8018, Best: 26.2601\n",
+      "Epoch 567, Loss: 29.3060, Best: 26.2601\n",
+      "Epoch 568, Loss: 29.5982, Best: 26.2601\n",
+      "Epoch 569, Loss: 29.3889, Best: 26.2601\n",
+      "Epoch 570, Loss: 33.6614, Best: 26.2601\n",
+      "Epoch 571, Loss: 31.3095, Best: 26.2601\n",
+      "Epoch 572, Loss: 29.0019, Best: 26.2601\n",
+      "Epoch 573, Loss: 27.2181, Best: 26.2601\n",
+      "Epoch 574, Loss: 30.5924, Best: 26.2601\n",
+      "Epoch 575, Loss: 30.2880, Best: 26.2601\n",
+      "Epoch 576, Loss: 28.2150, Best: 26.2601\n",
+      "Epoch 577, Loss: 30.5421, Best: 26.2601\n",
+      "Epoch 578, Loss: 29.5994, Best: 26.2601\n",
+      "Epoch 579, Loss: 27.1639, Best: 26.2601\n",
+      "Epoch 580, Loss: 26.4931, Best: 26.2601\n",
+      "Epoch 581, Loss: 27.9361, Best: 26.2601\n",
+      "Epoch 582, Loss: 27.5788, Best: 26.2601\n",
+      "Epoch 583, Loss: 27.1808, Best: 26.2601\n",
+      "Epoch 584, Loss: 30.8347, Best: 26.2601\n",
+      "Epoch 585, Loss: 29.8768, Best: 26.2601\n",
+      "Epoch 586, Loss: 28.6582, Best: 26.2601\n",
+      "Epoch 587, Loss: 28.8579, Best: 26.2601\n",
+      "Epoch 588, Loss: 28.5406, Best: 26.2601\n",
+      "Epoch 589, Loss: 30.7728, Best: 26.2601\n",
+      "Epoch 590, Loss: 27.4821, Best: 26.2601\n",
+      "Epoch 591, Loss: 34.3459, Best: 26.2601\n",
+      "Epoch 592, Loss: 29.1705, Best: 26.2601\n",
+      "Epoch 593, Loss: 32.1301, Best: 26.2601\n",
+      "Epoch 594, Loss: 28.5303, Best: 26.2601\n",
+      "Epoch 595, Loss: 28.6648, Best: 26.2601\n",
+      "Epoch 596, Loss: 29.0840, Best: 26.2601\n",
+      "Epoch 597, Loss: 29.3603, Best: 26.2601\n",
+      "Epoch 598, Loss: 29.3213, Best: 26.2601\n",
+      "Epoch 599, Loss: 30.2449, Best: 26.2601\n",
+      "Epoch 600, Loss: 26.6028, Best: 26.2601\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "Epoch 601, Loss: 29.1437, Best: 26.2601\n",
+      "Epoch 602, Loss: 29.7512, Best: 26.2601\n",
+      "Epoch 603, Loss: 26.9036, Best: 26.2601\n",
+      "Epoch 604, Loss: 29.5476, Best: 26.2601\n",
+      "Epoch 605, Loss: 28.2885, Best: 26.2601\n",
+      "Epoch 606, Loss: 28.9900, Best: 26.2601\n",
+      "Epoch 607, Loss: 32.1337, Best: 26.2601\n",
+      "Epoch 608, Loss: 27.5103, Best: 26.2601\n",
+      "Epoch 609, Loss: 35.3851, Best: 26.2601\n",
+      "Epoch 610, Loss: 32.7638, Best: 26.2601\n",
+      "Epoch 611, Loss: 27.5540, Best: 26.2601\n",
+      "Epoch 612, Loss: 29.0575, Best: 26.2601\n",
+      "Epoch 613, Loss: 29.7268, Best: 26.2601\n",
+      "Epoch 614, Loss: 29.1746, Best: 26.2601\n",
+      "Epoch 615, Loss: 28.7687, Best: 26.2601\n",
+      "Epoch 616, Loss: 26.4365, Best: 26.2601\n",
+      "Epoch 617, Loss: 29.0859, Best: 26.2601\n",
+      "Epoch 618, Loss: 29.2557, Best: 26.2601\n",
+      "Epoch 619, Loss: 28.5195, Best: 26.2601\n",
+      "Epoch 620, Loss: 27.7128, Best: 26.2601\n",
+      "Epoch 621, Loss: 27.5421, Best: 26.2601\n",
+      "Epoch 622, Loss: 27.6878, Best: 26.2601\n",
+      "Epoch 623, Loss: 30.7090, Best: 26.2601\n",
+      "Epoch 624, Loss: 27.5660, Best: 26.2601\n",
