notebooks/3_pretained_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "78afe1ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PyTorch library imports\n",
    "import torch  # Main library for tensors and deep learning in PyTorch\n",
    "import torch.nn as nn  # Neural network module in PyTorch\n",
    "import torchvision  # Computer vision extension for PyTorch\n",
    "from torchvision.datasets import ImageFolder  # For loading image data from a directory\n",
    "import torchvision.transforms as transforms  # For image transformations in PyTorch\n",
    "\n",
    "# Data handling imports for PyTorch\n",
    "from torch.utils.data import DataLoader, random_split, Dataset  # For handling datasets in PyTorch\n",
    "\n",
    "# Standard library imports\n",
    "import time  # For time-related functions\n",
    "import copy  # For deep copy operations\n",
    "\n",
    "# Third-party library imports\n",
    "import numpy as np  # For numerical operations\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score  # For model evaluation metrics\n",
    "from tqdm import tqdm  # For progress bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d72577e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://pytorch.org/vision/main/models/generated/torchvision.models.vgg11.html\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e47498e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if GPU is available and use it, otherwise use CPU\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8c73a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = ImageFolder(root='../data/art-styles', transform=transform)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3efcedeb-af85-4ad8-aefc-553eddf4bc8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# transpose the array to format expected by Matplotlib\n",
    "array = np.transpose(dataset[0][0], (1, 2, 0))\n",
    "\n",
    "# plot the image using Matplotlib\n",
    "plt.title(dataset.classes[dataset[0][1]])\n",
    "plt.imshow(array)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c8ba467",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a91e1bfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split up our data\n",
    "\n",
    "train_size = int(0.8 * len(dataset))\n",
    "test_size = len(dataset) - train_size\n",
    "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
    "\n",
    "# create data loaders to load up batches\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False, num_workers=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30818a82-3f94-4d9a-990e-30df802b5bfd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de8686fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, dataloaders, criterion, optimizer, device, num_epochs=25):\n",
    "    model.to(device)\n",
    "    since = time.time()\n",
    "\n",
    "    test_acc_history = []\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print(f\"Epoch {epoch + 1}/{num_epochs}\")\n",
    "        print(\"-\" * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in [\"train\", \"test\"]:\n",
    "            if phase == \"train\":\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()   # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "            dataloader = tqdm(dataloaders[phase], total=len(dataloaders[phase]))\n",
    "            \n",
    "            # Iterate over data.\n",
    "            for inputs, labels, *extra in dataloader:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "                for e in extra:\n",
    "                    e = e.to(device)    \n",
    "\n",
    "                # Zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # Forward\n",
    "                # Track history if only in train\n",
    "                with torch.set_grad_enabled(phase == \"train\"):\n",
    "                    # Get model outputs and calculate loss\n",
    "                    outputs = model(inputs, *extra)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "\n",
    "                    # Backward + optimize only if in training phase\n",
    "                    if phase == \"train\":\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "                    \n",
    "                # Statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
    "            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n",
    "\n",
    "            print(f\"{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}\")\n",
    "\n",
    "            # Deep copy the model\n",
    "            if phase == \"test\" and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "            if phase == \"test\":\n",
    "                test_acc_history.append(epoch_acc)\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print(f\"Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s\")\n",
    "    print(f\"Best test Acc: {best_acc:4f}\")\n",
    "\n",
    "    # Load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model, test_acc_history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f2f75221",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Function to evaluate the model\n",
    "def evaluate_model(model, test_loader, criterion):\n",
    "    model.eval()\n",
    "    y_true, y_pred = [], []\n",
    "    total_loss = 0.0\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels, *extra in tqdm(test_loader, total=len(test_loader)):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "            for e in extra:\n",
    "                e = e.to(device)   \n",
