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Acc 27.08% | Grad 0.00124422\n","Ep 2 | Step 650 | Loss 2.7864 | Acc 28.00% | Grad 0.00101047\n","Ep 2 | Step 700 | Loss 2.8609 | Acc 28.38% | Grad 0.00131712\n","Ep 2 | Step 750 | Loss 2.9523 | Acc 28.67% | Grad 0.00120121\n","Ep 2 | Step 800 | Loss 2.6628 | Acc 29.59% | Grad 0.00138555\n","Ep 2 | Step 850 | Loss 2.8657 | Acc 29.27% | Grad 0.00132173\n","Ep 2 | Step 900 | Loss 2.5587 | Acc 28.95% | Grad 0.00134417\n","Ep 2 | Step 950 | Loss 2.7971 | Acc 30.70% | Grad 0.00115698\n","Ep 3 | Step 50 | Loss 2.7408 | Acc 32.83% | Grad 0.00125827\n","Ep 3 | Step 100 | Loss 2.6159 | Acc 32.92% | Grad 0.00141710\n","Ep 3 | Step 150 | Loss 2.3914 | Acc 32.67% | Grad 0.00120163\n","Ep 3 | Step 200 | Loss 2.4997 | Acc 33.80% | Grad 0.00155457\n","Ep 3 | Step 250 | Loss 2.6259 | Acc 32.53% | Grad 0.00136161\n","Ep 3 | Step 300 | Loss 2.3535 | Acc 33.28% | Grad 0.00134072\n","Ep 3 | Step 350 | Loss 2.3601 | Acc 33.70% | Grad 0.00151076\n","Ep 3 | Step 400 | Loss 2.7105 | Acc 33.16% | Grad 0.00116704\n","Ep 3 | Step 450 | Loss 2.5348 | Acc 34.95% | Grad 0.00154246\n","Ep 3 | Step 500 | Loss 2.5322 | Acc 33.73% | Grad 0.00152612\n","Ep 3 | Step 550 | Loss 2.7415 | Acc 33.12% | Grad 0.00130826\n","Ep 3 | Step 600 | Loss 2.4328 | Acc 35.27% | Grad 0.00112656\n","Ep 3 | Step 650 | Loss 2.7274 | Acc 35.56% | Grad 0.00147477\n","Ep 3 | Step 700 | Loss 2.5199 | Acc 35.75% | Grad 0.00123642\n","Ep 3 | Step 750 | Loss 2.3627 | Acc 35.92% | Grad 0.00153670\n","Ep 3 | Step 800 | Loss 2.5831 | Acc 36.61% | Grad 0.00145630\n","Ep 3 | Step 850 | Loss 2.6554 | Acc 35.66% | Grad 0.00154350\n","Ep 3 | Step 900 | Loss 2.5891 | Acc 35.48% | Grad 0.00133522\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_1468/2963696157.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    161\u001b[0m     \u001b[0mrunning_corrects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0mrunning_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    739\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    740\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 741\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    742\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    743\u001b[0m             if (\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1522\u001b[0m                     \u001b[0;34m\"Invalid iterator state: shutdown or no outstanding tasks when fetching next data\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1523\u001b[0m                 )\n\u001b[0;32m-> 1524\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1525\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1471\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1472\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_alive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1473\u001b[0;31m                 \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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1310\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1311\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1312\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    357\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["import os\n","import io\n","import math\n","from PIL import Image\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.optim as optim\n","from torch.utils.data import Dataset, DataLoader\n","import torchvision.transforms as transforms\n","from datasets import load_dataset\n","\n","DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","BATCH_SIZE_TRAIN = 128\n","BATCH_SIZE_VAL = 32\n","EPOCHS = 15\n","LR = 1e-3\n","WEIGHT_DECAY = 1e-4\n","MODEL_SAVE_PATH = \"LookThem_V8_5_Stabilized.pth\"\n","\n","# --- TRANSFORMS ---\n","transform_train = transforms.Compose([\n","    transforms.Lambda(lambda img: img.convert(\"RGB\")),\n","    transforms.RandomResizedCrop((256, 256), scale=(0.8, 1.0)), # Stronger augmentation\n","    transforms.RandomHorizontalFlip(),\n","    transforms.RandomRotation(15), # Add random rotation\n","    transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1), # Add color jitter\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n","])\n","\n","transform_val = transforms.Compose([\n","    transforms.Lambda(lambda img: img.convert(\"RGB\")),\n","    transforms.Resize((256, 256)),\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n","])\n","\n","# --- DATASET LOADER ---\n","raw_train = load_dataset(\"clane9/imagenet-100\", split=\"train\")\n","raw_val = load_dataset(\"clane9/imagenet-100\", split=\"validation\")\n","\n","class ImageNet100ParquetDataset(Dataset):\n","    def __init__(self, hf_subset, transform=None):\n","        self.dataset = hf_subset\n","        self.transform = transform\n","    def __getitem__(self, index):\n","        row = self.dataset[index]\n","        img = row[\"image\"]\n","        if not isinstance(img, Image.Image):\n","            img = Image.open(io.BytesIO(img))\n","        if self.transform: img = self.transform(img)\n","        return img, row[\"label\"]\n","    def __len__(self):\n","        return len(self.dataset)\n","\n","train_loader = DataLoader(ImageNet100ParquetDataset(raw_train, transform_train), batch_size=BATCH_SIZE_TRAIN, shuffle=True, num_workers=2, pin_memory=True)\n","val_loader = DataLoader(ImageNet100ParquetDataset(raw_val, transform_val), batch_size=BATCH_SIZE_VAL, shuffle=False, num_workers=2, pin_memory=True)\n","\n","# --- STABILIZED LOOKTHEM LAYER ---\n","class LookThemLayer(nn.Module):\n","    def __init__(self, num_tokens, in_features, hidden_dim):\n","        super().__init__()\n","        self.num_tokens = num_tokens\n","        self.mod1_w1 = nn.Parameter(torch.randn(num_tokens, in_features, hidden_dim))\n","        self.mod1_b1 = nn.Parameter(torch.zeros(num_tokens, hidden_dim))\n","        self.mod1_w2 = nn.Parameter(torch.randn(num_tokens, hidden_dim, 1))\n","        self.mod1_b2 = nn.Parameter(torch.zeros(num_tokens, 1))\n","        self.mod2_w1 = nn.Parameter(torch.randn(num_tokens, in_features, hidden_dim))\n","        self.mod2_b1 = nn.Parameter(torch.zeros(num_tokens, hidden_dim))\n","        self.mod2_w2 = nn.Parameter(torch.randn(num_tokens, hidden_dim, 1))\n","        self.mod2_b2 = nn.Parameter(torch.zeros(num_tokens, 1))\n","        self.trans_w = nn.Parameter(torch.randn(num_tokens, 1, 1))\n","        self.trans_b = nn.Parameter(torch.zeros(num_tokens, 1))\n","        self._init_weights()\n","\n","    def _init_weights(self):\n","        for w in [self.mod1_w1, self.mod2_w1, self.mod1_w2, self.mod2_w2]:\n","            nn.init.xavier_uniform_(w)\n","\n","    def forward(self, x):\n","        N = self.num_tokens\n","        h1 = torch.einsum(\"bti,tij->btj\", x, self.mod1_w1) + self.mod1_b1\n","        out_m1 = torch.einsum(\"btj,tjk->btk\", F.gelu(h1), self.mod1_w2) + self.mod1_b2\n","        h2 = torch.einsum(\"bti,tij->btj\", x, self.mod2_w1) + self.mod2_b1\n","        out_m2 = torch.einsum(\"btj,tjk->btk\", F.gelu(h2), self.mod2_w2) + self.mod2_b2\n","\n","        out_m2_safe = torch.sign(out_m2) * torch.clamp(torch.abs(out_m2), min=1e-6)\n","        compare = torch.tanh(out_m1.unsqueeze(2) / out_m2_safe.unsqueeze(1))\n","        compare2 = torch.tanh(out_m1.unsqueeze(1) / out_m2_safe.unsqueeze(2))\n","\n","        trans_compare = torch.einsum(\"bije,jef->bijf\", compare, self.trans_w) + self.trans_b.view(1, 1, N, 1)\n","        trans_compare2 = torch.einsum(\"bije,jef->bijf\", compare2, self.trans_w) + self.trans_b.view(1, 1, N, 1)\n","\n","        interaksi = (trans_compare * x.unsqueeze(2) + trans_compare2 * x.unsqueeze(1)) / 2\n","        mask = (1.0 - torch.eye(N, device=x.device)).view(1, N, N, 1)\n","        return (interaksi * mask).sum(dim=2) / (N - 1.0)\n","\n","class LiteResidualBlock(nn.Module):\n","    def __init__(self, dim, dropout=0.05):\n","        super().__init__()\n","        self.block = nn.Sequential(nn.Linear(dim, dim), nn.GELU(), nn.Dropout(dropout), nn.Linear(dim, dim))\n","        self.norm = nn.LayerNorm(dim)\n","    def forward(self, x):\n","        return self.norm(x + self.block(x))\n","\n","class LookThemV8_5(nn.Module):\n","    def __init__(self):\n","        super().__init__()\n","        self.stream_a = nn.Sequential(nn.Conv2d(3, 16, 3, 2, 1), nn.BatchNorm2d(16), nn.GELU(), nn.Conv2d(16, 32, 3, 2, 1), nn.BatchNorm2d(32), nn.GELU(), nn.Conv2d(32, 64, 3, 2, 1), nn.BatchNorm2d(64), nn.GELU(), nn.AdaptiveMaxPool2d((8, 8)))\n","        self.stream_b = nn.Sequential(nn.Conv2d(3, 16, 3, 2, 1), nn.BatchNorm2d(16), nn.GELU(), nn.Conv2d(16, 32, 3, 1, 1), nn.BatchNorm2d(32), nn.GELU(), nn.Conv2d(32, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.GELU(), nn.AdaptiveMaxPool2d((8, 8)))\n","\n","        self.lookthemA = LookThemLayer(64, 64, 32)\n","        self.lookthemB = LookThemLayer(64, 64, 32)\n","        self.lookthem_comb = LookThemLayer(64, 128, 32)\n","        self.comb_norm = nn.LayerNorm(128)\n","        self.FFN1 = nn.Conv1d(128, 64, 1)\n","\n","        self.lookthem2 = LookThemLayer(64, 64, 32)\n","        self.norm2 = nn.LayerNorm(64)\n","        self.FFN2 = nn.Conv1d(64, 64, 1)\n","\n","        self.lookthem3 = LookThemLayer(64, 64, 32)\n","        self.norm3 = nn.LayerNorm(64)\n","        self.FFN3 = nn.Conv1d(64, 64, 1)\n","\n","        self.compressor = nn.Conv1d(64, 16, 1)\n","        self.input_proj = nn.Linear(64 * 16, 256)\n","        self.res_blocks = nn.Sequential(LiteResidualBlock(256), LiteResidualBlock(256))\n","        self.head = nn.Sequential(nn.Linear(256, 128), nn.GELU(), nn.Linear(128, 100))\n","\n","    def forward(self, x):\n","        b = x.size(0)\n","        fa = self.lookthemA(self.stream_a(x).view(b, 64, 64).transpose(1, 2))\n","        fb = self.lookthemB(self.stream_b(x).view(b, 64, 64).transpose(1, 2))\n","        x = self.comb_norm(self.lookthem_comb(torch.cat([fa, fb], dim=2)))\n","        x = x.transpose(1, 2)\n","        x = self.FFN1(x).transpose(1, 2)\n","        res = x\n","        x = self.lookthem2(x).transpose(1, 2)\n","        x = self.FFN2(x) + res.transpose(1, 2)\n","        res = x\n","        x = self.lookthem3(x).transpose(1, 2)\n","        x = self.FFN3(x) + res.transpose(1, 2)\n","        x = self.compressor(x).flatten(1)\n","        x = self.res_blocks(self.input_proj(x))\n","        return self.head(x)\n","\n","model = LookThemV8_5().to(DEVICE)\n","if os.path.exists(MODEL_SAVE_PATH):\n","    model.load_state_dict(torch.load(MODEL_SAVE_PATH))\n","\n","optimizer = optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n","criterion = nn.CrossEntropyLoss()\n","scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)\n","\n","print(f\"Model initialized. Total params: {sum(p.numel() for p in model.parameters()):,}\")\n","\n","for epoch in range(EPOCHS):\n","    model.train()\n","    running_corrects = 0\n","    running_samples = 0\n","    for step, (data, target) in enumerate(train_loader):\n","        data, target = data.to(DEVICE), target.to(DEVICE)\n","        optimizer.zero_grad()\n","        output = model(data)\n","        loss = criterion(output, target)\n","        loss.backward()\n","        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n","        optimizer.step()\n","\n","        # Track accuracy\n","        _, preds = torch.max(output, 1)\n","        running_corrects += torch.sum(preds == target.data)\n","        running_samples += data.size(0)\n","\n","        if (step+1) % 50 == 0:\n","            grads = [p.grad.abs().mean().item() for p in model.parameters() if p.grad is not None]\n","            avg_grad = sum(grads)/len(grads) if grads else 0\n","            accuracy = 100.0 * running_corrects.double() / running_samples\n","            print(f\"Ep {epoch+1} | Step {step+1} | Loss {loss.item():.4f} | Acc {accuracy:.2f}% | Grad {avg_grad:.8f}\")\n","            # Optional: reset counters for interval-based accuracy\n","            running_corrects = 0\n","            