+      "Epoch 625, Loss: 29.8840, Best: 26.2601\n",
+      "Epoch 626, Loss: 29.8221, Best: 26.2601\n",
+      "Epoch 627, Loss: 29.5211, Best: 26.2601\n",
+      "Epoch 628, Loss: 30.7905, Best: 26.2601\n",
+      "Epoch 629, Loss: 29.4924, Best: 26.2601\n",
+      "Epoch 630, Loss: 27.8323, Best: 26.2601\n",
+      "Epoch 631, Loss: 28.1711, Best: 26.2601\n",
+      "Epoch 632, Loss: 31.9912, Best: 26.2601\n",
+      "Epoch 633, Loss: 33.9636, Best: 26.2601\n",
+      "Epoch 634, Loss: 28.9319, Best: 26.2601\n",
+      "Epoch 635, Loss: 31.6984, Best: 26.2601\n",
+      "Epoch 636, Loss: 28.9147, Best: 26.2601\n",
+      "Epoch 637, Loss: 30.1057, Best: 26.2601\n",
+      "Epoch 638, Loss: 27.2951, Best: 26.2601\n",
+      "Epoch 639, Loss: 28.6090, Best: 26.2601\n",
+      "Epoch 640, Loss: 29.2364, Best: 26.2601\n",
+      "Epoch 641, Loss: 30.2214, Best: 26.2601\n",
+      "Epoch 642, Loss: 31.1107, Best: 26.2601\n",
+      "Epoch 643, Loss: 28.0152, Best: 26.2601\n",
+      "Epoch 644, Loss: 29.5943, Best: 26.2601\n",
+      "Epoch 645, Loss: 28.9359, Best: 26.2601\n",
+      "Epoch 646, Loss: 31.5628, Best: 26.2601\n",
+      "Epoch 647, Loss: 29.9630, Best: 26.2601\n",
+      "Epoch 648, Loss: 26.8452, Best: 26.2601\n",
+      "Epoch 649, Loss: 27.7530, Best: 26.2601\n",
+      "Epoch 650, Loss: 28.5350, Best: 26.2601\n",
+      "Epoch 651, Loss: 30.9010, Best: 26.2601\n",
+      "Epoch 652, Loss: 27.5374, Best: 26.2601\n",
+      "Epoch 653, Loss: 29.0494, Best: 26.2601\n",
+      "Epoch 654, Loss: 29.9048, Best: 26.2601\n",
+      "Epoch 655, Loss: 28.7700, Best: 26.2601\n",
+      "Epoch 656, Loss: 27.4409, Best: 26.2601\n",
+      "Epoch 657, Loss: 28.2190, Best: 26.2601\n",
+      "Epoch 658, Loss: 27.8390, Best: 26.2601\n",
+      "Epoch 659, Loss: 35.8901, Best: 26.2601\n",
+      "Epoch 660, Loss: 28.3003, Best: 26.2601\n",
+      "Epoch 661, Loss: 29.0626, Best: 26.2601\n",
+      "Epoch 662, Loss: 29.8299, Best: 26.2601\n",
+      "Epoch 663, Loss: 27.5147, Best: 26.2601\n",
+      "Epoch 664, Loss: 30.4401, Best: 26.2601\n",
+      "Epoch 665, Loss: 28.1162, Best: 26.2601\n",
+      "Epoch 666, Loss: 28.1753, Best: 26.2601\n",
+      "Epoch 667, Loss: 30.3621, Best: 26.2601\n",
+      "Epoch 668, Loss: 28.0627, Best: 26.2601\n",
+      "Epoch 669, Loss: 30.3101, Best: 26.2601\n",
+      "Epoch 670, Loss: 27.2355, Best: 26.2601\n",
+      "Epoch 671, Loss: 28.3839, Best: 26.2601\n",
+      "Epoch 672, Loss: 28.5348, Best: 26.2601\n",
+      "Epoch 673, Loss: 27.2872, Best: 26.2601\n",
+      "Epoch 674, Loss: 28.6636, Best: 26.2601\n",
+      "Epoch 675, Loss: 30.1151, Best: 26.2601\n",
+      "Epoch 676, Loss: 28.4336, Best: 26.2601\n",
+      "Epoch 677, Loss: 28.3773, Best: 26.2601\n",
+      "Epoch 678, Loss: 34.2147, Best: 26.2601\n",
+      "Epoch 679, Loss: 30.3779, Best: 26.2601\n",
+      "Epoch 680, Loss: 30.4092, Best: 26.2601\n",
+      "Epoch 681, Loss: 28.7696, Best: 26.2601\n",
+      "Epoch 682, Loss: 27.6019, Best: 26.2601\n",
+      "Epoch 683, Loss: 28.2493, Best: 26.2601\n",