    "            outputs = model(inputs, *extra)\n",
    "            loss = criterion(outputs, labels)\n",
    "            total_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            y_true.extend(labels.cpu().numpy())\n",
    "            y_pred.extend(predicted.cpu().numpy())\n",
    "    \n",
    "    avg_loss = total_loss / len(test_loader)\n",
    "    accuracy, f1, precision, recall = evaluate_model_metrics(np.array(y_true), np.array(y_pred))\n",
    "    print(f'Loss: {avg_loss:.4f}, Accuracy: {accuracy:.4f}, F1-score: {f1:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}\\n')\n",
    "    return avg_loss, accuracy, f1, precision, recall\n",
    "\n",
    "# Function to calculate evaluation metrics\n",
    "def evaluate_model_metrics(y_true, y_pred):\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    f1 = f1_score(y_true, y_pred, average='macro', zero_division=0)\n",
    "    precision = precision_score(y_true, y_pred, average='macro', zero_division=0)\n",
    "    recall = recall_score(y_true, y_pred, average='macro', zero_division=0)\n",
    "    return accuracy, f1, precision, recall\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1336a802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mappingproxy({'IMAGENET1K_V1': VGG11_Weights.IMAGENET1K_V1,\n",
       "              'DEFAULT': VGG11_Weights.IMAGENET1K_V1})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torchvision.models.VGG11_Weights.__members__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54d81734",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the pre-trained VGG-11 model and fine-tune on your custom dataset\n",
    "vgg11_model = torchvision.models.vgg11(\n",
    "    weights=torchvision.models.VGG11_Weights.IMAGENET1K_V1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "76e2ba11",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (6): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (7): ReLU(inplace=True)\n",
       "    (8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (9): ReLU(inplace=True)\n",
       "    (10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (11): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (12): ReLU(inplace=True)\n",
       "    (13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (14): ReLU(inplace=True)\n",
       "    (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (17): ReLU(inplace=True)\n",
       "    (18): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (19): ReLU(inplace=True)\n",
       "    (20): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Dropout(p=0.5, inplace=False)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): Dropout(p=0.5, inplace=False)\n",
       "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg11_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e197b735",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modify the last layer to match the number of classes in your custom dataset\n",
    "num_classes = len(set(dataset.classes))\n",
    "vgg11_model.classifier[6] = nn.Linear(vgg11_model.classifier[6].in_features, num_classes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4da63713",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (6): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (7): ReLU(inplace=True)\n",
       "    (8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (9): ReLU(inplace=True)\n",
       "    (10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (11): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (12): ReLU(inplace=True)\n",
       "    (13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (14): ReLU(inplace=True)\n",
       "    (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (17): ReLU(inplace=True)\n",
       "    (18): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (19): ReLU(inplace=True)\n",
       "    (20): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Dropout(p=0.5, inplace=False)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): Dropout(p=0.5, inplace=False)\n",
       "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg11_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3d70f90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10e472cd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define loss and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(vgg11_model.parameters(), lr=0.001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb1daec1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [01:37<00:00,  3.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 2.3478, Accuracy: 0.1085, F1-score: 0.0882, Precision: 0.0863, Recall: 0.1080\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2.3478305265307426,\n",
       " 0.1085,\n",
       " 0.08816811396209259,\n",
       " 0.08626981043227755,\n",
       " 0.10803183787904538)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_model(vgg11_model, test_loader, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c7360e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "335c7dab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [18:15<00:00,  8.77s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 2.2659 Acc: 0.1315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [01:32<00:00,  2.89s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 2.1374 Acc: 0.1980\n",
      "\n",
      "Epoch 2/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [18:23<00:00,  8.83s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 2.1078 Acc: 0.2166\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [01:33<00:00,  2.91s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 2.0447 Acc: 0.2580\n",