running_samples = 0\n","\n","    scheduler.step()"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"009ed88c","executionInfo":{"status":"ok","timestamp":1780058848293,"user_tz":-420,"elapsed":4194,"user":{"displayName":"Apa Tau","userId":"02751722439959693254"}},"outputId":"772cf09e-47be-44ec-a4b9-348a1c8ea970"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"]}]},{"cell_type":"code","metadata":{"id":"d7c8209f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1780058871785,"user_tz":-420,"elapsed":71,"user":{"displayName":"Apa Tau","userId":"02751722439959693254"}},"outputId":"45fbc16c-c729-42bc-ad4e-8b60c3e6c8c5"},"source":["model_save_path_gdrive = MODEL_SAVE_PATH\n","torch.save(model.state_dict(), model_save_path_gdrive)\n","print(f\"Model saved to {model_save_path_gdrive}\")"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved to /content/gdrive/My Drive/LookThem_V8_5_Stabilized.pth\n"]}]},{"cell_type":"code","source":["import os\n","import io\n","import math\n","from PIL import Image\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.optim as optim\n","from torch.utils.data import Dataset, DataLoader\n","import torchvision.transforms as transforms\n","from datasets import load_dataset\n","from google.colab import drive\n","\n","# Mount Google Drive to access saved models\n","drive.mount('/content/gdrive', force_remount=True)\n","\n","DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","BATCH_SIZE_TRAIN = 128\n","BATCH_SIZE_VAL = 32\n","EPOCHS = 15\n","LR = 1e-3\n","WEIGHT_DECAY = 1e-4\n","# Update MODEL_SAVE_PATH to point to the Google Drive location\n","MODEL_SAVE_PATH = \"/content/gdrive/My Drive/LookThem_V8_5_Stabilized.pth\"\n","\n","# --- TRANSFORMS ---\n","transform_train = transforms.Compose([\n","    transforms.Lambda(lambda img: img.convert(\"RGB\")),\n","    transforms.RandomResizedCrop((256, 256), scale=(0.8, 1.0)), # Stronger augmentation\n","    transforms.RandomHorizontalFlip(),\n","    transforms.RandomRotation(15), # Add random rotation\n","    transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1), # Add color jitter\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n","])\n","\n","transform_val = transforms.Compose([\n","    transforms.Lambda(lambda img: img.convert(\"RGB\")),\n","    transforms.Resize((256, 256)),\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n","])\n","\n","# --- DATASET LOADER ---\n","raw_train = load_dataset(\"clane9/imagenet-100\", split=\"train\")\n","raw_val = load_dataset(\"clane9/imagenet-100\", split=\"validation\")\n","\n","class ImageNet100ParquetDataset(Dataset):\n","    def __init__(self, hf_subset, transform=None):\n","        self.dataset = hf_subset\n","        self.transform = transform\n","    def __getitem__(self, index):\n","        row = self.dataset[index]\n","        img = row[\"image\"]\n","        if not isinstance(img, Image.Image):\n","            img = Image.open(io.BytesIO(img))\n","        if self.transform: img = self.transform(img)\n","        return img, row[\"label\"]\n","    def __len__(self):\n","        return len(self.dataset)\n","\n","train_loader = DataLoader(ImageNet100ParquetDataset(raw_train, transform_train), batch_size=BATCH_SIZE_TRAIN, shuffle=True, num_workers=2, pin_memory=True)\n","val_loader = DataLoader(ImageNet100ParquetDataset(raw_val, transform_val), batch_size=BATCH_SIZE_VAL, shuffle=False, num_workers=2, pin_memory=True)\n","\n","# --- STABILIZED LOOKTHEM LAYER ---\n","class LookThemLayer(nn.Module):\n","    def __init__(self, num_tokens, in_features, hidden_dim):\n","        super().__init__()\n","        self.num_tokens = num_tokens\n","        self.mod1_w1 = nn.Parameter(torch.randn(num_tokens, in_features, hidden_dim))\n","        self.mod1_b1 = nn.Parameter(torch.zeros(num_tokens, hidden_dim))\n","        self.mod1_w2 = nn.Parameter(torch.randn(num_tokens, hidden_dim, 1))\n","        self.mod1_b2 = nn.Parameter(torch.zeros(num_tokens, 1))\n","        self.mod2_w1 = nn.Parameter(torch.randn(num_tokens, in_features, hidden_dim))\n","        self.mod2_b1 = nn.Parameter(torch.zeros(num_tokens, hidden_dim))\n","        self.mod2_w2 = nn.Parameter(torch.randn(num_tokens, hidden_dim, 1))\n","        self.mod2_b2 = nn.Parameter(torch.zeros(num_tokens, 1))\n","        self.trans_w = nn.Parameter(torch.randn(num_tokens, 1, 1))\n","        self.trans_b = nn.Parameter(torch.zeros(num_tokens, 1))\n","        self._init_weights()\n","\n","    def _init_weights(self):\n","        for w in [self.mod1_w1, self.mod2_w1, self.mod1_w2, self.mod2_w2]:\n","            nn.init.xavier_uniform_(w)\n","\n","    def forward(self, x):\n","        N = self.num_tokens\n","        h1 = torch.einsum(\"bti,tij->btj\", x, self.mod1_w1) + self.mod1_b1\n","        out_m1 = torch.einsum(\"btj,tjk->btk\", F.gelu(h1), self.mod1_w2) + self.mod1_b2\n","        h2 = torch.einsum(\"bti,tij->btj\", x, self.mod2_w1) + self.mod2_b1\n","        out_m2 = torch.einsum(\"btj,tjk->btk\", F.gelu(h2), self.mod2_w2) + self.mod2_b2\n","\n","        out_m2_safe = torch.sign(out_m2) * torch.clamp(torch.abs(out_m2), min=1e-6)\n","        compare = torch.tanh(out_m1.unsqueeze(2) / out_m2_safe.unsqueeze(1))\n","        compare2 = torch.tanh(out_m1.unsqueeze(1) / out_m2_safe.unsqueeze(2))\n","\n","        trans_compare = torch.einsum(\"bije,jef->bijf\", compare, self.trans_w) + self.trans_b.view(1, 1, N, 1)\n","        trans_compare2 = torch.einsum(\"bije,jef->bijf\", compare2, self.trans_w) + self.trans_b.view(1, 1, N, 1)\n","\n","        interaksi = (trans_compare * x.unsqueeze(2) + trans_compare2 * x.unsqueeze(1)) / 2\n","        mask = (1.0 - torch.eye(N, device=x.device)).view(1, N, N, 1)\n","        return (interaksi * mask).sum(dim=2) / (N - 1.0)\n","\n","class LiteResidualBlock(nn.Module):\n","    def __init__(self, dim, dropout=0.05):\n","        super().__init__()\n","        self.block = nn.Sequential(nn.Linear(dim, dim), nn.GELU(), nn.Dropout(dropout), nn.Linear(dim, dim))\n","        self.norm = nn.LayerNorm(dim)\n","    def forward(self, x):\n","        return self.norm(x + self.block(x))\n","\n","class LookThemV8_5(nn.Module):\n","    def __init__(self):\n","        super().__init__()\n","        self.stream_a = nn.Sequential(nn.Conv2d(3, 16, 3, 2, 1), nn.BatchNorm2d(16), nn.GELU(), nn.Conv2d(16, 32, 3, 2, 1), nn.BatchNorm2d(32), nn.GELU(), nn.Conv2d(32, 64, 3, 2, 1), nn.BatchNorm2d(64), nn.GELU(), nn.AdaptiveMaxPool2d((8, 8)))\n","        self.stream_b = nn.Sequential(nn.Conv2d(3, 16, 3, 2, 1), nn.BatchNorm2d(16), nn.GELU(), nn.Conv2d(16, 32, 3, 1, 1), nn.BatchNorm2d(32), nn.GELU(), nn.Conv2d(32, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.GELU(), nn.AdaptiveMaxPool2d((8, 8)))\n","\n","        self.lookthemA = LookThemLayer(64, 64, 32)\n","        self.lookthemB = LookThemLayer(64, 64, 32)\n","        self.lookthem_comb = LookThemLayer(64, 128, 32)\n","        self.comb_norm = nn.LayerNorm(128)\n","        self.FFN1 = nn.Conv1d(128, 64, 1)\n","\n","        self.lookthem2 = LookThemLayer(64, 64, 32)\n","        self.norm2 = nn.LayerNorm(64)\n","        self.FFN2 = nn.Conv1d(64, 64, 1)\n","\n","        self.lookthem3 = LookThemLayer(64, 64, 32)\n","        self.norm3 = nn.LayerNorm(64)\n","        self.FFN3 = nn.Conv1d(64, 64, 1)\n","\n","        self.compressor = nn.Conv1d(64, 16, 1)\n","        self.input_proj = nn.Linear(64 * 16, 256)\n","        self.res_blocks = nn.Sequential(LiteResidualBlock(256), LiteResidualBlock(256))\n","        self.head = nn.Sequential(nn.Linear(256, 128), nn.GELU(), nn.Linear(128, 100))\n","\n","    def forward(self, x):\n","        b = x.size(0)\n","        fa = self.lookthemA(self.stream_a(x).view(b, 64, 64).transpose(1, 2))\n","        fb = self.lookthemB(self.stream_b(x).view(b, 64, 64).transpose(1, 2))\n","        x = self.comb_norm(self.lookthem_comb(torch.cat([fa, fb], dim=2)))\n","        x = x.transpose(1, 2)\n","        x = self.FFN1(x).transpose(1, 2)\n","        res = x\n","        x = self.lookthem2(x).transpose(1, 2)\n","        x = self.FFN2(x) + res.transpose(1, 2)\n","        res = x\n","        x = self.lookthem3(x).transpose(1, 2)\n","        x = self.FFN3(x) + res.transpose(1, 2)\n","        x = self.compressor(x).flatten(1)\n","        x = self.res_blocks(self.input_proj(x))\n","        return self.head(x)\n","\n","model = LookThemV8_5().to(DEVICE)\n","if os.path.exists(MODEL_SAVE_PATH):\n","    print(f\"Loading model from {MODEL_SAVE_PATH}\")\n","    model.load_state_dict(torch.load(MODEL_SAVE_PATH))\n","else:\n","    print(f\"Model not found at {MODEL_SAVE_PATH}. Initializing with new weights.\")\n","\n","optimizer = optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n","criterion = nn.CrossEntropyLoss()\n","scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)\n","\n","print(f\"Model initialized. Total params: {sum(p.numel() for p in model.parameters()):,}\")\n","\n","for epoch in range(EPOCHS):\n","    model.train()\n","    running_corrects = 0\n","    running_samples = 0\n","    for step, (data, target) in enumerate(train_loader):\n","        data, target = data.to(DEVICE), target.to(DEVICE)\n","        optimizer.zero_grad()\n","        output = model(data)\n","        loss = criterion(output, target)\n","        loss.backward()\n","        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n","        optimizer.step()\n","\n","        # Track accuracy\n","        _, preds = torch.max(output, 1)\n","        running_corrects += torch.sum(preds == target.data)\n","        running_samples += data.size(0)\n","\n","        if (step+1) % 50 == 0:\n","            grads = [p.grad.abs().mean().item() for p in model.parameters() if p.grad is not None]\n","            avg_grad = sum(grads)/len(grads) if grads else 0\n","            accuracy = 100.0 * running_corrects.double() / running_samples\n","            print(f\"Ep {epoch+1} | Step {step+1} | Loss {loss.item():.4f} | Acc {accuracy:.2f}% | Grad {avg_grad:.8f}\")\n","            # Optional: reset counters for interval-based accuracy\n","            running_corrects = 0\n","            running_samples = 0\n","\n","    scheduler.step()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["a2e2b805ee004168b6e79ebc8f4e8d21","3ff36e9fe4094e5d9e9920a776d76def","a873590179c249d792322d917ed291cf","3f31b809bbc347d8bb1b3b86c2f2ed82","acc4f8b3a18f4b89a74ff7450de81ee1","d562e3bbe6034c70b14cd4e109ee2ff6","330fa8c4a74443ba8623ddb36aadee31","4ab822b2ff5044e8b48103f7738c3eb7","560ca23e167a48b9a8bc0f71686b10f3","30d48bc846f94894b00426788e354025","6a60001ffb9e4813967b2df6604f0a2d","d952efc3379c4956a6b2b581538f1522","73f4ea1f3e824c209e15399d8eb01faf","b23b1754ae2945ca825213f97fe41713","9ad18c3ca3614c6099a5d41e0d0d9d26","5fa3910ce866492cbc526ed113358901","4dea50351c7745309243672e5bb55fcd","d5064af3cb4f4722ab245d9a6bf6cf59","70c52edce5634b0886ef57971b7bdc5d","49d4768cae884aabb0859fcf081ae93e","301af1b7d9fb42429d21cef917a43e00","8bf9735c67e045e6a17d8bdf0d3ee383","59426e8d39564c35af3e1172f2dfe51c","c8508d16eba9404d9c01c53fcd78748b","77e46106439a4447bbca716b49de7ff4","6c6afd2aa767400899b83bfc43d974de","8c38a8971600440b84bc740a70cf6d8d","062480a83732403585d293d854151cc3","99b49cfde0f34422b7d7e929f60f935c","9dc9b9e7f8544ee8be0f418f31ae58de","133b1f5eabb64feaab25478a5b011c90","7596043105cf496c9a380a0dcf2d8d6b","bf2f8dd4eb2b418cade49bab91d93acb","cacbfeb2ad30404292a87808dcbf96cd","9c62e983add4423183a50e656b4e40c9","391b3da2251e40af94d7350f42d672f0","5c94ec789f52401e9aae9c99e65fb25c","c5dc79eac39b4691b2e9b31e628274e2","1a9a3df121254d8da36f7126137216ce","92c0f1fa7aeb4696b1733095d16b8536","6b30911dc3544f5088f9b730d3219103","d5e756fb817047ee98a36b8c16bd1d31","946ff570fb53458bb688af5016a70986","7fb41c64025b43e5974e7f5b49a401a0","7f61640dac2644a5b52d8d286f4457e1","ad302ac2a978443d8b4f45a0933022b8","42292bd44cd84697a44f04c26a74a696","2d61f7d9d51f4eee842bcab6e6dfacf2","ad7d607eee9b451c930d336c0a2d9ed1","631bbddde29640e493dce6e24776f62e","9877cea2c2554fb7adb2d1bf7e73f8e2","f305b5a3bc1444479a1c5a7a9d1b9bbc","409ba3b5b1bf4a49ada2140c445ec4ac","975bcc7f6fe644dfad47f94fc8512e3c","f2f5d6a243744b388645855137b50a31","99e0a57461c14bd291b68fbf215bfeb1","af4a12ad80494fee96e48f4e2a4a73a3","26fd66ccccfb48b4b83b288817326ed0","c189b618623b490e8cf9d0b1a3dd321d","4d579e9d292f408c879782d60bdaafe2","2c27398ec3074684acc687c882c25775","dd3f6d5ca647425ba20016bd8f62d5ec","168bb33f161444a4b66d62496495390b","24da989fa2ea4996b818d5f192b0e8f9","68bce239ce5f493ea7885ecadd9183f6","041be1bb920c4d9189582698ebed1094","908d9ad1078940e9a4a00f841dccde2f","dd7332ef21f14b9b9464b77bc0a0e467","89d057e4122c4051b339518217cf493b","04694f6017444bb188caaa0de6a07a3f","1347f2cedb244a4a8e27e244ce7fca64","86795752189548d380e164095f29f935","dc5b0b6d261a4e2aa7d77f70d9cf3e4a","72a34e81f4cb4d6d83c50c5a6256cbb7","89b56dfe55754ed3978a1cccbadd084a","548e7da655604ad387f5be81a5549d3c","0ad350ee45764020a5c9aaeb59d114b0","7d44e8f155a046f8bfd2868860baece3","6ca1764a05aa4e53af7aba8e7eba2b57","56e6dfdafb3d4225afcf5ba5e1ca9cff","4c3eb2b907a144519d9384c12fe05dc7","8a7fb845ed774d10bff1355d36814c12","f08ea5baae8d47dcaaf13d47189df6d3","323e2a696f3d45aab9da3119ce0be41d","48ce8d71c6b24732a6dbf68705b28d9e","72f9d730d422480087460f8b8c52320d","6d9be61f63be4707a0f5408590f23359","a80512b5808d4ab98617ff5157f7415c","2e23c927dc1348d18ef92bd886b5795c","f8cb7b7fed8e48da8bb353a868a43f3d","a21b95a158514eaaa28667369e83cf62","8aea7b4e852d43f58d73dfad8d75bf8c","5283eb818dc64e49996c9bfe2cf4619a","ae6f397ff60c473d8bb38f5cb58b8ec0","83232dbd4f634c798d646abe655b12e9","376b51c28cba4847ab3d0c6868a3f741","57d71f52d1ea425b8018178b9c5ce42d","aded34f43e394ac8ad8012101ce8195b","676501ce68fd49bd8997dc5358832a4b","ebe01cefef0d4bc5a2a293fde0d46701","5cf8ff2b06654162b896d61378330dd5","9d44f1e4d1e94eabb2bb90a312ecfa47","f76cebb6faa74f90a436182565b83fab","56dcfe1a89e04fa089a9043c34d44e7a","08560d3838cd4fc88762b059ecfb24e0","a2c63963479d43b9843b8fc04e332686","3c82dcb696bd439fb7544347017f5cf3","759d98fbf7734466a35e627060501a6c","25ff51bc0399428e8be443b55115a11e","5bb559b97f634266a38bd4ce7ed0b9f2","df6b1a32d7f2407382b9f651d1617a5b","3ed16f089f4447008aeeda32eda098cd","88a12b3d38894c9f86127f4236e7932a","e95c5bb7fcf2432abcb63c32fa7b8216","54d9c1daae3f45b4ba0b614f01d41dab","7718398656224cef8d72d90e6507b081","706979de650e49a78405192495ecca85","25e4b8e9e32a44b6b5ea57fbedca402b","a8d8960c984b461088a3ba9d488ee175","5e1842b78c3d4e578a3fe6f5e71ec672","b9c9d4c1fca8420eb4f1e1204ed0de12","a8f6faead874414aa48f573a33ce2de4","26ad363cf9f94bc3b6caf3fe80a68ba8","cc029092fd0e4f848cab6d57d0df0845","6ecc5ae0ac26458587f3f4299029f78a","a980b93c8509445b9651550e301d95d6","76cb646d96f34547a2ecd5d885e8cf24","3dda9142ccde40a5bb65ca795e8e94cc","e07022554d3f40f193b0b974c7183bc3","1ecb45d255df404bb22cb1ea0421772a","d5057c7660a8404ea567323e9800731c","41212b0012dc4646aeb8378941961260","bcfe20ba7f184b91b78d00dc36025866","044f1db3183545e3ad20245d88966660","b5d6633543ac41288d412fb4f8cf8d43","74a58a51eaff444ca35876446f05c570","2a2d50554550477a9d736504c9661c96","18bb3dde97304c2191da12dd190f6414","6435a4e7a3b04bff9a834cb23699e0e3","e8b9674a811c447fb1b86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Tau","userId":"02751722439959693254"}},"outputId":"c7d732b7-fd2c-47e9-9817-7b5b20e2095b"},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Mounted at /content/gdrive\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:112: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n","Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n","WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a2e2b805ee004168b6e79ebc8f4e8d21","version_major":2,"version_minor":0},"text/plain":["README.md:   0%|          | 0.00/7.87k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d952efc3379c4956a6b2b581538f1522","version_major":2,"version_minor":0},"text/plain":["Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"59426e8d39564c35af3e1172f2dfe51c","version_major":2,"version_minor":0},"text/plain":["Resolving data files:   0%|          | 0/17 [00:00<?, 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Total params: 2,253,236\n","Ep 1 | Step 50 | Loss 2.4034 | Acc 36.66% | Grad 0.00155892\n","Ep 1 | Step 100 | Loss 2.5810 | Acc 36.97% | Grad 0.00137576\n","Ep 1 | Step 150 | Loss 2.5934 | Acc 38.14% | Grad 0.00166423\n","Ep 1 | Step 200 | Loss 2.2822 | Acc 37.97% | Grad 0.00135740\n","Ep 1 | Step 250 | Loss 2.4116 | Acc 37.45% | Grad 0.00141987\n","Ep 1 | Step 300 | Loss 2.3882 | Acc 38.56% | Grad 0.00152575\n","Ep 1 | Step 350 | Loss 2.4563 | Acc 37.80% | Grad 0.00151905\n","Ep 1 | Step 400 | Loss 2.3676 | Acc 37.66% | Grad 0.00165188\n","Ep 1 | Step 450 | Loss 2.4178 | Acc 38.67% | Grad 0.00134275\n","Ep 1 | Step 500 | Loss 2.5261 | Acc 38.34% | Grad 0.00147284\n","Ep 1 | Step 550 | Loss 2.4749 | Acc 39.33% | Grad 0.00126390\n","Ep 1 | Step 600 | Loss 2.5513 | Acc 39.31% | Grad 0.00169036\n","Ep 1 | Step 650 | Loss 2.2192 | Acc 39.84% | Grad 0.00152626\n","Ep 1 | Step 700 | Loss 2.5243 | Acc 38.78% | Grad 0.00137621\n","Ep 1 | Step 750 | Loss 2.1053 | Acc 39.72% | Grad 0.00109242\n","Ep 1 | Step 800 | Loss 2.1441 | Acc 41.00% | Grad 0.00138786\n","Ep 1 | Step 850 | Loss 2.2332 | Acc 39.38% | Grad 0.00134677\n","Ep 1 | Step 900 | Loss 2.3249 | Acc 41.77% | Grad 0.00140563\n","Ep 1 | Step 950 | Loss 2.3368 | Acc 40.30% | Grad 0.00149071\n","Ep 2 | Step 50 | Loss 2.1470 | Acc 42.59% | Grad 0.00134655\n","Ep 2 | Step 100 | Loss 2.3636 | Acc 42.58% | Grad 0.00142412\n","Ep 2 | Step 150 | Loss 2.3189 | Acc 41.59% | Grad 0.00114002\n","Ep 2 | Step 200 | Loss 2.0489 | Acc 41.45% | Grad 0.00147821\n","Ep 2 | Step 250 | Loss 2.2704 | Acc 42.66% | Grad 0.00135301\n","Ep 2 | Step 300 | Loss 2.3110 | Acc 43.12% | Grad 0.00139972\n","Ep 2 | Step 350 | Loss 2.2944 | Acc 42.02% | Grad 0.00153173\n","Ep 2 | Step 400 | Loss 2.1033 | Acc 41.61% | Grad 0.00148663\n","Ep 2 | Step 450 | Loss 2.2446 | Acc 42.39% | Grad 0.00139748\n","Ep 2 | Step 500 | Loss 2.2280 | Acc 41.77% | Grad 0.00165063\n","Ep 2 | Step 550 | Loss 2.0809 | Acc 42.53% | Grad 0.00135748\n","Ep 2 | Step 600 | Loss 2.0918 | Acc 43.25% | Grad 0.00122340\n","Ep 2 | Step 650 | Loss 2.1660 | Acc 42.92% | Grad 0.00136779\n","Ep 2 | Step 700 | Loss 2.0341 | Acc 43.86% | Grad 0.00147593\n","Ep 2 | Step 750 | Loss 2.0274 | Acc 44.14% | Grad 0.00135473\n","Ep 2 | Step 800 | Loss 2.3306 | Acc 42.91% | Grad 0.00123244\n","Ep 2 | Step 850 | Loss 2.4098 | Acc 42.41% | Grad 0.00148154\n","Ep 2 | Step 900 | Loss 2.2983 | Acc 42.48% | Grad 0.00147011\n","Ep 2 | Step 950 | Loss 2.3055 | Acc 43.20% | Grad 0.00158771\n","Ep 3 | Step 50 | Loss 2.3503 | Acc 45.38% | Grad 0.00141837\n","Ep 3 | Step 100 | Loss 2.0030 | Acc 44.88% | Grad 0.00137477\n","Ep 3 | Step 150 | Loss 1.9884 | Acc 44.00% | Grad 0.00119402\n","Ep 3 | Step 200 | Loss 2.0562 | Acc 45.28% | Grad 0.00135314\n","Ep 3 | Step 250 | Loss 2.3820 | Acc 44.89% | Grad 0.00150266\n","Ep 3 | Step 300 | Loss 2.2053 | Acc 44.44% | Grad 0.00146790\n","Ep 3 | Step 350 | Loss 2.1122 | Acc 42.14% | Grad 0.00131437\n","Ep 3 | Step 400 | Loss 2.3598 | Acc 43.83% | Grad 0.00123952\n","Ep 3 | Step 450 | Loss 2.2807 | Acc 43.94% | Grad 0.00130034\n","Ep 3 | Step 500 | Loss 2.2860 | Acc 44.67% | Grad 0.00154355\n","Ep 3 | Step 550 | Loss 1.9714 | Acc 45.14% | Grad 0.00147530\n","Ep 3 | Step 600 | Loss 1.7980 | Acc 45.67% | Grad 0.00150303\n","Ep 3 | Step 650 | Loss 2.0401 | Acc 45.75% | Grad 0.00150967\n","Ep 3 | Step 700 | Loss 2.1462 | Acc 45.70% | Grad 0.00169821\n","Ep 3 | Step 750 | Loss 2.5596 | Acc 44.25% | Grad 0.00141623\n","Ep 3 | Step 800 | Loss 1.8795 | Acc 45.67% | Grad 0.00148474\n","Ep 3 | Step 850 | Loss 2.1789 | Acc 45.72% | Grad 0.00153971\n","Ep 3 | Step 900 | Loss 2.0849 | Acc 45.52% | Grad 0.00140873\n","Ep 3 | Step 950 | Loss 1.8474 | Acc 46.05% | Grad 0.00160510\n","Ep 4 | Step 50 | Loss 2.0573 | Acc 48.31% | Grad 0.00148070\n","Ep 4 | Step 100 | Loss 1.8016 | Acc 47.25% | Grad 0.00142723\n","Ep 4 | Step 150 | Loss 1.9745 | Acc 47.27% | Grad 0.00170767\n","Ep 4 | Step 200 | Loss 1.6541 | Acc 48.22% | Grad 0.00158505\n","Ep 4 | Step 250 | Loss 2.0298 | Acc 47.02% | Grad 0.00131076\n","Ep 4 | Step 300 | Loss 1.9381 | Acc 48.94% | Grad 0.00136507\n","Ep 4 | Step 350 | Loss 1.8258 | Acc 46.66% | Grad 0.00131198\n","Ep 4 | Step 400 | Loss 1.9162 | Acc 47.39% | Grad 0.00159852\n","Ep 4 | Step 450 | Loss 2.1749 | Acc 46.78% | Grad 0.00144610\n","Ep 4 | Step 500 | Loss 1.8281 | Acc 48.12% | Grad 0.00171270\n","Ep 4 | Step 550 | Loss 1.9057 | Acc 47.69% | Grad 0.00166238\n","Ep 4 | Step 600 | Loss 1.8610 | Acc 48.17% | Grad 0.00178746\n","Ep 4 | Step 650 | Loss 2.1965 | Acc 47.19% | Grad 0.00147898\n","Ep 4 | Step 700 | Loss 2.0055 | Acc 47.86% | Grad 0.00133516\n","Ep 4 | Step 750 | Loss 2.1844 | Acc 49.36% | Grad 0.00142381\n","Ep 4 | Step 800 | Loss 2.2416 | Acc 48.09% | Grad 0.00156316\n","Ep 4 | Step 850 | Loss 1.9370 | Acc 47.97% | Grad 0.00127875\n","Ep 4 | Step 900 | Loss 2.0970 | Acc 47.39% | Grad 0.00132557\n","Ep 4 | Step 950 | Loss 1.9282 | Acc 47.88% | Grad 0.00152135\n","Ep 5 | Step 50 | Loss 1.7696 | Acc 51.11% | Grad 0.00165341\n","Ep 5 | Step 100 | Loss 1.7520 | Acc 50.08% | Grad 0.00137686\n","Ep 5 | Step 150 | Loss 1.7118 | Acc 50.39% | Grad 0.00141875\n","Ep 5 | Step 200 | Loss 1.7404 | Acc 50.39% | Grad 0.00123784\n","Ep 5 | Step 250 | Loss 1.7452 | Acc 50.03% | Grad 0.00199038\n","Ep 5 | Step 300 | Loss 1.8097 | Acc 50.30% | Grad 0.00157968\n","Ep 5 | Step 350 | Loss 1.7221 | Acc 49.61% | Grad 0.00126435\n","Ep 5 | Step 400 | Loss 1.9299 | Acc 50.17% | Grad 0.00158356\n","Ep 5 | Step 450 | Loss 2.0209 | Acc 49.83% | Grad 0.00136259\n","Ep 5 | Step 500 | Loss 1.7490 | Acc 49.27% | Grad 0.00147060\n","Ep 5 | Step 550 | Loss 1.9862 | Acc 49.16% | Grad 0.00177045\n","Ep 5 | Step 600 | Loss 2.0896 | Acc 49.94% | Grad 0.00156890\n","Ep 5 | Step 650 | Loss 1.8647 | Acc 50.91% | Grad 0.00150267\n","Ep 5 | Step 700 | Loss 1.6821 | Acc 49.92% | Grad 0.00158650\n","Ep 5 | Step 750 | Loss 2.2412 | Acc 48.69% | Grad 0.00146199\n","Ep 5 | Step 800 | Loss 2.1368 | Acc 49.45% | Grad 0.00155725\n","Ep 5 | Step 850 | Loss 1.8791 | Acc 49.27% | Grad 0.00136028\n","Ep 5 | Step 900 | Loss 1.9666 | Acc 49.23% | Grad 0.00155627\n","Ep 5 | Step 950 | Loss 1.8905 | Acc 50.05% | Grad 0.00150037\n","Ep 6 | Step 50 | Loss 1.8368 | Acc 51.95% | Grad 0.00150831\n","Ep 6 | Step 100 | Loss 1.8989 | Acc 52.11% | Grad 0.00142608\n","Ep 6 | Step 150 | Loss 1.7490 | Acc 53.36% | Grad 0.00141420\n","Ep 6 | Step 200 | Loss 1.7663 | Acc 52.52% | Grad 0.00144913\n","Ep 6 | Step 250 | Loss 2.0945 | Acc 52.42% | Grad 0.00149887\n","Ep 6 | Step 300 | Loss 1.5948 | Acc 52.17% | Grad 0.00150260\n","Ep 6 | Step 350 | Loss 1.6549 | Acc 52.12% | Grad 0.00162027\n","Ep 6 | Step 400 | Loss 1.8901 | Acc 51.95% | Grad 0.00172534\n","Ep 6 | Step 450 | Loss 1.7260 | Acc 51.94% | Grad 0.00162619\n","Ep 6 | Step 500 | Loss 2.1196 | Acc 50.88% | Grad 0.00159258\n","Ep 6 | Step 550 | Loss 1.9079 | Acc 52.41% | Grad 0.00155181\n","Ep 6 | Step 600 | Loss 1.7405 | Acc 51.50% | Grad 0.00163641\n","Ep 6 | Step 650 | Loss 1.7191 | Acc 52.77% | Grad 0.00154042\n","Ep 6 | Step 700 | Loss 1.8061 | Acc 51.27% | Grad 0.00140193\n","Ep 6 | Step 750 | Loss 1.6965 | Acc 52.38% | Grad 0.00150570\n","Ep 6 | Step 800 | Loss 1.9185 | Acc 51.84% | Grad 0.00168845\n","Ep 6 | Step 850 | Loss 1.6764 | Acc 51.81% | Grad 0.00135922\n","Ep 6 | Step 900 | Loss 1.6063 | Acc 52.36% | Grad 0.00166676\n","Ep 6 | Step 950 | Loss 1.6295 | Acc 53.00% | Grad 0.00138604\n","Ep 7 | Step 50 | Loss 1.7806 | Acc 54.80% | Grad 0.00120425\n","Ep 7 | Step 100 | Loss 1.9518 | Acc 54.09% | Grad 0.00146148\n","Ep 7 | Step 150 | Loss 1.9528 | Acc 54.66% | Grad 0.00162820\n","Ep 7 | Step 200 | Loss 1.8434 | Acc 53.81% | Grad 0.00131145\n","Ep 7 | Step 250 | Loss 1.8379 | Acc 54.39% | Grad 0.00171131\n","Ep 7 | Step 300 | Loss 1.4336 | Acc 54.27% | Grad 0.00158936\n","Ep 7 | Step 350 | Loss 1.7125 | Acc 53.12% | Grad 