+      "Epoch 684, Loss: 35.7088, Best: 26.2601\n",
+      "Epoch 685, Loss: 30.9320, Best: 26.2601\n",
+      "Epoch 686, Loss: 29.4762, Best: 26.2601\n",
+      "Epoch 687, Loss: 28.1041, Best: 26.2601\n",
+      "Epoch 688, Loss: 35.1393, Best: 26.2601\n",
+      "Epoch 689, Loss: 28.5477, Best: 26.2601\n",
+      "Epoch 690, Loss: 27.8524, Best: 26.2601\n",
+      "Epoch 691, Loss: 31.6607, Best: 26.2601\n",
+      "Epoch 692, Loss: 32.8216, Best: 26.2601\n",
+      "Epoch 693, Loss: 29.1401, Best: 26.2601\n",
+      "Epoch 694, Loss: 30.9020, Best: 26.2601\n",
+      "Epoch 695, Loss: 28.9069, Best: 26.2601\n",
+      "Epoch 696, Loss: 29.2682, Best: 26.2601\n",
+      "Epoch 697, Loss: 30.2187, Best: 26.2601\n",
+      "Epoch 698, Loss: 28.7234, Best: 26.2601\n",
+      "Epoch 699, Loss: 29.4677, Best: 26.2601\n",
+      "Epoch 700, Loss: 28.3422, Best: 26.2601\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.1 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "Epoch 701, Loss: 28.6925, Best: 26.2601\n",
+      "Epoch 702, Loss: 28.1961, Best: 26.2601\n",
+      "Epoch 703, Loss: 27.6060, Best: 26.2601\n",
+      "Epoch 704, Loss: 27.0159, Best: 26.2601\n",
+      "Epoch 705, Loss: 29.7448, Best: 26.2601\n",
+      "Epoch 706, Loss: 27.8172, Best: 26.2601\n",
+      "Epoch 707, Loss: 29.9588, Best: 26.2601\n",
+      "Epoch 708, Loss: 29.3662, Best: 26.2601\n",
+      "Epoch 709, Loss: 28.2702, Best: 26.2601\n",
+      "Epoch 710, Loss: 29.9643, Best: 26.2601\n",
+      "Epoch 711, Loss: 30.4537, Best: 26.2601\n",
+      "Epoch 712, Loss: 29.6767, Best: 26.2601\n",
+      "Epoch 713, Loss: 35.5156, Best: 26.2601\n",
+      "Epoch 714, Loss: 27.7627, Best: 26.2601\n",
+      "Epoch 715, Loss: 30.9406, Best: 26.2601\n",
+      "Epoch 716, Loss: 31.5272, Best: 26.2601\n",
+      "Epoch 717, Loss: 29.7888, Best: 26.2601\n",
+      "Epoch 718, Loss: 27.8865, Best: 26.2601\n",
+      "Epoch 719, Loss: 32.4976, Best: 26.2601\n",
+      "Epoch 720, Loss: 31.1101, Best: 26.2601\n",
+      "Epoch 721, Loss: 30.7715, Best: 26.2601\n",
+      "Epoch 722, Loss: 28.1277, Best: 26.2601\n",
+      "Epoch 723, Loss: 30.2034, Best: 26.2601\n",
+      "Epoch 724, Loss: 28.7653, Best: 26.2601\n",
+      "Epoch 725, Loss: 27.7278, Best: 26.2601\n",
+      "Epoch 726, Loss: 27.3685, Best: 26.2601\n",
+      "Epoch 727, Loss: 28.6729, Best: 26.2601\n",
+      "Epoch 728, Loss: 30.4739, Best: 26.2601\n",
+      "Epoch 729, Loss: 34.9815, Best: 26.2601\n",
+      "Epoch 730, Loss: 26.9802, Best: 26.2601\n",
+      "Epoch 731, Loss: 31.3109, Best: 26.2601\n",
+      "Epoch 732, Loss: 28.7897, Best: 26.2601\n",
+      "Epoch 733, Loss: 30.6587, Best: 26.2601\n",
+      "Epoch 734, Loss: 27.7995, Best: 26.2601\n",
+      "Epoch 735, Loss: 30.6129, Best: 26.2601\n",
+      "Epoch 736, Loss: 31.0089, Best: 26.2601\n",
+      "Epoch 737, Loss: 30.3284, Best: 26.2601\n",
+      "Epoch 738, Loss: 28.6461, Best: 26.2601\n",
+      "Epoch 739, Loss: 30.0345, Best: 26.2601\n",
+      "Epoch 740, Loss: 28.5255, Best: 26.2601\n",