      "\n",
      "Epoch 3/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [18:15<00:00,  8.76s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 2.0358 Acc: 0.2441\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [01:32<00:00,  2.88s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 1.9909 Acc: 0.2680\n",
      "\n",
      "Epoch 4/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [18:20<00:00,  8.81s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 1.9633 Acc: 0.2737\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [01:33<00:00,  2.91s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 1.9542 Acc: 0.2800\n",
      "\n",
      "Epoch 5/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [18:19<00:00,  8.80s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 1.9132 Acc: 0.2985\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [01:32<00:00,  2.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 1.8837 Acc: 0.3310\n",
      "\n",
      "Training complete in 99m 18s\n",
      "Best test Acc: 0.331000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dataloaders = {\"train\": train_loader, \"test\": test_loader}\n",
    "\n",
    "num_epochs = 5\n",
    "\n",
    "# Train the model (using the train_model function defined earlier)\n",
    "trained_vgg_model, test_acc_history = train_model(vgg11_model, dataloaders, criterion, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1abdca8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Loss values for each epoch\n",
    "train_loss = [2.2659, 2.1078, 2.0358, 1.9633, 1.9132]\n",
    "test_loss = [2.1374, 2.0447, 1.9909, 1.9542, 1.8837]\n",
    "\n",
    "# Accuracy values for each epoch\n",
    "train_acc = [0.1315, 0.2166, 0.2441, 0.2737, 0.2985]\n",
    "test_acc = [0.1980, 0.2580, 0.2680, 0.2800, 0.3310]\n",
    "\n",
    "# Number of epochs\n",
    "epochs = range(1, 6)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot for loss\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs, train_loss, 'o-', lw=2, markersize=8, label='Training Loss', color='dodgerblue')\n",
    "plt.plot(epochs, test_loss, 's-', lw=2, markersize=8, label='Test Loss', color='darkorange')\n",
    "plt.title('Training and Test Loss', fontsize=15)\n",
    "plt.xlabel('Epochs', fontsize=12)\n",
    "plt.ylabel('Loss', fontsize=12)\n",
    "plt.grid(True, linestyle='--')\n",
    "plt.xticks(epochs)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# Plot for accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs, train_acc, 'o-', lw=2, markersize=8, label='Training Accuracy', color='forestgreen')\n",
    "plt.plot(epochs, test_acc, 's-', lw=2, markersize=8, label='Test Accuracy', color='firebrick')\n",
    "plt.title('Training and Test Accuracy', fontsize=15)\n",
    "plt.xlabel('Epochs', fontsize=12)\n",
    "plt.ylabel('Accuracy', fontsize=12)\n",
    "plt.grid(True, linestyle='--')\n",
    "plt.xticks(epochs)\n",
    "plt.yticks([i * 0.1 for i in range(11)]) # Y-axis in increments of 0.1\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02dae242",
   "metadata": {},
   "source": [
    "The training and testing loss, as well as training and testing accuracy, over five epochs. The plot shows that both training and testing loss are decreasing together, and training and testing accuracy are increasing together.\n",
    "\n",
    "Here's why this might be happening, especially considering that we are using a larger pre-trained VGG model:\n",
    "\n",
    "1. Appropriate Model Complexity: A larger pre-trained VGG model is likely to have a more expressive feature extraction capacity. If the problem aligns well with the features that VGG is capable of capturing, the model may be able to learn more rapidly and effectively. This might result in both training and testing loss moving together as the model identifies relevant patterns in both datasets.\n",
    "\n",
    "2. Regularization: The model is regularized appropriately, either through techniques like dropout, weight decay, or simply through careful hyperparameter tuning, it may generalize better to the unseen test data. This can result in the training and testing loss (and accuracy) following a similar trend.\n",
    "\n",
    "3. Pre-training: Since VGG is a pre-trained model, it already has learned valuable features from its training on a large dataset like ImageNet. This can make the model more robust and capable of generalizing from the training data to the test data in the specific problem, leading to the observed behavior.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e924d448",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [01:32<00:00,  2.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 1.8897, Accuracy: 0.3310, F1-score: 0.3248, Precision: 0.3591, Recall: 0.3294\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.8896917812526226,\n",
       " 0.331,\n",
       " 0.3247992930099854,\n",
       " 0.3591019502652492,\n",
       " 0.3294200596279827)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_model(trained_vgg_model, test_loader, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "26da7789",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# Save the trained model\n",
    "torch.save(trained_vgg_model.state_dict(), \"../data/trained_vgg_model.pt\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1af9a5e",