0.00141918\n","Ep 7 | Step 400 | Loss 1.9657 | Acc 54.17% | Grad 0.00141979\n","Ep 7 | Step 450 | Loss 1.9174 | Acc 53.72% | Grad 0.00161919\n","Ep 7 | Step 500 | Loss 1.6139 | Acc 54.72% | Grad 0.00157859\n","Ep 7 | Step 550 | Loss 2.0410 | Acc 53.88% | Grad 0.00155053\n","Ep 7 | Step 600 | Loss 1.7688 | Acc 54.34% | Grad 0.00155923\n","Ep 7 | Step 650 | Loss 1.6870 | Acc 52.95% | Grad 0.00197170\n","Ep 7 | Step 700 | Loss 1.7612 | Acc 54.64% | Grad 0.00161475\n","Ep 7 | Step 750 | Loss 1.6375 | Acc 53.56% | Grad 0.00136550\n","Ep 7 | Step 800 | Loss 1.5891 | Acc 54.83% | Grad 0.00132072\n","Ep 7 | Step 850 | Loss 1.7574 | Acc 53.27% | Grad 0.00166075\n","Ep 7 | Step 900 | Loss 2.0137 | Acc 52.78% | Grad 0.00163698\n","Ep 7 | Step 950 | Loss 1.5926 | Acc 53.14% | Grad 0.00142624\n","Ep 8 | Step 50 | Loss 1.6055 | Acc 55.77% | Grad 0.00156112\n","Ep 8 | Step 100 | Loss 1.5898 | Acc 56.42% | Grad 0.00152266\n","Ep 8 | Step 150 | Loss 1.6166 | Acc 55.73% | Grad 0.00156613\n","Ep 8 | Step 200 | Loss 1.6513 | Acc 56.17% | Grad 0.00149909\n","Ep 8 | Step 250 | Loss 1.8589 | Acc 55.80% | Grad 0.00141322\n","Ep 8 | Step 300 | Loss 1.3813 | Acc 56.16% | Grad 0.00151288\n","Ep 8 | Step 350 | Loss 1.6533 | Acc 55.25% | Grad 0.00163920\n","Ep 8 | Step 400 | Loss 1.3422 | Acc 56.62% | Grad 0.00141632\n","Ep 8 | Step 450 | Loss 1.7202 | Acc 55.83% | Grad 0.00146541\n","Ep 8 | Step 500 | Loss 1.5446 | Acc 55.44% | Grad 0.00151396\n","Ep 8 | Step 550 | Loss 1.5320 | Acc 55.80% | Grad 0.00080714\n","Ep 8 | Step 600 | Loss 1.7357 | Acc 55.62% | Grad 0.00151805\n","Ep 8 | Step 650 | Loss 1.9582 | Acc 55.73% | Grad 0.00149612\n","Ep 8 | Step 700 | Loss 1.5825 | Acc 54.30% | Grad 0.00158654\n","Ep 8 | Step 750 | Loss 1.6055 | Acc 56.09% | Grad 0.00163798\n","Ep 8 | Step 800 | Loss 1.7340 | Acc 55.34% | Grad 0.00157138\n","Ep 8 | Step 850 | Loss 1.7601 | Acc 55.61% | Grad 0.00136881\n","Ep 8 | Step 900 | Loss 1.3958 | Acc 55.81% | Grad 0.00162361\n","Ep 8 | Step 950 | Loss 1.5735 | Acc 57.09% | Grad 0.00142777\n","Ep 9 | Step 50 | Loss 1.5951 | Acc 57.50% | Grad 0.00159429\n","Ep 9 | Step 100 | Loss 1.7104 | Acc 58.62% | Grad 0.00169982\n","Ep 9 | Step 150 | Loss 1.8579 | Acc 58.00% | Grad 0.00157821\n","Ep 9 | Step 200 | Loss 1.5125 | Acc 58.05% | Grad 0.00161099\n","Ep 9 | Step 250 | Loss 1.6785 | Acc 57.45% | Grad 0.00131915\n","Ep 9 | Step 300 | Loss 1.5024 | Acc 57.88% | Grad 0.00158125\n","Ep 9 | Step 350 | Loss 1.5889 | Acc 57.56% | Grad 0.00156232\n","Ep 9 | Step 400 | Loss 1.4267 | Acc 56.66% | Grad 0.00164435\n","Ep 9 | Step 450 | Loss 1.4582 | Acc 57.80% | Grad 0.00165840\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_1011/502269081.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0mrunning_corrects\u001b[0m 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\u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    739\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    740\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 741\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    742\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    743\u001b[0m             if (\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1522\u001b[0m                     \u001b[0;34m\"Invalid iterator state: shutdown or no outstanding tasks when fetching next data\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1523\u001b[0m                 )\n\u001b[0;32m-> 1524\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1525\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1471\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1472\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_alive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1473\u001b[0;31m                 \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1474\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1475\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1308\u001b[0m         \u001b[0;31m#   (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1309\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1310\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;36m0.0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    357\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["import os\n","import io\n","import math\n","from PIL import Image\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.optim as optim\n","from torch.utils.data import Dataset, DataLoader\n","import torchvision.transforms as transforms\n","from datasets import load_dataset\n","from google.colab import drive\n","\n","# Mount Google Drive to access saved models\n","#drive.mount('/content/gdrive', force_remount=True)\n","\n","DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","BATCH_SIZE_TRAIN = 128\n","BATCH_SIZE_VAL = 32\n","EPOCHS = 7\n","LR = 1e-4\n","WEIGHT_DECAY = 1e-4\n","# Update MODEL_SAVE_PATH to point to the Google Drive location\n","MODEL_SAVE_PATH = \"/content/LookThem_V8_5_Stabilized.pth\"\n","\n","# --- TRANSFORMS ---\n","transform_train = transforms.Compose([\n","    transforms.Lambda(lambda img: img.convert(\"RGB\")),\n","    transforms.RandomResizedCrop((256, 256), scale=(0.8, 1.0)), # Stronger augmentation\n","    transforms.RandomHorizontalFlip(),\n","    transforms.RandomRotation(15), # Add random rotation\n","    transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1), # Add color jitter\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n","])\n","\n","transform_val = transforms.Compose([\n","    transforms.Lambda(lambda img: img.convert(\"RGB\")),\n","    transforms.Resize((256, 256)),\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n","])\n","\n","# --- DATASET LOADER ---\n","raw_train = load_dataset(\"clane9/imagenet-100\", split=\"train\")\n","raw_val = load_dataset(\"clane9/imagenet-100\", split=\"validation\")\n","\n","class ImageNet100ParquetDataset(Dataset):\n","    def __init__(self, hf_subset, transform=None):\n","        self.dataset = hf_subset\n","        self.transform = transform\n","    def __getitem__(self, index):\n","        row = self.dataset[index]\n","        img = row[\"image\"]\n","        if not isinstance(img, Image.Image):\n","            img = Image.open(io.BytesIO(img))\n","        if self.transform: img = self.transform(img)\n","        return img, row[\"label\"]\n","    def __len__(self):\n","        return len(self.dataset)\n","\n","train_loader = DataLoader(ImageNet100ParquetDataset(raw_train, transform_train), batch_size=BATCH_SIZE_TRAIN, shuffle=True, num_workers=2, pin_memory=True)\n","val_loader = DataLoader(ImageNet100ParquetDataset(raw_val, transform_val), batch_size=BATCH_SIZE_VAL, shuffle=False, num_workers=2, pin_memory=True)\n","\n","# --- STABILIZED LOOKTHEM LAYER ---\n","class LookThemLayer(nn.Module):\n","    def __init__(self, num_tokens, in_features, hidden_dim):\n","        super().__init__()\n","        self.num_tokens = num_tokens\n","        self.mod1_w1 = nn.Parameter(torch.randn(num_tokens, in_features, hidden_dim))\n","        self.mod1_b1 = nn.Parameter(torch.zeros(num_tokens, hidden_dim))\n","        self.mod1_w2 = nn.Parameter(torch.randn(num_tokens, hidden_dim, 1))\n","        self.mod1_b2 = nn.Parameter(torch.zeros(num_tokens, 1))\n","        self.mod2_w1 = nn.Parameter(torch.randn(num_tokens, in_features, hidden_dim))\n","        self.mod2_b1 = nn.Parameter(torch.zeros(num_tokens, hidden_dim))\n","        self.mod2_w2 = nn.Parameter(torch.randn(num_tokens, hidden_dim, 1))\n","        self.mod2_b2 = nn.Parameter(torch.zeros(num_tokens, 1))\n","        self.trans_w = nn.Parameter(torch.randn(num_tokens, 1, 1))\n","        self.trans_b = nn.Parameter(torch.zeros(num_tokens, 1))\n","        self._init_weights()\n","\n","    def _init_weights(self):\n","        for w in [self.mod1_w1, self.mod2_w1, self.mod1_w2, self.mod2_w2]:\n","            nn.init.xavier_uniform_(w)\n","\n","    def forward(self, x):\n","        N = self.num_tokens\n","        h1 = torch.einsum(\"bti,tij->btj\", x, self.mod1_w1) + self.mod1_b1\n","        out_m1 = torch.einsum(\"btj,tjk->btk\", F.gelu(h1), self.mod1_w2) + self.mod1_b2\n","        h2 = torch.einsum(\"bti,tij->btj\", x, self.mod2_w1) + self.mod2_b1\n","        out_m2 = torch.einsum(\"btj,tjk->btk\", F.gelu(h2), self.mod2_w2) + self.mod2_b2\n","\n","        out_m2_safe = torch.sign(out_m2) * torch.clamp(torch.abs(out_m2), min=1e-6)\n","        compare = torch.tanh(out_m1.unsqueeze(2) / out_m2_safe.unsqueeze(1))\n","        compare2 = torch.tanh(out_m1.unsqueeze(1) / out_m2_safe.unsqueeze(2))\n","\n","        trans_compare = torch.einsum(\"bije,jef->bijf\", compare, self.trans_w) + self.trans_b.view(1, 1, N, 1)\n","        trans_compare2 = torch.einsum(\"bije,jef->bijf\", compare2, self.trans_w) + self.trans_b.view(1, 1, N, 1)\n","\n","        interaksi = (trans_compare * x.unsqueeze(2) + trans_compare2 * x.unsqueeze(1)) / 2\n","        mask = (1.0 - torch.eye(N, device=x.device)).view(1, N, N, 1)\n","        return (interaksi * mask).sum(dim=2) / (N - 1.0)\n","\n","class LiteResidualBlock(nn.Module):\n","    def __init__(self, dim, dropout=0.05):\n","        super().__init__()\n","        self.block = nn.Sequential(nn.Linear(dim, dim), nn.GELU(), nn.Dropout(dropout), nn.Linear(dim, dim))\n","        self.norm = nn.LayerNorm(dim)\n","    def forward(self, x):\n","        return self.norm(x + self.block(x))\n","\n","class LookThemV8_5(nn.Module):\n","    def __init__(self):\n","        super().__init__()\n","        self.stream_a = nn.Sequential(nn.Conv2d(3, 16, 3, 2, 1), nn.BatchNorm2d(16), nn.GELU(), nn.Conv2d(16, 32, 3, 2, 1), nn.BatchNorm2d(32), nn.GELU(), nn.Conv2d(32, 64, 3, 2, 1), nn.BatchNorm2d(64), nn.GELU(), nn.AdaptiveMaxPool2d((8, 8)))\n","        self.stream_b = nn.Sequential(nn.Conv2d(3, 16, 3, 2, 1), nn.BatchNorm2d(16), nn.GELU(), nn.Conv2d(16, 32, 3, 1, 1), nn.BatchNorm2d(32), nn.GELU(), nn.Conv2d(32, 64, 3, 1, 1), nn.BatchNorm2d(64), nn.GELU(), nn.AdaptiveMaxPool2d((8, 8)))\n","\n","        self.lookthemA = LookThemLayer(64, 64, 32)\n","        self.lookthemB = LookThemLayer(64, 64, 32)\n","        self.lookthem_comb = LookThemLayer(64, 128, 32)\n","        self.comb_norm = nn.LayerNorm(128)\n","        self.FFN1 = nn.Conv1d(128, 64, 1)\n","\n","        self.lookthem2 = LookThemLayer(64, 64, 32)\n","        self.norm2 = nn.LayerNorm(64)\n","        self.FFN2 = nn.Conv1d(64, 64, 1)\n","\n","        self.lookthem3 = LookThemLayer(64, 64, 32)\n","        self.norm3 = nn.LayerNorm(64)\n","        self.FFN3 = nn.Conv1d(64, 64, 1)\n","\n","        self.compressor = nn.Conv1d(64, 16, 1)\n","        self.input_proj = nn.Linear(64 * 16, 256)\n","        self.res_blocks = nn.Sequential(LiteResidualBlock(256), LiteResidualBlock(256))\n","        self.head = nn.Sequential(nn.Linear(256, 128), nn.GELU(), nn.Linear(128, 100))\n","\n","    def forward(self, x):\n","        b = x.size(0)\n","        fa = self.lookthemA(self.stream_a(x).view(b, 64, 64).transpose(1, 2))\n","        fb = self.lookthemB(self.stream_b(x).view(b, 64, 64).transpose(1, 2))\n","        x = self.comb_norm(self.lookthem_comb(torch.cat([fa, fb], dim=2)))\n","        x = x.transpose(1, 2)\n","        x = self.FFN1(x).transpose(1, 2)\n","        res = x\n","        x = self.lookthem2(x).transpose(1, 2)\n","        x = self.FFN2(x) + res.transpose(1, 2)\n","        res = x\n","        x = self.lookthem3(x).transpose(1, 2)\n","        x = self.FFN3(x) + res.transpose(1, 2)\n","        x = self.compressor(x).flatten(1)\n","        x = self.res_blocks(self.input_proj(x))\n","        return self.head(x)\n","\n","model = LookThemV8_5().to(DEVICE)\n","if os.path.exists(MODEL_SAVE_PATH):\n","    print(f\"Loading model from {MODEL_SAVE_PATH}\")\n","    model.load_state_dict(torch.load(MODEL_SAVE_PATH))\n","else:\n","    print(f\"Model not found at {MODEL_SAVE_PATH}. Initializing with new weights.