+      "Epoch 741, Loss: 28.2149, Best: 26.2601\n",
+      "Epoch 742, Loss: 26.9556, Best: 26.2601\n",
+      "Epoch 743, Loss: 29.0146, Best: 26.2601\n",
+      "Epoch 744, Loss: 27.6890, Best: 26.2601\n",
+      "Epoch 745, Loss: 28.1471, Best: 26.2601\n",
+      "Epoch 746, Loss: 28.2203, Best: 26.2601\n",
+      "Epoch 747, Loss: 27.1803, Best: 26.2601\n",
+      "Epoch 748, Loss: 29.3474, Best: 26.2601\n",
+      "Epoch 749, Loss: 27.3658, Best: 26.2601\n",
+      "Epoch 750, Loss: 29.6004, Best: 26.2601\n",
+      "Epoch 751, Loss: 30.5252, Best: 26.2601\n",
+      "Epoch 752, Loss: 29.3655, Best: 26.2601\n",
+      "Epoch 753, Loss: 31.7555, Best: 26.2601\n",
+      "Epoch 754, Loss: 29.8267, Best: 26.2601\n",
+      "Epoch 755, Loss: 29.9167, Best: 26.2601\n",
+      "Epoch 756, Loss: 26.1240, Best: 26.1240\n",
+      "Epoch 757, Loss: 28.7552, Best: 26.1240\n",
+      "Epoch 758, Loss: 30.4056, Best: 26.1240\n",
+      "Epoch 759, Loss: 28.3267, Best: 26.1240\n",
+      "Epoch 760, Loss: 27.2966, Best: 26.1240\n",
+      "Epoch 761, Loss: 26.7957, Best: 26.1240\n",
+      "Epoch 762, Loss: 29.9840, Best: 26.1240\n",
+      "Epoch 763, Loss: 29.0125, Best: 26.1240\n",
+      "Epoch 764, Loss: 28.9860, Best: 26.1240\n",
+      "Epoch 765, Loss: 31.0707, Best: 26.1240\n",
+      "Epoch 766, Loss: 29.4885, Best: 26.1240\n",
+      "Epoch 767, Loss: 28.5796, Best: 26.1240\n",
+      "Epoch 768, Loss: 28.4940, Best: 26.1240\n",
+      "Epoch 769, Loss: 32.4712, Best: 26.1240\n",
+      "Epoch 770, Loss: 27.8753, Best: 26.1240\n",
+      "Epoch 771, Loss: 29.6865, Best: 26.1240\n",
+      "Epoch 772, Loss: 29.9187, Best: 26.1240\n",
+      "Epoch 773, Loss: 26.7002, Best: 26.1240\n",
+      "Epoch 774, Loss: 27.3873, Best: 26.1240\n",
+      "Epoch 775, Loss: 27.8097, Best: 26.1240\n",
+      "Epoch 776, Loss: 27.7096, Best: 26.1240\n",
+      "Epoch 777, Loss: 27.4795, Best: 26.1240\n",
+      "Epoch 778, Loss: 28.7663, Best: 26.1240\n",
+      "Epoch 779, Loss: 33.9915, Best: 26.1240\n",
+      "Epoch 780, Loss: 28.5677, Best: 26.1240\n",
+      "Epoch 781, Loss: 29.3418, Best: 26.1240\n",
+      "Epoch 782, Loss: 27.4502, Best: 26.1240\n",
+      "Epoch 783, Loss: 29.8309, Best: 26.1240\n",
+      "Epoch 784, Loss: 29.7380, Best: 26.1240\n",
+      "Epoch 785, Loss: 30.0554, Best: 26.1240\n",
+      "Epoch 786, Loss: 28.6061, Best: 26.1240\n",
+      "Epoch 787, Loss: 34.6601, Best: 26.1240\n",
+      "Epoch 788, Loss: 28.7153, Best: 26.1240\n",
+      "Epoch 789, Loss: 27.1678, Best: 26.1240\n",
+      "Epoch 790, Loss: 29.8063, Best: 26.1240\n",
+      "Epoch 791, Loss: 27.8223, Best: 26.1240\n",
+      "Epoch 792, Loss: 28.5005, Best: 26.1240\n",
+      "Epoch 793, Loss: 28.5939, Best: 26.1240\n",
+      "Epoch 794, Loss: 27.6476, Best: 26.1240\n",
+      "Epoch 795, Loss: 29.6316, Best: 26.1240\n",
+      "Epoch 796, Loss: 29.7815, Best: 26.1240\n",
+      "Epoch 797, Loss: 27.7064, Best: 26.1240\n",
+      "Epoch 798, Loss: 28.2744, Best: 26.1240\n",
+      "Epoch 799, Loss: 30.6418, Best: 26.1240\n",
+      "Epoch 800, Loss: 33.6876, Best: 26.1240\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.1 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "Epoch 801, Loss: 27.2402, Best: 26.1240\n",
+      "Epoch 802, Loss: 26.4848, Best: 26.1240\n",
+      "Epoch 803, Loss: 30.4972, Best: 26.1240\n",
+      "Epoch 804, Loss: 29.4358, Best: 26.1240\n",
+      "Epoch 805, Loss: 27.3534, Best: 26.1240\n",
+      "Epoch 806, Loss: 27.5028, Best: 26.1240\n",
+      "Epoch 807, Loss: 27.5761, Best: 26.1240\n",
+      "Epoch 808, Loss: 28.0863, Best: 26.1240\n",
+      "Epoch 809, Loss: 28.2568, Best: 26.1240\n",
+      "Epoch 810, Loss: 30.1675, Best: 26.1240\n",
+      "Epoch 811, Loss: 27.3639, Best: 26.1240\n",
+      "Epoch 812, Loss: 29.1954, Best: 26.1240\n",
+      "Epoch 813, Loss: 29.6918, Best: 26.1240\n",
+      "Epoch 814, Loss: 29.1382, Best: 26.1240\n",
+      "Epoch 815, Loss: 28.5752, Best: 26.1240\n",
+      "Epoch 816, Loss: 26.3328, Best: 26.1240\n",
+      "Epoch 817, Loss: 28.8813, Best: 26.1240\n",
+      "Epoch 818, Loss: 27.3837, Best: 26.1240\n",
+      "Epoch 819, Loss: 29.3029, Best: 26.1240\n",
+      "Epoch 820, Loss: 35.1128, Best: 26.1240\n",
+      "Epoch 821, Loss: 30.5528, Best: 26.1240\n",
+      "Epoch 822, Loss: 28.6279, Best: 26.1240\n",
+      "Epoch 823, Loss: 30.6945, Best: 26.1240\n",
+      "Epoch 824, Loss: 31.2195, Best: 26.1240\n",
+      "Epoch 825, Loss: 29.3237, Best: 26.1240\n",
+      "Epoch 826, Loss: 29.3894, Best: 26.1240\n",
+      "Epoch 827, Loss: 29.2691, Best: 26.1240\n",
+      "Epoch 828, Loss: 27.8639, Best: 26.1240\n",
+      "Epoch 829, Loss: 28.2704, Best: 26.1240\n",
+      "Epoch 830, Loss: 30.9692, Best: 26.1240\n",
+      "Epoch 831, Loss: 29.3594, Best: 26.1240\n",
+      "Epoch 832, Loss: 29.6296, Best: 26.1240\n",
+      "Epoch 833, Loss: 27.9269, Best: 26.1240\n",
+      "Epoch 834, Loss: 27.9462, Best: 26.1240\n",
+      "Epoch 835, Loss: 28.3507, Best: 26.1240\n",
+      "Epoch 836, Loss: 27.1385, Best: 26.1240\n",
+      "Epoch 837, Loss: 28.6232, Best: 26.1240\n",
+      "Epoch 838, Loss: 28.2627, Best: 26.1240\n",
+      "Epoch 839, Loss: 31.5193, Best: 26.1240\n",
+      "Epoch 840, Loss: 29.1019, Best: 26.1240\n",
+      "Epoch 841, Loss: 29.7157, Best: 26.1240\n",
+      "Epoch 842, Loss: 27.7274, Best: 26.1240\n",
+      "Epoch 843, Loss: 27.2471, Best: 26.1240\n",
+      "Epoch 844, Loss: 28.6889, Best: 26.1240\n",
+      "Epoch 845, Loss: 28.4884, Best: 26.1240\n",
+      "Epoch 846, Loss: 26.4908, Best: 26.1240\n",
+      "Epoch 847, Loss: 30.7017, Best: 26.1240\n",
+      "Epoch 848, Loss: 28.6891, Best: 26.1240\n",
+      "Epoch 849, Loss: 28.5013, Best: 26.1240\n",
+      "Epoch 850, Loss: 29.4348, Best: 26.1240\n",
+      "Epoch 851, Loss: 27.4091, Best: 26.1240\n",
+      "Epoch 852, Loss: 28.3345, Best: 26.1240\n",
+      "Epoch 853, Loss: 28.4183, Best: 26.1240\n",
+      "Epoch 854, Loss: 27.4765, Best: 26.1240\n",
+      "Epoch 855, Loss: 31.4219, Best: 26.1240\n",
+      "Epoch 856, Loss: 29.4268, Best: 26.1240\n",