\")\n","\n","optimizer = optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n","criterion = nn.CrossEntropyLoss()\n","scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)\n","\n","print(f\"Model initialized. Total params: {sum(p.numel() for p in model.parameters()):,}\")\n","\n","for epoch in range(11, 11+EPOCHS):\n","    model.train()\n","    running_corrects = 0\n","    running_samples = 0\n","    for step, (data, target) in enumerate(train_loader):\n","        data, target = data.to(DEVICE), target.to(DEVICE)\n","        optimizer.zero_grad()\n","        output = model(data)\n","        loss = criterion(output, target)\n","        loss.backward()\n","        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n","        optimizer.step()\n","\n","        # Track accuracy\n","        _, preds = torch.max(output, 1)\n","        running_corrects += torch.sum(preds == target.data)\n","        running_samples += data.size(0)\n","\n","        if (step+1) % 50 == 0:\n","            grads = [p.grad.abs().mean().item() for p in model.parameters() if p.grad is not None]\n","            avg_grad = sum(grads)/len(grads) if grads else 0\n","            accuracy = 100.0 * running_corrects.double() / running_samples\n","            print(f\"Ep {epoch+1} | Step {step+1} | Loss {loss.item():.4f} | Acc {accuracy:.2f}% | Grad {avg_grad:.8f}\")\n","            # Optional: reset counters for interval-based accuracy\n","            running_corrects = 0\n","            running_samples = 0\n","\n","    scheduler.step()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["2e172de6df794646902f53e0340e99b9","faccb479080e4832aec08e7eaeef0011","2117413ae58840a0ae1902b6cab45838","5c873fd1e42446098eaee90389ac3ced","818e14c43b4f453582212e38ac7a8bff","ffd6eea6ea334ca98a8d39625c2e8473","94ff8910bacd4f6589f43df9172eb8a6","5b6d21c65536493aa0f2175ad6da0b1b","ac149615f6634671bcb7ad7b22c2cf6f","1dc5b1243d8a468895cc1212cedbe07a","01ae3fc002b84fcba07a4f51932621bd","9a194183dc0046fd860cb65d541b192f","42fee58dbfef4b0eaca22f1c14cf54a5","b1b82608ce88400f8f9408bf9019eaa2","82c354fb49b74397863d75c59338909f","003a098a32f741f9b08e6332f7f446e9","c3e19bc936c6400abd8cf9a02a50b07a","2c0590defcc8442eb9e9818a4196512a","47c0da78202345019b2aad699ebe1ba4","743c1ea7ba274773a214c52a0b12c07f","2bd1a5a94a664fef90a714633879cdb2","b0f98c21a3cd41d198058a4a43f0314b","4e89f3d7c77b4be5a12157fa76cd58ae","ee20addc5b2c4a349562fcc8933afb6b","41c55dc832eb4da68a8a215c481ad823","7898a2fcf37b4ea382600c201dbc1bda","975d0fc1a64d4e888877cff4d8a976b5","a26a65b3041a4640a16add8974a05476","c13c8747149b4988a9b2532c4240d19e","5f24fc6040e04ca883c4fcf918aff9c3","200d0e84da59422eba2975801db12b19","82ddee53652d4f669936cd4fbfa1cc5d","de77640f3c0340f5aa490032a69cc829","7ab37cbbdfcb4720b4d93f80ed4c9d46","8a0eadff3211443c8038617189400f6a","c5518f12a84c461491f6629b50f599f7","61d8c8cc8b7d41a9b95cebb792692c1d","c47d04f256e740449b5afb2ba9047bcc","694fdf5b21cb497aa4a09c9a8a28c265","31bf4b1313a24d1f9615d06a95398eb9","3463a696a37e4768a6b5bc2179db7942","9885518498af41b6afe9fa5fdae7e9f4","44372bb118654b979405e487360fbd1a","95401f2a2d9248e488abd8b2b11fe552","2574df034fe04487a62807ebae5aa4ef","1eb2f576fe5846fe9dd59a00094349a0","10d8e5e9f99d4be7b65e4b6b1f627561","e6fb708bf2664af5817ebb7898406127","b1ac1a132ce24f81883353cf5c6fc439","e99813e078d1484caaf282f27819e1dc","4eb7f8e947d741b5be6729fcc1a11e5b","64643e51eb5b4cd2a03174cabadcb14f","c72a6ef1b30142d4b1f8c10929e33c65","33a70e3f7e4441749a7386c3f04cbfb4","d10fa1ca2c754cfbaa024ad415a1549c","0228b14ff48c472889d6733d9225efd0","372201183ac743eda0928ba3caf7d4aa","54c0490367414688b0735db3a4ef67b3","426d320913de4a2b992d60d53e9bf9d5","f16e5a9297524888a33ac1c02bd9e35f","e57e030787314cdaa9abbbd1eeb3d1f1","3d8086267c8b44d9ab3f1bd713d4499e","6d4b2869c71646988e56c2fb4a8cdefa","fffdc4a121e94e05bc991cc9a2617a96","4d94d141076c4ff7b2c94325f90a8a66","2eab328f45b24904ae73053bd04d42dd","3cfd79422418447080e1cdc858d282a5","8a9ae370e6524ae2b7391acd54c627ec","a312a858ae2d4295a522a93461611c1e","29cf266b8ecb4959a253e5ba9233b827","fa10a8acdfe340669a3583ece2a3a63f","c41ce5799f4940fb85ee040f2fdf5ecc","c1e0c268aae847449a80d6f572ba4af6","9c886ba6243e451eb825099c175a197f","ae74b6bc1ddf45a39240224f2193a7b1","209e42d12faf43498bd7e02ccde63b2d","c1ab4b3fb844487ba93dfb186e50c708","a20346db457d41899522342fe5dc80ec","84e162a023384e1c87500e4ac6e32e55","183ca753c32947a08d060a334f9ee181","0365978fc53e48049fa28701114ffa5e","9ead83f44dcb419984e077c73919c5bf","5011c55cc08e441ca35988dae3c16a33","785150c74b4146d4b1479cdc721d655d","9d34e56321d84dda98b401f25053ba52","a579d7966b3c4b629e2f0f8e4a418459","1ade9957e6b0445da11fd9880467748d","99909c735280448fba4c0d857dbf353a","01913762760941639de1c8a3dd95e1bf","d4af07691fb14ed084d82a8129ca05ff","b41300e5d78b4237a229d24a0cbac891","f2c7b8cff1e84820b4e340e0393e7bfe","a1f3da81c52f4dc0ad4ef98fff2cfff1","c05247c3715c4921bb369917ed6782d7","63fc83e9b12c469aa7ebf6436f180c05","a1815851b4f542e491cf3cf6617ff1dc","add32ab225bb4a0eb0ef7cce9253964c","e2869ae44bd646df9e73f193026108a3","56e382b69b6e4fd1bb7935e491e9f75d","57f22d984d774e0da071bdeaef091b33","7d6282a1a2c44b74bfd1335f38eb5b6c","22443bc17a07466fbc27338a296986bc","302d2d9576744503a01c6fdc3588439d","4e92352a5ac84d078a730144084adacb","62996309cb4a4244951b5b5b92038dbf","713acf63709347b39197a783407389a5","0ba7bd66dab54f82b2f9ac1d9d97062d","1b68846ee5b641f5b6c2d04815ecbf61","e474cde23df94b5da5f164e356f74985","6b08b81ec5a44e7cb4dacd5d3014ee0e","16746af0c6f44b14a3eb80f9049d9adb","62feabb224a04b30bf494ebab6b5f5ea","c317f7e6e19b4e92bfa618bfac993ecb","bba0008ec680471dbff515e2cbf63d43","b729f8dee99b4ce4935905bb05ed1f9c","b9c5bc9b2f3546178c543e353774ebb5","e9d7071082fe48239063dab24c0eb104","c9b8b767c410469aace8efd76d5157a5","1c978dd101344cf09e8d52463d13371b","c974b526ff26442297932b8b1387c4b7","1d1d8347a44a4041af7574f41a6f121c","3fa3fb4b91834789b8bd4e9c55de4c7f","c6e5a323d375493087cf31c4b63dbe95","04697914294c4613acfaeb1e631dda66","95cd74d74e624790822c12cf30359d2f","e33892f624cf48fa9f443fcf77665677","32264e946bf44c85ae8ab9d5bd71b54d","9d320b9943664b28b3824746ed5f1c2c","4e217f97b2ee4f20af30bf093b43e170","24664bb68d414cca9a0002885bdaa785","0e727d88db8c430ba851f36623ab286a","d485d1cc5922411e93f3d15ccd94866e","e0d18bac0eaa4b60891d9377e39d3eb0","1a1267cde4534a1aae4ef28992011f61","f5af8fd285234a5db901e0e46fc6e8bf","b35cb8a3f02c4c4c8e3bd21ca6524cd1","0db0b6a4a85c436fa581d770d833328e","5562f1a4458c43a2b9dc0a0503bdb795","4fac37d1d5574d67b76e49cf27d15390","aef045adc3ba4812bf7f3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rizky plk","userId":"03714270658772765776"}}},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:112: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2e172de6df794646902f53e0340e99b9","version_major":2,"version_minor":0},"text/plain":["README.md:   0%|          | 0.00/7.87k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n","WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9a194183dc0046fd860cb65d541b192f","version_major":2,"version_minor":0},"text/plain":["Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4e89f3d7c77b4be5a12157fa76cd58ae","version_major":2,"version_minor":0},"text/plain":["Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7ab37cbbdfcb4720b4d93f80ed4c9d46","version_major":2,"version_minor":0},"text/plain":["Downloading data:   0%|          | 0/17 [00:00<?, ?files/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2574df034fe04487a62807ebae5aa4ef","version_major":2,"version_minor":0},"text/plain":["data/train-00000-of-00017.parquet:   0%|          | 0.00/505M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0228b14ff48c472889d6733d9225efd0","version_major":2,"version_minor":0},"text/plain":["data/train-00001-of-00017.parquet:   0%|          | 0.00/469M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3cfd79422418447080e1cdc858d282a5","version_major":2,"version_minor":0},"text/plain":["data/train-00002-of-00017.parquet:   0%|          | 0.00/471M [00:00<?, 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?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"16746af0c6f44b14a3eb80f9049d9adb","version_major":2,"version_minor":0},"text/plain":["data/train-00006-of-00017.parquet:   0%|          | 0.00/522M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3fa3fb4b91834789b8bd4e9c55de4c7f","version_major":2,"version_minor":0},"text/plain":["data/train-00007-of-00017.parquet:   0%|          | 0.00/429M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e0d18bac0eaa4b60891d9377e39d3eb0","version_major":2,"version_minor":0},"text/plain":["data/train-00008-of-00017.parquet:   0%|          | 0.00/474M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"05f6b8073b514161a92404ec6d4a448f","version_major":2,"version_minor":0},"text/plain":["data/train-00009-of-00017.parquet:   0%|          | 0.00/473M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0f44b5123a1041cdb9e82a558d38292f","version_major":2,"version_minor":0},"text/plain":["data/train-00010-of-00017.parquet:   0%|          | 0.00/451M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"48bccfa2e9404d1ebad92da748d7dff8","version_major":2,"version_minor":0},"text/plain":["data/train-00011-of-00017.parquet:   0%|          | 0.00/508M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6c7dacac5dac400495b0640e73360d5b","version_major":2,"version_minor":0},"text/plain":["data/train-00012-of-00017.parquet:   0%|          | 0.00/468M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"addb195d923f41b78d96c96b2bd1ade8","version_major":2,"version_minor":0},"text/plain":["data/train-00013-of-00017.parquet:   0%|          | 0.00/457M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"266e1d9bff214dfdb234e5d4963ff2c9","version_major":2,"version_minor":0},"text/plain":["data/train-00014-of-00017.parquet:   0%|          | 0.00/444M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a95ff9bf10504072800db972c8504774","version_major":2,"version_minor":0},"text/plain":["data/train-00015-of-00017.parquet:   0%|          | 0.00/454M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8782523616e94289afef4384fa8ed932","version_major":2,"version_minor":0},"text/plain":["data/train-00016-of-00017.parquet:   0%|          | 0.00/488M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2a9c774404f44819b48b2544f1ee1d42","version_major":2,"version_minor":0},"text/plain":["data/validation-00000-of-00001.parquet:   0%|          | 0.00/314M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"abdeeb8ce444422781d0db6d4fa7639a","version_major":2,"version_minor":0},"text/plain":["Generating train split:   0%|          | 0/126689 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"43913edc7a3443ea92b59974d33c0322","version_major":2,"version_minor":0},"text/plain":["Generating validation split:   0%|          | 0/5000 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c52d4ece25d840fe82e3c774b0620859","version_major":2,"version_minor":0},"text/plain":["Loading dataset shards:   0%|          | 0/17 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b5b0a074b6d044a3ac5ac8c741b62688","version_major":2,"version_minor":0},"text/plain":["Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fb08653cf3f642c8b9cc54acf795a5e1","version_major":2,"version_minor":0},"text/plain":["Resolving data files:   0%|          | 0/17 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"output_type":"stream","name":"stdout","text":["Loading model from /content/LookThem_V8_5_Stabilized.pth\n","Model initialized. Total params: 2,253,236\n","Ep 12 | Step 50 | Loss 1.2019 | Acc 59.84% | Grad 0.00177505\n","Ep 12 | Step 100 | Loss 1.6276 | Acc 60.75% | Grad 0.00160901\n","Ep 12 | Step 150 | Loss 1.4217 | Acc 60.20% | Grad 0.00140864\n","Ep 12 | Step 200 | Loss 1.4022 | Acc 59.53% | Grad 0.00141251\n","Ep 12 | Step 250 | Loss 1.3600 | Acc 60.19% | Grad 0.00139130\n","Ep 12 | Step 300 | Loss 1.6829 | Acc 59.75% | Grad 0.00131966\n","Ep 12 | Step 350 | Loss 1.4175 | Acc 61.23% | Grad 0.00148325\n","Ep 12 | Step 400 | Loss 1.4448 | Acc 60.19% | Grad 0.00153516\n","Ep 