+      "Epoch 857, Loss: 37.4745, Best: 26.1240\n",
+      "Epoch 858, Loss: 27.3977, Best: 26.1240\n",
+      "Epoch 859, Loss: 29.0583, Best: 26.1240\n",
+      "Epoch 860, Loss: 29.3409, Best: 26.1240\n",
+      "Epoch 861, Loss: 27.6320, Best: 26.1240\n",
+      "Epoch 862, Loss: 29.3364, Best: 26.1240\n",
+      "Epoch 863, Loss: 30.2554, Best: 26.1240\n",
+      "Epoch 864, Loss: 29.4256, Best: 26.1240\n",
+      "Epoch 865, Loss: 30.3940, Best: 26.1240\n",
+      "Epoch 866, Loss: 30.6441, Best: 26.1240\n",
+      "Epoch 867, Loss: 26.9099, Best: 26.1240\n",
+      "Epoch 868, Loss: 28.7077, Best: 26.1240\n",
+      "Epoch 869, Loss: 34.3933, Best: 26.1240\n",
+      "Epoch 870, Loss: 29.4792, Best: 26.1240\n",
+      "Epoch 871, Loss: 29.7918, Best: 26.1240\n",
+      "Epoch 872, Loss: 27.7176, Best: 26.1240\n",
+      "Epoch 873, Loss: 29.0128, Best: 26.1240\n",
+      "Epoch 874, Loss: 28.6476, Best: 26.1240\n",
+      "Epoch 875, Loss: 29.5858, Best: 26.1240\n",
+      "Epoch 876, Loss: 28.0657, Best: 26.1240\n",
+      "Epoch 877, Loss: 27.6625, Best: 26.1240\n",
+      "Epoch 878, Loss: 27.2052, Best: 26.1240\n",
+      "Epoch 879, Loss: 27.4576, Best: 26.1240\n",
+      "Epoch 880, Loss: 30.7872, Best: 26.1240\n",
+      "Epoch 881, Loss: 31.7460, Best: 26.1240\n",
+      "Epoch 882, Loss: 27.2289, Best: 26.1240\n",
+      "Epoch 883, Loss: 28.1053, Best: 26.1240\n",
+      "Epoch 884, Loss: 27.4026, Best: 26.1240\n",
+      "Epoch 885, Loss: 28.3055, Best: 26.1240\n",
+      "Epoch 886, Loss: 29.2319, Best: 26.1240\n",
+      "Epoch 887, Loss: 28.0726, Best: 26.1240\n",
+      "Epoch 888, Loss: 28.2029, Best: 26.1240\n",
+      "Epoch 889, Loss: 29.1692, Best: 26.1240\n",
+      "Epoch 890, Loss: 27.2165, Best: 26.1240\n",
+      "Epoch 891, Loss: 31.7888, Best: 26.1240\n",
+      "Epoch 892, Loss: 28.8535, Best: 26.1240\n",
+      "Epoch 893, Loss: 31.4847, Best: 26.1240\n",
+      "Epoch 894, Loss: 26.4660, Best: 26.1240\n",
+      "Epoch 895, Loss: 31.6076, Best: 26.1240\n",
+      "Epoch 896, Loss: 27.3773, Best: 26.1240\n",
+      "Epoch 897, Loss: 27.9873, Best: 26.1240\n",
+      "Epoch 898, Loss: 31.1337, Best: 26.1240\n",
+      "Epoch 899, Loss: 30.8071, Best: 26.1240\n",
+      "Epoch 900, Loss: 29.7481, Best: 26.1240\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "GPU memory allocated: 265.0 MB\n",
+      "Epoch 901, Loss: 28.3999, Best: 26.1240\n",
+      "Epoch 902, Loss: 29.5210, Best: 26.1240\n",
+      "Epoch 903, Loss: 27.4918, Best: 26.1240\n",
+      "Epoch 904, Loss: 27.5503, Best: 26.1240\n",
+      "Epoch 905, Loss: 29.1054, Best: 26.1240\n",
+      "Epoch 906, Loss: 29.8597, Best: 26.1240\n",
+      "Epoch 907, Loss: 28.5285, Best: 26.1240\n",
+      "Epoch 908, Loss: 26.7940, Best: 26.1240\n",
+      "Epoch 909, Loss: 30.0333, Best: 26.1240\n",
+      "Epoch 910, Loss: 29.0947, Best: 26.1240\n",
+      "Epoch 911, Loss: 29.7588, Best: 26.1240\n",
+      "Epoch 912, Loss: 29.0724, Best: 26.1240\n",
+      "Epoch 913, Loss: 29.6843, Best: 26.1240\n",