12 | Step 450 | Loss 1.2682 | Acc 60.92% | Grad 0.00162545\n","Ep 12 | Step 500 | Loss 1.5443 | Acc 59.84% | Grad 0.00152006\n","Ep 12 | Step 550 | Loss 1.5067 | Acc 60.55% | Grad 0.00141200\n","Ep 12 | Step 600 | Loss 1.5772 | Acc 60.75% | Grad 0.00168931\n","Ep 12 | Step 650 | Loss 1.7800 | Acc 60.36% | Grad 0.00172059\n","Ep 12 | Step 700 | Loss 1.8566 | Acc 60.25% | Grad 0.00164862\n","Ep 12 | Step 750 | Loss 1.4830 | Acc 61.30% | Grad 0.00134161\n","Ep 12 | Step 800 | Loss 1.4473 | Acc 60.92% | Grad 0.00169913\n","Ep 12 | Step 850 | Loss 1.6256 | Acc 60.12% | Grad 0.00179967\n","Ep 12 | Step 900 | Loss 1.6671 | Acc 59.62% | Grad 0.00124813\n","Ep 12 | Step 950 | Loss 1.6733 | Acc 59.61% | Grad 0.00149906\n","Ep 13 | Step 50 | Loss 1.3751 | Acc 61.64% | Grad 0.00156028\n","Ep 13 | Step 100 | Loss 1.5066 | Acc 61.02% | Grad 0.00151823\n","Ep 13 | Step 150 | Loss 1.2924 | Acc 60.95% | Grad 0.00144638\n","Ep 13 | Step 200 | Loss 1.2648 | Acc 61.66% | Grad 0.00150706\n","Ep 13 | Step 250 | Loss 1.3301 | Acc 61.61% | Grad 0.00159585\n","Ep 13 | Step 300 | Loss 1.7244 | Acc 60.83% | Grad 0.00143962\n","Ep 13 | Step 350 | Loss 1.4759 | Acc 61.94% | Grad 0.00159381\n","Ep 13 | Step 400 | Loss 1.4092 | Acc 60.20% | Grad 0.00132829\n","Ep 13 | Step 450 | Loss 1.4160 | Acc 60.53% | Grad 0.00141845\n","Ep 13 | Step 500 | Loss 1.3135 | Acc 61.94% | Grad 0.00161497\n","Ep 13 | Step 550 | Loss 1.4883 | Acc 61.34% | Grad 0.00154499\n","Ep 13 | Step 600 | Loss 1.6846 | Acc 60.23% | Grad 0.00126357\n","Ep 13 | Step 650 | Loss 1.4160 | Acc 60.72% | Grad 0.00158862\n","Ep 13 | Step 700 | Loss 1.1908 | Acc 61.33% | Grad 0.00163351\n","Ep 13 | Step 750 | Loss 1.3892 | Acc 61.44% | Grad 0.00137799\n","Ep 13 | Step 800 | Loss 1.2880 | Acc 61.61% | Grad 0.00167802\n","Ep 13 | Step 850 | Loss 1.5131 | Acc 60.62% | Grad 0.00145065\n","Ep 13 | Step 900 | Loss 1.5407 | Acc 60.73% | Grad 0.00169354\n","Ep 13 | Step 950 | Loss 1.4273 | Acc 61.06% | Grad 0.00161808\n","Ep 14 | Step 50 | Loss 1.2097 | Acc 60.58% | Grad 0.00148890\n","Ep 14 | Step 100 | Loss 1.5309 | Acc 61.08% | Grad 0.00139597\n","Ep 14 | Step 150 | Loss 1.5237 | Acc 62.20% | Grad 0.00144591\n","Ep 14 | Step 200 | Loss 1.4018 | Acc 61.25% | Grad 0.00145921\n","Ep 14 | Step 250 | Loss 1.5174 | Acc 61.16% | Grad 0.00139352\n","Ep 14 | Step 300 | Loss 1.4696 | Acc 60.70% | Grad 0.00163637\n","Ep 14 | Step 350 | Loss 1.3393 | Acc 62.17% | Grad 0.00152342\n","Ep 14 | Step 400 | Loss 1.3383 | Acc 62.31% | Grad 0.00152580\n","Ep 14 | Step 450 | Loss 1.4457 | Acc 62.03% | Grad 0.00155242\n","Ep 14 | Step 500 | Loss 1.6969 | Acc 61.53% | Grad 0.00149811\n","Ep 14 | Step 550 | Loss 1.4988 | Acc 61.30% | Grad 0.00137513\n","Ep 14 | Step 600 | Loss 1.4308 | Acc 61.38% | Grad 0.00171842\n","Ep 14 | Step 650 | Loss 1.0787 | Acc 62.92% | Grad 0.00144659\n","Ep 14 | Step 700 | Loss 1.3870 | Acc 61.89% | Grad 0.00139586\n","Ep 14 | Step 750 | Loss 1.1370 | Acc 61.33% | Grad 0.00149201\n","Ep 14 | Step 800 | Loss 1.4694 | Acc 61.95% | Grad 0.00149416\n","Ep 14 | Step 850 | Loss 1.4162 | Acc 61.58% | Grad 0.00163227\n","Ep 14 | Step 900 | Loss 1.2771 | Acc 62.25% | Grad 0.00157086\n","Ep 14 | Step 950 | Loss 1.5309 | Acc 61.73% | Grad 0.00161700\n","Ep 15 | Step 50 | Loss 1.5193 | Acc 62.86% | Grad 0.00154542\n","Ep 15 | Step 100 | Loss 1.2178 | Acc 62.42% | Grad 0.00178698\n","Ep 15 | Step 150 | Loss 1.4496 | Acc 63.12% | Grad 0.00161806\n","Ep 15 | Step 200 | Loss 1.2970 | Acc 63.23% | Grad 0.00139730\n","Ep 15 | Step 250 | Loss 1.4667 | Acc 62.75% | Grad 0.00148607\n","Ep 15 | Step 300 | Loss 1.6167 | Acc 61.64% | Grad 0.00157411\n","Ep 15 | Step 350 | Loss 1.2910 | Acc 62.16% | Grad 0.00140636\n","Ep 15 | Step 400 | Loss 1.0153 | Acc 62.44% | Grad 0.00169266\n","Ep 15 | Step 450 | Loss 1.3792 | Acc 61.69% | Grad 0.00182977\n","Ep 15 | Step 500 | Loss 1.4484 | Acc 62.09% | Grad 0.00129288\n","Ep 15 | Step 550 | Loss 1.4808 | Acc 63.88% | Grad 0.00133402\n","Ep 15 | Step 600 | Loss 1.2684 | Acc 63.34% | Grad 0.00155531\n","Ep 15 | Step 650 | Loss 1.3922 | Acc 61.84% | Grad 0.00134539\n","Ep 15 | Step 700 | Loss 1.2980 | Acc 62.02% | Grad 0.00148782\n","Ep 15 | Step 750 | Loss 1.5170 | Acc 60.75% | Grad 0.00188433\n","Ep 15 | Step 800 | Loss 1.2945 | Acc 60.75% | Grad 0.00153120\n","Ep 15 | Step 850 | Loss 1.4413 | Acc 61.41% | Grad 0.00161327\n","Ep 15 | Step 900 | Loss 1.4650 | Acc 61.44% | Grad 0.00151224\n","Ep 15 | Step 950 | Loss 1.5822 | Acc 61.75% | Grad 0.00145301\n","Ep 16 | Step 50 | Loss 1.1666 | Acc 62.77% | Grad 0.00162405\n","Ep 16 | Step 100 | Loss 1.6808 | Acc 62.45% | Grad 0.00141377\n","Ep 16 | Step 150 | Loss 1.3121 | Acc 62.38% | Grad 0.00146225\n","Ep 16 | Step 200 | Loss 1.2442 | Acc 63.05% | Grad 0.00148629\n","Ep 16 | Step 250 | Loss 1.4774 | Acc 62.55% | Grad 0.00158767\n","Ep 16 | Step 300 | Loss 1.3240 | Acc 62.73% | Grad 0.00186658\n","Ep 16 | Step 350 | Loss 1.4689 | Acc 62.39% | Grad 0.00159918\n","Ep 16 | Step 400 | Loss 1.1350 | Acc 62.12% | Grad 0.00139615\n","Ep 16 | Step 450 | Loss 1.3013 | Acc 62.28% | Grad 0.00160390\n","Ep 16 | Step 500 | Loss 1.3788 | Acc 62.86% | Grad 0.00161834\n","Ep 16 | Step 550 | Loss 1.4534 | Acc 61.83% | Grad 0.00126823\n","Ep 16 | Step 600 | Loss 1.2970 | Acc 62.34% | Grad 0.00134765\n","Ep 16 | Step 650 | Loss 1.2864 | Acc 62.70% | Grad 0.00158940\n","Ep 16 | Step 700 | Loss 1.2813 | Acc 62.62% | Grad 0.00177550\n","Ep 16 | Step 750 | Loss 1.4638 | Acc 62.47% | Grad 0.00129832\n","Ep 16 | Step 800 | Loss 1.4987 | Acc 63.28% | Grad 0.00161794\n","Ep 16 | Step 850 | Loss 1.2979 | Acc 63.44% | Grad 0.00142128\n","Ep 16 | Step 900 | Loss 1.3321 | Acc 62.05% | Grad 0.00137223\n","Ep 16 | Step 950 | Loss 1.3040 | Acc 62.44% | Grad 0.00134109\n","Ep 17 | Step 50 | Loss 1.3874 | Acc 62.22% | Grad 0.00145777\n","Ep 17 | Step 100 | Loss 1.4148 | Acc 63.25% | Grad 0.00156809\n","Ep 17 | Step 150 | Loss 1.7662 | Acc 62.52% | Grad 0.00163314\n","Ep 17 | Step 200 | Loss 1.4678 | Acc 62.72% | Grad 0.00164388\n","Ep 17 | Step 250 | Loss 1.3380 | Acc 63.95% | Grad 0.00142753\n","Ep 17 | Step 300 | Loss 1.8695 | Acc 61.48% | Grad 0.00164751\n","Ep 17 | Step 350 | Loss 1.2940 | Acc 63.67% | Grad 0.00139682\n","Ep 17 | Step 400 | Loss 1.4334 | Acc 62.44% | Grad 0.00165871\n","Ep 17 | Step 450 | Loss 1.2702 | Acc 62.95% | Grad 0.00144201\n","Ep 17 | Step 500 | Loss 1.1709 | Acc 63.59% | Grad 0.00132672\n","Ep 17 | Step 550 | Loss 1.4136 | Acc 62.80% | Grad 0.00158207\n","Ep 17 | Step 600 | Loss 1.3518 | Acc 62.61% | Grad 0.00160226\n","Ep 17 | Step 650 | Loss 1.0169 | Acc 63.17% | Grad 0.00163005\n","Ep 17 | Step 700 | Loss 1.3642 | Acc 62.22% | Grad 0.00139881\n","Ep 17 | Step 750 | Loss 1.4291 | Acc 63.86% | Grad 0.00155034\n","Ep 17 | Step 800 | Loss 1.5460 | Acc 63.22% | Grad 0.00176997\n","Ep 17 | Step 850 | Loss 1.1355 | Acc 63.45% | Grad 0.00132325\n","Ep 17 | Step 900 | Loss 1.4423 | Acc 62.27% | Grad 0.00170866\n","Ep 17 | Step 950 | Loss 1.2832 | Acc 63.34% | Grad 0.00153062\n","Ep 18 | Step 50 | Loss 1.2984 | Acc 63.31% | Grad 0.00153258\n","Ep 18 | Step 100 | Loss 1.3380 | Acc 62.98% | Grad 0.00161119\n","Ep 18 | Step 150 | Loss 1.3789 | Acc 62.83% | Grad 0.00099703\n","Ep 18 | Step 200 | Loss 1.3688 | Acc 63.61% | Grad 0.00156920\n","Ep 18 | Step 250 | Loss 1.2239 | Acc 62.42% | Grad 0.00165773\n","Ep 18 | Step 300 | Loss 1.1595 | Acc 62.91% | Grad 0.00165344\n","Ep 18 | Step 350 | Loss 1.3593 | Acc 63.52% | Grad 0.00163645\n","Ep 18 | Step 400 | Loss 1.3179 | Acc 64.00% | Grad 0.00143758\n","Ep 18 | Step 450 | Loss 1.4150 | Acc 62.31% | Grad 0.00145908\n","Ep 18 | Step 500 | Loss 1.1715 | Acc 63.72% | Grad 0.00144717\n","Ep 18 | Step 550 | Loss 1.3479 | Acc 63.14% | Grad 0.00184566\n","Ep 18 | Step 600 | Loss 1.1392 | Acc 63.02% | Grad 0.00141474\n","Ep 18 | Step 650 | Loss 1.2843 | Acc 63.91% | Grad 0.00131280\n","Ep 18 | Step 700 | Loss 1.1929 | Acc 62.62% | Grad 0.00153361\n","Ep 18 | Step 750 | Loss 1.4540 | Acc 62.83% | Grad 0.00138610\n","Ep 18 | Step 800 | Loss 1.5392 | Acc 64.19% | Grad 0.00164233\n","Ep 18 | Step 850 | Loss 1.3504 | Acc 63.45% | Grad 0.00142007\n","Ep 18 | Step 900 | Loss 1.2245 | Acc 63.69% | Grad 0.00141001\n","Ep 18 | Step 950 | Loss 1.2351 | Acc 64.06% | Grad 0.00155552\n"]}]},{"cell_type":"code","source":["model.eval()\n","val_loss = 0\n","correct = 0\n","total = 0\n","with torch.no_grad():\n","    for data, target in val_loader:\n","        data, target = data.to(DEVICE), target.to(DEVICE)\n","        output = model(data)\n","        val_loss += criterion(output, target).item()\n","        _, predicted = torch.max(output.data, 1)\n","        total += target.size(0)\n","        correct += (predicted == target).sum().item()\n","\n","val_loss /= len(val_loader)\n","accuracy = 100 * correct / total\n","\n","print(f'\\nValidation set: Average loss: {val_loss:.4f}, Accuracy: {correct}/{total} ({accuracy:.2f}%)\\n')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e1tu95kg0uux","executionInfo":{"status":"ok","timestamp":1780210722985,"user_tz":-420,"elapsed":19752,"user":{"displayName":"Cici rizky plk","userId":"03714270658772765776"}},"outputId":"67a65358-23a1-4f31-aab6-230ae426c69f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Validation set: Average loss: 1.7702, Accuracy: 2674/5000 (53.48%)\n","\n"]}]},{"cell_type":"code","source":["import torch\n","from google.colab import files\n","\n","# Assuming MODEL_SAVE_PATH is already defined and points to a local path\n","torch.save(model.state_dict(), MODEL_SAVE_PATH)\n","print(f\"Model saved locally to {MODEL_SAVE_PATH}. Preparing for download...\")\n","files.download(MODEL_SAVE_PATH)\n","print(\"Model download initiated.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":72},"id":"5nUbJmg31Iiz","executionInfo":{"status":"ok","timestamp":1780212881630,"user_tz":-420,"elapsed":262,"user":{"displayName":"Cici rizky plk","userId":"03714270658772765776"}},"outputId":"4e1e6d7d-5d31-4e12-8e2f-1b12241cf9e2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model saved locally to /content/LookThem_V8_5_Stabilized.pth. Preparing for download...\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","    async function download(id, filename, size) {\n","      if (!google.colab.kernel.accessAllowed) {\n","        return;\n","      }\n","      const div = document.createElement('div');\n","      const label = document.createElement('label');\n","      label.textContent = `Downloading \"${filename}\": `;\n","      div.appendChild(label);\n","      const progress = document.createElement('progress');\n","      progress.max = size;\n","      div.appendChild(progress);\n","      document.body.appendChild(div);\n","\n","      const buffers = [];\n","      let downloaded = 0;\n","\n","      const channel = await google.colab.kernel.comms.open(id);\n","      // Send a message to notify the kernel that we're ready.\n","      channel.send({})\n","\n","      for await (const message of channel.messages) {\n","        // Send a message to notify the kernel that we're ready.\n","        channel.send({})\n","        if (message.buffers) {\n","          for (const buffer of message.buffers) {\n","            buffers.push(buffer);\n","            downloaded += buffer.byteLength;\n","            progress.value = downloaded;\n","          }\n","        }\n","      }\n","      const blob = new Blob(buffers, {type: 'application/binary'});\n","      const a = document.createElement('a');\n","      a.href = window.URL.createObjectURL(blob);\n","      a.download = filename;\n","      div.appendChild(a);\n","      a.click();\n","      div.remove();\n","    }\n","  "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["download(\"download_9b4a1651-6f1e-4b25-a5aa-7e7863809f2a\", \"LookThem_V8_5_Stabilized.pth\", 9057577)"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Model download initiated.\n"]}]},{"cell_type":"code","source":["print(\"\\n--- Preparing for Classifier-only Training ---\\n\")\n","\n","# 1. Freeze all layers initially\n","for param in model.parameters():\n","    param.requires_grad = False\n","\n","# 2. Unfreeze only the classifier layers (input_proj, res_blocks, head)\n","# Based on the model definition, the classifier starts after the compressor.\n","# The input_proj, res_blocks, and head are the parts to train.\n","for param in model.input_proj.parameters():\n","    param.requires_grad = True\n","for param in model.res_blocks.parameters():\n","    param.requires_grad = True\n","for param in model.head.parameters():\n","    param.requires_grad = True\n","\n","print(\"Model layers frozen except for input_proj, res_blocks, and head.\")\n","\n","# Verify which parameters are trainable\n","trainable_params = [name for name, param in model.named_parameters() if param.requires_grad]\n","print(f\"Trainable parameters: {trainable_params}\")\n","\n","# 3. Precompute features\n","class PrecomputedFeatureDataset(Dataset):\n","    def __init__(self, features, labels):\n","        self.features = features\n","        self.labels = labels\n","\n","    def __len__(self):\n","        return len(self.labels)\n","\n","    def __getitem__(self, idx):\n","        return self.features[idx], self.labels[idx]\n","\n","model.eval() # Set model to evaluation mode for feature extraction\n","\n","def precompute_features(dataloader, device):\n","    all_features = []\n","    all_labels = []\n","    with torch.no_grad():\n","        for i, (data, target) in enumerate(dataloader):\n","            data, target = data.to(device), target.to(device)\n","            b = data.size(0)\n","\n","            # Forward pass up to the compressor layer\n","            fa = model.lookthemA(model.stream_a(data).view(b, 64, 64).transpose(1, 2))\n","            fb = model.lookthemB(model.stream_b(data).view(b, 64, 64).transpose(1, 2))\n","            x = model.comb_norm(model.lookthem_comb(torch.cat([fa, fb], dim=2)))\n","            x = x.transpose(1, 2)\n","            x = model.FFN1(x).transpose(1, 2)\n","            res = x\n","            x = model.lookthem2(x).transpose(1, 2)\n","            x = model.FFN2(x) + res.transpose(1, 2)\n","            res = x\n","            x = model.lookthem3(x).transpose(1, 2)\n","            x = model.FFN3(x) + res.transpose(1, 2)\n","            features = model.compressor(x).flatten(1) # Output of feature extractor\n","\n","            all_features.append(features.cpu())\n","            all_labels.append(target.cpu())\n","            if (i+1) % 100 == 0:\n","                print(f\"Precomputing batch {i+1}/{len(dataloader)}\")\n","\n","    return torch.cat(all_features), torch.cat(all_labels)\n","\n","print(\"Precomputing training features...\")\n","train_features, train_labels = precompute_features(train_loader, DEVICE)\n","print(f\"Training features shape: {train_features.shape}, labels shape: {train_labels.shape}\")\n","\n","print(\"Precomputing validation features...\")\n","val_features, val_labels = precompute_features(val_loader, DEVICE)\n","print(f\"Validation features shape: {val_features.shape}, labels shape: {val_labels.shape}\")\n","\n","# Create new DataLoaders for the classifier training\n","precomputed_train_dataset = PrecomputedFeatureDataset(train_features, train_labels)\n","precomputed_val_dataset = PrecomputedFeatureDataset(val_features, val_labels)\n","\n","precomputed_train_loader = DataLoader(precomputed_train_dataset, batch_size=BATCH_SIZE_TRAIN, shuffle=True, pin_memory=True)\n","precomputed_val_loader = DataLoader(precomputed_val_dataset, batch_size=BATCH_SIZE_VAL, shuffle=False, pin_memory=True)\n","\n","print(\"New DataLoaders created with precomputed features.\")\n","\n","# 4. Define new optimizer for only the trainable parameters\n","optimizer_classifier = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=LR, weight_decay=WEIGHT_DECAY)\n","\n","# 5. Modified training loop for classifier\n","print(\"\\n--- Starting Classifier Training ---\\n\")\n","\n","# Define new EPOCHS for this classifier training if desired, or reuse global EPOCHS\n","CLASSIFIER_EPOCHS = 10 # Example: you can adjust this\n","scheduler_classifier = optim.lr_scheduler.CosineAnnealingLR(optimizer_classifier, T_max=CLASSIFIER_EPOCHS)\n","\n","# Re-initialize lists for plotting if you want to plot this separate training phase\n","classifier_train_losses = []\n","classifier_train_accuracies = []\n","classifier_val_losses = []\n","classifier_val_accuracies = []\n","\n","for epoch in range(CLASSIFIER_EPOCHS):\n","    model.train() # Set model to train mode (only unfrozen layers will update)\n","    running_loss = 0.0\n","    running_corrects = 0\n","    running_samples = 0\n","\n","    for step, (features, target) in enumerate(precomputed_train_loader):\n","        features, target = features.to(DEVICE), target.to(DEVICE)\n","        optimizer_classifier.zero_grad()\n","\n","        # Forward pass through only the classifier part\n","        x = model.input_proj(features)\n","        x = model.res_blocks(x)\n","        output = model.head(x)\n","\n","        loss = criterion(output, target)\n","        loss.backward()\n","        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n","        optimizer_classifier.step()\n","\n","        running_loss += loss.item() * features.size(0)\n","        _, preds = torch.max(output, 1)\n","        running_corrects += torch.sum(preds == target.data)\n","        running_samples += features.size(0)\n","\n","        if (step+1) % 100 == 0:\n","            print(f\"Classifier Ep {epoch+1} | Step {step+1}/{len(precomputed_train_loader)} | Loss {loss.item():.4f}\")\n","\n","    epoch_train_loss = running_loss / running_samples\n","    epoch_train_accuracy = 100.0 * running_corrects.double() / running_samples\n","    classifier_train_losses.append(epoch_train_loss)\n","    classifier_train_accuracies.append(epoch_train_accuracy.item())\n","\n","    # Validation step\n","    model.eval()\n","    val_running_loss = 0.0\n","    val_running_corrects = 0\n","    val_running_samples = 0\n","    with torch.no_grad():\n","        for features, target in precomputed_val_loader:\n","            features, target = features.to(DEVICE), target.to(DEVICE)\n","\n","            x = model.input_proj(features)\n","            x = model.res_blocks(x)\n","            output = model.head(x)\n","\n","            loss = criterion(output, target)\n","            val_running_loss += loss.item() * features.size(0)\n","            _, preds = torch.max(output, 1)\n","            val_running_corrects += torch.sum(preds == target.data)\n","            val_running_samples += features.size(0)\n","\n","    epoch_val_loss = val_running_loss / val_running_samples\n","    epoch_val_accuracy = 100.0 * val_running_corrects.double() / val_running_samples\n","    classifier_val_losses.append(epoch_val_loss)\n","    classifier_val_accuracies.append(epoch_val_accuracy.item())\n","\n","    print(f\"Epoch {epoch+1} Classifier Training Complete. Train Loss: {epoch_train_loss:.4f}, Train Acc: {epoch_train_accuracy:.2f}% | Val Loss: {epoch_val_loss:.4f}, Val Acc: {epoch_val_accuracy:.2f}%\")\n","\n","    scheduler_classifier.step()\n","\n","print(\"\\n--- Classifier Training Finished ---\\n\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8wwLWabl2Rmk","executionInfo":{"status":"ok","timestamp":1780212668001,"user_tz":-420,"elapsed":1398127,"user":{"displayName":"Cici rizky plk","userId":"03714270658772765776"}},"outputId":"eeabeaa8-f661-418b-d3c6-14268d103b6d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","--- Preparing for Classifier-only Training ---\n","\n","Model layers frozen except for input_proj, res_blocks, and head.\n","Trainable parameters: ['input_proj.weight', 'input_proj.bias', 'res_blocks.0.block.0.weight', 'res_blocks.0.block.0.bias', 'res_blocks.0.block.3.weight', 'res_blocks.0.block.3.bias', 'res_blocks.0.norm.weight', 'res_blocks.0.norm.bias', 'res_blocks.1.block.0.weight', 'res_blocks.1.block.0.bias', 'res_blocks.1.block.3.weight', 'res_blocks.1.block.3.bias', 'res_blocks.1.norm.weight', 'res_blocks.1.norm.bias', 'head.0.weight', 'head.0.bias', 'head.2.weight', 'head.2.bias']\n","Precomputing training features...\n","Precomputing batch 100/990\n","Precomputing batch 200/990\n","Precomputing batch 300/990\n","Precomputing batch 400/990\n","Precomputing batch 500/990\n","Precomputing batch 600/990\n","Precomputing batch 700/990\n","Precomputing batch 800/990\n","Precomputing batch 900/990\n","Training features shape: torch.Size([126689, 1024]), labels shape: torch.Size([126689])\n","Precomputing validation features...\n","Precomputing batch 100/157\n","Validation features shape: torch.Size([5000, 1024]), labels shape: torch.Size([5000])\n","New DataLoaders created with precomputed features.\n","\n","--- Starting Classifier Training ---\n","\n","Classifier Ep 1 | Step 100/990 | Loss 1.0428\n","Classifier Ep 1 | Step 200/990 | Loss 1.3297\n","Classifier Ep 1 | Step 300/990 | Loss 1.3002\n","Classifier Ep 1 | Step 400/990 | Loss 1.3607\n","Classifier Ep 1 | Step 500/990 | Loss 1.2848\n","Classifier Ep 1 | Step 600/990 | Loss 1.2878\n","Classifier Ep 1 | Step 700/990 | Loss 1.3643\n","Classifier Ep 1 | Step 800/990 | Loss 1.3337\n","Classifier Ep 1 | Step 900/990 | Loss 1.4291\n","Epoch 1 Classifier Training Complete. Train Loss: 1.3557, Train Acc: 63.00% | Val Loss: 1.7735, Val Acc: 53.62%\n","Classifier Ep 2 | Step 100/990 | Loss 1.4447\n","Classifier Ep 2 | Step 200/990 | Loss 1.4289\n","Classifier Ep 2 | Step 300/990 | Loss 1.2383\n","Classifier Ep 2 | Step 400/990 | Loss 1.4741\n","Classifier Ep 2 | Step 500/990 | Loss 1.2122\n","Classifier Ep 2 | Step 600/990 | Loss 1.2621\n","Classifier Ep 2 | Step 700/990 | Loss 1.3622\n","Classifier Ep 2 | Step 800/990 | Loss 1.4757\n","Classifier Ep 2 | Step 900/990 | Loss 1.4085\n","Epoch 2 Classifier Training Complete. Train Loss: 1.3237, Train Acc: 63.80% | Val Loss: 1.7814, Val Acc: 53.64%\n","Classifier Ep 3 | Step 100/990 | Loss 1.3816\n","Classifier Ep 3 | Step 200/990 | Loss 1.3590\n","Classifier Ep 3 | Step 300/990 | Loss 1.1102\n","Classifier Ep 3 | Step 400/990 | Loss 1.3962\n","Classifier Ep 3 | Step 500/990 | Loss 1.4070\n","Classifier Ep 3 | Step 600/990 | Loss 1.2653\n","Classifier Ep 3 | Step 700/990 | Loss 1.2707\n","Classifier Ep 3 | Step 800/990 | Loss 1.0930\n","Classifier Ep 3 | Step 900/990 | Loss 1.0200\n","Epoch 3 Classifier Training Complete. Train Loss: 1.2957, Train Acc: 64.44% | Val Loss: 1.7868, Val Acc: 53.80%\n","Classifier Ep 4 | Step 100/990 | Loss 1.0929\n","Classifier Ep 4 | Step 200/990 | Loss 1.3340\n","Classifier Ep 4 | Step 300/990 | Loss 1.3791\n","Classifier Ep 4 | Step 400/990 | Loss 1.1851\n","Classifier Ep 4 | Step 500/990 | Loss 1.1846\n","Classifier Ep 4 | Step 600/990 | Loss 1.5666\n","Classifier Ep 4 | Step 700/990 | Loss 1.1117\n","Classifier Ep 4 | Step 800/990 | Loss 1.1196\n","Classifier Ep 4 | Step 900/990 | Loss 1.5543\n","Epoch 4 Classifier Training Complete. Train Loss: 1.2702, Train Acc: 65.13% | Val Loss: 1.7915, Val Acc: 53.66%\n","Classifier Ep 5 | Step 100/990 | Loss 1.3435\n","Classifier Ep 5 | Step 200/990 | Loss 1.2772\n","Classifier Ep 5 | Step 300/990 | Loss 1.3480\n","Classifier Ep 5 | Step 400/990 | Loss 1.2380\n","Classifier Ep 5 | Step 500/990 | Loss 1.5307\n","Classifier Ep 5 | Step 600/990 | Loss 1.1653\n","Classifier Ep 5 | Step 700/990 | Loss 1.3140\n","Classifier Ep 5 | Step 800/990 | Loss 1.1067\n","Classifier Ep 5 | Step 900/990 | Loss 1.3781\n","Epoch 5 Classifier Training Complete. Train Loss: 1.2480, Train Acc: 65.74% | Val Loss: 1.7979, Val Acc: 53.78%\n","Classifier Ep 6 | Step 100/990 | Loss 1.2455\n","Classifier Ep 6 | Step 200/990 | Loss 1.2007\n","Classifier Ep 6 | Step 300/990 | Loss 1.0846\n","Classifier Ep 6 | Step 400/990 | Loss 1.1837\n","Classifier Ep 6 | Step 500/990 | Loss 1.2771\n","Classifier Ep 6 | Step 600/990 | Loss 1.2340\n","Classifier Ep 6 | Step 700/990 | Loss 1.3500\n","Classifier Ep 6 | Step 800/990 | Loss 1.1424\n","Classifier Ep 6 | Step 900/990 | Loss 1.3833\n","Epoch 6 Classifier Training Complete. Train Loss: 1.2294, Train Acc: 66.29% | Val Loss: 1.8015, Val Acc: 53.62%\n","Classifier Ep 7 | Step 100/990 | Loss 1.4600\n","Classifier Ep 7 | Step 200/990 | Loss 1.1267\n","Classifier Ep 7 | Step 300/990 | Loss 1.1663\n","Classifier Ep 7 | Step 400/990 | Loss 1.2789\n","Classifier Ep 7 | Step 500/990 | Loss 1.3731\n","Classifier Ep 7 | Step 600/990 | Loss 1.2361\n","Classifier Ep 7 | Step 700/990 | Loss 1.0858\n","Classifier Ep 7 | Step 800/990 | Loss 1.1114\n","Classifier Ep 7 | Step 900/990 | Loss 1.2883\n","Epoch 7 Classifier Training Complete. Train Loss: 1.2136, Train Acc: 66.58% | Val Loss: 1.8047, Val Acc: 53.64%\n","Classifier Ep 8 | Step 100/990 | Loss 0.8882\n","Classifier Ep 8 | Step 200/990 | Loss 1.1901\n","Classifier Ep 8 | Step 300/990 | Loss 1.4939\n","Classifier Ep 8 | Step 400/990 | Loss 1.1496\n","Classifier Ep 8 | Step 500/990 | Loss 1.3164\n","Classifier Ep 8 | Step 600/990 | Loss 1.0845\n","Classifier Ep 8 | Step 700/990 | Loss 1.3968\n","Classifier Ep 8 | Step 800/990 | Loss 1.1345\n","Classifier Ep 8 | Step 900/990 | Loss 1.6198\n","Epoch 8 Classifier Training Complete. Train Loss: 1.2006, Train Acc: 67.05% | Val Loss: 1.8080, Val Acc: 53.62%\n","Classifier Ep 9 | Step 100/990 | Loss 1.0463\n","Classifier Ep 9 | Step 200/990 | Loss 1.2753\n","Classifier Ep 9 | Step 300/990 | Loss 0.9279\n","Classifier Ep 9 | Step 400/990 | Loss 1.0579\n","Classifier Ep 9 | Step 500/990 | Loss 1.2457\n","Classifier Ep 9 | Step 600/990 | Loss 1.2596\n","Classifier Ep 9 | Step 700/990 | Loss 1.3585\n","Classifier Ep 9 | Step 800/990 | Loss 1.1310\n","Classifier Ep 9 | Step 900/990 | Loss 1.2731\n","Epoch 9 Classifier Training Complete. Train Loss: 1.1930, Train Acc: 67.23% | Val Loss: 1.8086, Val Acc: 53.72%\n","Classifier Ep 10 | Step 100/990 | Loss 0.9754\n","Classifier Ep 10 | Step 200/990 | Loss 1.0703\n","Classifier Ep 10 | Step 300/990 | Loss 1.1707\n","Classifier Ep 10 | Step 400/990 | Loss 1.0163\n","Classifier Ep 10 | Step 500/990 | Loss 1.1193\n","Classifier Ep 10 | Step 600/990 | Loss 1.1771\n","Classifier Ep 10 | Step 700/990 | Loss 1.2514\n","Classifier Ep 10 | Step 800/990 | Loss 1.0228\n","Classifier Ep 10 | Step 900/990 | Loss 1.4214\n","Epoch 10 Classifier Training Complete. Train Loss: 1.1884, Train Acc: 67.35% | Val Loss: 1.8090, Val Acc: 53.70%\n","\n","--- Classifier Training Finished ---\n","\n"]}]},{"cell_type":"code","source":["# 4. Define new optimizer for only the trainable parameters\n","optimizer_classifier = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=LR, weight_decay=WEIGHT_DECAY)\n","\n","# 5. Modified training loop for classifier\n","print(\"\\n--- Starting Classifier Training ---\\n\")\n","\n","# Define new EPOCHS for this classifier training if desired, or reuse global EPOCHS\n","CLASSIFIER_EPOCHS = 10 # Example: you can adjust this\n","scheduler_classifier = optim.lr_scheduler.CosineAnnealingLR(optimizer_classifier, T_max=CLASSIFIER_EPOCHS)\n","\n","# Re-initialize lists for plotting if you want to plot this separate training phase\n","classifier_train_losses = []\n","classifier_train_accuracies = []\n","classifier_val_losses = []\n","classifier_val_accuracies = []\n","\n","for epoch in range(10, 10+CLASSIFIER_EPOCHS):\n","    model.train() # Set model to train mode (only unfrozen layers will update)\n","    running_loss = 0.0\n","    running_corrects = 0\n","    running_samples = 0\n","\n","    for step, (features, target) in enumerate(precomputed_train_loader):\n","        features, target = features.to(DEVICE), target.to(DEVICE)\n","        optimizer_classifier.zero_grad()\n","\n","        # Forward pass through only the classifier part\n","        x = model.input_proj(features)\n","        x = model.res_blocks(x)\n","        output = model.head(x)\n","\n","        loss = criterion(output, target)\n","        loss.backward()\n","        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n","        optimizer_classifier.step()\n","\n","        running_loss += loss.item() * features.size(0)\n","        _, preds = torch.max(output, 1)\n","        running_corrects += torch.sum(preds == target.data)\n","        running_samples += features.size(0)\n","\n","        if (step+1) % 100 == 0:\n","            print(f\"Classifier Ep {epoch+1} | Step {step+1}/{len(precomputed_train_loader)} | Loss {loss.item():.4f}\")\n","\n","    epoch_train_loss = running_loss / running_samples\n","    epoch_train_accuracy = 100.0 * running_corrects.double() / running_samples\n","    classifier_train_losses.append(epoch_train_loss)\n","    classifier_train_accuracies.append(epoch_train_accuracy.item())\n","\n","    # Validation step\n","    model.eval()\n","    val_running_loss = 0.0\n","    val_running_corrects = 0\n","    val_running_samples = 0\n","    with torch.no_grad():\n","        for features, target in precomputed_val_loader:\n","            features, target = features.to(DEVICE), target.to(DEVICE)\n","\n","            x = model.input_proj(features)\n","            x = model.res_blocks(x)\n","            output = model.head(x)\n","\n","            loss = criterion(output, target)\n","            val_running_loss += loss.item() * features.size(0)\n","            _, preds = torch.max(output, 1)\n","            val_running_corrects += torch.sum(preds == target.data)\n","            val_running_samples += features.size(0)\n","\n","    epoch_val_loss = val_running_loss / val_running_samples\n","    epoch_val_accuracy = 100.0 * val_running_corrects.double() / val_running_samples\n","    classifier_val_losses.append(epoch_val_loss)\n","    classifier_val_accuracies.append(epoch_val_accuracy.item())\n","\n","    print(f\"Epoch {epoch+1} Classifier Training Complete. Train Loss: {epoch_train_loss:.4f}, Train Acc: {epoch_train_accuracy:.2f}% | Val Loss: {epoch_val_loss:.4f}, Val Acc: {epoch_val_accuracy:.2f}%\")\n","\n","    scheduler_classifier.step()\n","\n","print(\"\\n--- Classifier Training Finished ---\\n\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"Ya3dYsaL8m6q","executionInfo":{"status":"error","timestamp":1780212791346,"user_tz":-420,"elapsed":31378,"user":{"displayName":"Cici rizky plk","userId":"03714270658772765776"}},"outputId":"eb968732-f669-48c7-e925-2012a1dca6f8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","--- Starting Classifier Training ---\n","\n","Classifier Ep 11 | Step 100/990 | Loss 1.1814\n","Classifier Ep 11 | Step 200/990 | Loss 0.9902\n","Classifier Ep 11 | Step 300/990 | Loss 1.2605\n","Classifier Ep 11 | Step 400/990 | Loss 1.0301\n","Classifier Ep 11 | Step 500/990 | Loss 1.0562\n","Classifier Ep 11 | Step 600/990 | Loss 1.3090\n","Classifier Ep 11 | Step 700/990 | Loss 1.4702\n","Classifier Ep 11 | Step 800/990 | Loss 1.2620\n","Classifier Ep 11 | Step 900/990 | Loss 1.5132\n","Epoch 11 Classifier Training Complete. Train Loss: 1.2183, Train Acc: 66.44% | Val Loss: 1.8153, Val Acc: 53.66%\n","Classifier Ep 12 | Step 100/990 | Loss 1.3875\n","Classifier Ep 12 | Step 200/990 | Loss 1.2425\n","Classifier Ep 12 | Step 300/990 | Loss 1.2614\n","Classifier Ep 12 | Step 400/990 | Loss 1.3243\n","Classifier Ep 12 | Step 500/990 | Loss 1.0684\n","Classifier Ep 12 | Step 600/990 | Loss 1.3806\n","Classifier Ep 12 | Step 700/990 | Loss 1.0046\n","Classifier Ep 12 | Step 800/990 | Loss 1.1675\n","Classifier Ep 12 | Step 900/990 | Loss 1.2498\n","Epoch 12 Classifier Training Complete. Train Loss: 1.2002, Train Acc: 66.98% | Val Loss: 1.8292, Val Acc: 53.34%\n","Classifier Ep 13 | Step 100/990 | Loss 1.3661\n","Classifier Ep 13 | Step 200/990 | Loss 1.2365\n","Classifier Ep 13 | Step 300/990 | Loss 1.2388\n","Classifier Ep 13 | Step 400/990 | Loss 0.9729\n","Classifier Ep 13 | Step 500/990 | Loss 1.1616\n","Classifier Ep 13 | Step 600/990 | Loss 1.1476\n","Classifier Ep 13 | Step 700/990 | Loss 1.2395\n","Classifier Ep 13 | Step 800/990 | Loss 1.4016\n","Classifier Ep 13 | Step 900/990 | Loss 1.3372\n","Epoch 13 Classifier Training Complete. Train Loss: 1.1791, Train Acc: 67.57% | Val Loss: 1.8413, Val Acc: 53.44%\n","Classifier Ep 14 | Step 100/990 | Loss 1.1181\n","Classifier Ep 14 | Step 200/990 | Loss 1.2218\n","Classifier Ep 14 | Step 300/990 | Loss 1.1382\n","Classifier Ep 14 | Step 400/990 | Loss 1.0753\n","Classifier Ep 14 | Step 500/990 | Loss 1.2052\n","Classifier Ep 14 | Step 600/990 | Loss 1.0556\n","Classifier Ep 14 | Step 700/990 | Loss 1.2330\n","Classifier Ep 14 | Step 800/990 | Loss 1.3517\n","Classifier Ep 14 | Step 900/990 | Loss 1.1316\n","Epoch 14 Classifier Training Complete. Train Loss: 1.1578, Train Acc: 68.04% | Val Loss: 1.8465, Val Acc: 53.30%\n","Classifier Ep 15 | Step 100/990 | Loss 1.0904\n","Classifier Ep 15 | Step 200/990 | Loss 1.0389\n","Classifier Ep 15 | Step 300/990 | Loss 1.0067\n","Classifier Ep 15 | Step 400/990 | Loss 1.2791\n","Classifier Ep 15 | Step 500/990 | Loss 1.2488\n","Classifier Ep 15 | Step 600/990 | Loss 1.3129\n","Classifier Ep 15 | Step 700/990 | Loss 1.1243\n","Classifier Ep 15 | Step 800/990 | Loss 1.1999\n","Classifier Ep 15 | Step 900/990 | Loss 1.1083\n","Epoch 15 Classifier Training Complete. Train Loss: 1.1394, Train Acc: 68.59% | Val Loss: 1.8550, Val Acc: 53.36%\n","Classifier Ep 16 | Step 100/990 | Loss 1.2402\n","Classifier Ep 16 | Step 200/990 | Loss 1.0730\n","Classifier Ep 16 | Step 300/990 | Loss 1.2390\n","Classifier Ep 16 | Step 400/990 | Loss 0.9819\n","Classifier Ep 16 | Step 500/990 | Loss 1.1276\n","Classifier Ep 16 | Step 600/990 | Loss 1.1097\n","Classifier Ep 16 | Step 700/990 | Loss 1.1814\n","Classifier Ep 16 | Step 800/990 | Loss 0.9837\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_1318/537307369.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip_grad_norm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \u001b[0moptimizer_classifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/utils/clip_grad.py\u001b[0m in \u001b[0;36m_no_grad_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0;31m# pyrefly: ignore [invalid-param-spec]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_no_grad_wrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/utils/clip_grad.py\u001b[0m in \u001b[0;36mclip_grad_norm_\u001b[0;34m(parameters, max_norm, norm_type, error_if_nonfinite, foreach)\u001b[0m\n\u001b[1;32m    229\u001b[0m             )\n\u001b[1;32m    230\u001b[0m     \u001b[0mgrads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m     \u001b[0mtotal_norm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_total_norm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_if_nonfinite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforeach\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m     \u001b[0m_clip_grads_with_norm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforeach\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtotal_norm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/utils/clip_grad.py\u001b[0m in \u001b[0;36m_no_grad_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0;31m# pyrefly: ignore [invalid-param-spec]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_no_grad_wrapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/utils/clip_grad.py\u001b[0m in \u001b[0;36m_get_total_norm\u001b[0;34m(tensors, norm_type, error_if_nonfinite, foreach)\u001b[0m\n\u001b[1;32m     94\u001b[0m             \u001b[0mforeach\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0m_device_has_foreach_support\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         ):\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0mnorms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_foreach_norm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mforeach\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             raise RuntimeError(\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]}]}