+      "Epoch 914, Loss: 29.2582, Best: 26.1240\n",
+      "Epoch 915, Loss: 30.3395, Best: 26.1240\n",
+      "Epoch 916, Loss: 31.0450, Best: 26.1240\n",
+      "Epoch 917, Loss: 26.4661, Best: 26.1240\n",
+      "Epoch 918, Loss: 28.8231, Best: 26.1240\n",
+      "Epoch 919, Loss: 29.3307, Best: 26.1240\n",
+      "Epoch 920, Loss: 30.6067, Best: 26.1240\n",
+      "Epoch 921, Loss: 26.9962, Best: 26.1240\n",
+      "Epoch 922, Loss: 30.7886, Best: 26.1240\n",
+      "Epoch 923, Loss: 28.9960, Best: 26.1240\n",
+      "Epoch 924, Loss: 29.3035, Best: 26.1240\n",
+      "Epoch 925, Loss: 27.5148, Best: 26.1240\n",
+      "Epoch 926, Loss: 31.4326, Best: 26.1240\n",
+      "Epoch 927, Loss: 29.8251, Best: 26.1240\n",
+      "Epoch 928, Loss: 26.9739, Best: 26.1240\n",
+      "Epoch 929, Loss: 28.3474, Best: 26.1240\n",
+      "Epoch 930, Loss: 26.6139, Best: 26.1240\n",
+      "Epoch 931, Loss: 28.3303, Best: 26.1240\n",
+      "Epoch 932, Loss: 31.2511, Best: 26.1240\n",
+      "Epoch 933, Loss: 27.8164, Best: 26.1240\n",
+      "Epoch 934, Loss: 29.0204, Best: 26.1240\n",
+      "Epoch 935, Loss: 28.3277, Best: 26.1240\n",
+      "Epoch 936, Loss: 27.4496, Best: 26.1240\n",
+      "Epoch 937, Loss: 30.6179, Best: 26.1240\n",
+      "Epoch 938, Loss: 29.7374, Best: 26.1240\n",
+      "Epoch 939, Loss: 28.7153, Best: 26.1240\n",
+      "Epoch 940, Loss: 30.7621, Best: 26.1240\n",
+      "Epoch 941, Loss: 30.2818, Best: 26.1240\n",
+      "Epoch 942, Loss: 28.2960, Best: 26.1240\n",
+      "Epoch 943, Loss: 30.6707, Best: 26.1240\n",
+      "Epoch 944, Loss: 28.2636, Best: 26.1240\n",
+      "Epoch 945, Loss: 30.7352, Best: 26.1240\n",
+      "Epoch 946, Loss: 28.7800, Best: 26.1240\n",
+      "Epoch 947, Loss: 28.7569, Best: 26.1240\n",
+      "Epoch 948, Loss: 30.8037, Best: 26.1240\n",
+      "Epoch 949, Loss: 27.6367, Best: 26.1240\n",
+      "Epoch 950, Loss: 26.7307, Best: 26.1240\n",
+      "Epoch 951, Loss: 28.7464, Best: 26.1240\n",
+      "Epoch 952, Loss: 26.7274, Best: 26.1240\n",
+      "Epoch 953, Loss: 27.7063, Best: 26.1240\n",
+      "Epoch 954, Loss: 28.6251, Best: 26.1240\n",
+      "Epoch 955, Loss: 28.3561, Best: 26.1240\n",
+      "Epoch 956, Loss: 26.7971, Best: 26.1240\n",
+      "Epoch 957, Loss: 27.1979, Best: 26.1240\n",
+      "Epoch 958, Loss: 27.8337, Best: 26.1240\n",
+      "Epoch 959, Loss: 28.8871, Best: 26.1240\n",
+      "Epoch 960, Loss: 29.6547, Best: 26.1240\n",
+      "Epoch 961, Loss: 28.8666, Best: 26.1240\n",
+      "Epoch 962, Loss: 27.9745, Best: 26.1240\n",
+      "Epoch 963, Loss: 28.8620, Best: 26.1240\n",
+      "Epoch 964, Loss: 28.1696, Best: 26.1240\n",
+      "Epoch 965, Loss: 28.4254, Best: 26.1240\n",
+      "Epoch 966, Loss: 28.4344, Best: 26.1240\n",
+      "Epoch 967, Loss: 29.7876, Best: 26.1240\n",
+      "Epoch 968, Loss: 30.3926, Best: 26.1240\n",
+      "Epoch 969, Loss: 26.8781, Best: 26.1240\n",
+      "Epoch 970, Loss: 30.7460, Best: 26.1240\n",
+      "Epoch 971, Loss: 30.2433, Best: 26.1240\n",
+      "Epoch 972, Loss: 29.0718, Best: 26.1240\n",
+      "Epoch 973, Loss: 31.1777, Best: 26.1240\n",
+      "Epoch 974, Loss: 28.4244, Best: 26.1240\n",
+      "Epoch 975, Loss: 29.9365, Best: 26.1240\n",
+      "Epoch 976, Loss: 35.1277, Best: 26.1240\n",
+      "Epoch 977, Loss: 28.4883, Best: 26.1240\n",
+      "Epoch 978, Loss: 30.1169, Best: 26.1240\n",
+      "Epoch 979, Loss: 29.4951, Best: 26.1240\n",
+      "Epoch 980, Loss: 27.3577, Best: 26.1240\n",
+      "Epoch 981, Loss: 28.4834, Best: 26.1240\n",
+      "Epoch 982, Loss: 28.4458, Best: 26.1240\n",
+      "Epoch 983, Loss: 29.3698, Best: 26.1240\n",
+      "Epoch 984, Loss: 28.0598, Best: 26.1240\n",
+      "Epoch 985, Loss: 30.8905, Best: 26.1240\n",
+      "Epoch 986, Loss: 29.4141, Best: 26.1240\n",
+      "Epoch 987, Loss: 27.7255, Best: 26.1240\n",
+      "Epoch 988, Loss: 27.7016, Best: 26.1240\n",
+      "Epoch 989, Loss: 30.2936, Best: 26.1240\n",
+      "Epoch 990, Loss: 29.3387, Best: 26.1240\n",
+      "Epoch 991, Loss: 29.0193, Best: 26.1240\n",
+      "Epoch 992, Loss: 28.1378, Best: 26.1240\n",
+      "Epoch 993, Loss: 29.7636, Best: 26.1240\n",
+      "Epoch 994, Loss: 28.5948, Best: 26.1240\n",
+      "Epoch 995, Loss: 30.6817, Best: 26.1240\n",
+      "Epoch 996, Loss: 27.9221, Best: 26.1240\n",
+      "Epoch 997, Loss: 28.4937, Best: 26.1240\n",
+      "Epoch 998, Loss: 29.0485, Best: 26.1240\n",
+      "Epoch 999, Loss: 29.6285, Best: 26.1240\n",
+      "Epoch 1000, Loss: 28.2253, Best: 26.1240\n",
       "\n",
       "Training completed!\n"
      ]
@@ -1118,23 +1204,21 @@
    "source": [
     "import copy\n",
     "\n",
-    "EPOCH = 1000\n",
-    "\n",
-    "# 최고 성능 추적을 위한 변수들\n",
-    "best_loss = float('inf')  # 가장 좋은 loss 값\n",
-    "best_model_state = None   # 최고 성능 모델의 state_dict\n",
-    "\n",
     "for i in range(EPOCH):\n",
     "    total_loss = 0\n",
     "    for X_batch, Y_batch, lengths_batch in dataloader:\n",
-    "        X_batch = X_batch.to(device)\n",
-    "        Y_batch = Y_batch.to(device)\n",
-    "        lengths_batch = lengths_batch.to(device)\n",
+    "        X_batch = X_batch.to(device, non_blocking=True)  # non_blocking으로 성능 향상\n",
+    "        Y_batch = Y_batch.to(device, non_blocking=True)\n",
+    "        lengths_batch = lengths_batch.to(device, non_blocking=True)\n",
+    "\n",
+    "        # 첫 번째 배치에서 GPU 사용 확인\n",
+    "        if i % 100 == 0:\n",
+    "            print(f\"GPU memory allocated: {torch.cuda.memory_allocated()/1024**2:.1f} MB\")\n",
     "\n",
     "        optimizer.zero_grad()\n",
-    "        outputs = model(X_batch, lengths_batch, Y_batch)\n",
+    "        outputs = model.forward(X_batch, lengths_batch, Y_batch)\n",
     "\n",
-    "        loss = criterion.forward(outputs, Y_batch, lengths_batch)\n",
+    "        loss = criterion(outputs, Y_batch, lengths_batch)\n",
     "\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
@@ -1179,10 +1263,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "75530554",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.load_state_dict(torch.load('DIVA_Model_dict.pt')) # 모델 가중치, 매개변수 불러오기"
    ]