   "metadata": {},
   "source": [
    "## Try with a bigger VGG 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b56da0e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [02:43<00:00,  5.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 2.3784, Accuracy: 0.0690, F1-score: 0.0395, Precision: 0.0439, Recall: 0.0679\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2.3783892393112183,\n",
       " 0.069,\n",
       " 0.03949636325648687,\n",
       " 0.043851258824304844,\n",
       " 0.06785678990095537)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the pre-trained VGG-11 model and fine-tune on your custom dataset\n",
    "vgg16_model = torchvision.models.vgg16(weights=torchvision.models.VGG16_Weights.DEFAULT)\n",
    "\n",
    "# Modify the last layer to match the number of classes in your custom dataset\n",
    "num_classes = len(set(dataset.classes))\n",
    "vgg16_model.classifier[-1] = nn.Linear(vgg16_model.classifier[-1].in_features, num_classes)\n",
    "\n",
    "# Define loss and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(vgg16_model.parameters(), lr=0.001)\n",
    "\n",
    "evaluate_model(vgg16_model, test_loader, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51ee8aa4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace=True)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace=True)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace=True)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace=True)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace=True)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace=True)\n",
       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace=True)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace=True)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace=True)\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU(inplace=True)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace=True)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace=True)\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Dropout(p=0.5, inplace=False)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): Dropout(p=0.5, inplace=False)\n",
       "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg16_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "649b9f4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [39:35<00:00, 19.01s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 2.3226 Acc: 0.1062\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [02:43<00:00,  5.10s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 2.2488 Acc: 0.1500\n",
      "\n",
      "Epoch 2/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [40:01<00:00, 19.21s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 2.2746 Acc: 0.1411\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [02:42<00:00,  5.07s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 2.2292 Acc: 0.1405\n",
      "\n",
      "Epoch 3/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [40:08<00:00, 19.27s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 2.2074 Acc: 0.1709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [02:41<00:00,  5.06s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 2.1405 Acc: 0.1960\n",
      "\n",
      "Epoch 4/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [40:09<00:00, 19.28s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 2.1874 Acc: 0.1833\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [02:41<00:00,  5.06s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 2.1697 Acc: 0.1870\n",
      "\n",
      "Epoch 5/5\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 125/125 [40:07<00:00, 19.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 2.1504 Acc: 0.1941\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [02:43<00:00,  5.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test Loss: 2.0759 Acc: 0.2375\n",
      "\n",
      "Training complete in 213m 36s\n",
      "Best test Acc: 0.237500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dataloaders = {\"train\": train_loader, \"test\": test_loader}\n",
    "\n",
    "num_epochs = 5\n",
    "\n",
    "# Train the model (using the train_model function defined earlier)\n",
    "trained_vgg16_model, test_acc_history = train_model(vgg16_model, dataloaders, criterion, optimizer, device, num_epochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4ec313a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "epochs = [1, 2, 3, 4, 5]\n",
    "train_loss = [2.3226, 2.2746, 2.2074, 2.1874, 2.1504]\n",
    "train_acc = [0.1062, 0.1411, 0.1709, 0.1833, 0.1941]\n",
    "test_loss = [2.2488, 2.2292, 2.1405, 2.1697, 2.0759]\n",
    "test_acc = [0.1500, 0.1405, 0.1960, 0.1870, 0.2375]\n",
    "\n",
    "# Number of epochs\n",
    "epochs = range(1, 6)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot for loss\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs, train_loss, 'o-', lw=2, markersize=8, label='Training Loss', color='dodgerblue')\n",
    "plt.plot(epochs, test_loss, 's-', lw=2, markersize=8, label='Test Loss', color='darkorange')\n",
    "plt.title('Training and Test Loss', fontsize=15)\n",
    "plt.xlabel('Epochs', fontsize=12)\n",
    "plt.ylabel('Loss', fontsize=12)\n",
    "plt.grid(True, linestyle='--')\n",
    "plt.xticks(epochs)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# Plot for accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs, train_acc, 'o-', lw=2, markersize=8, label='Training Accuracy', color='forestgreen')\n",
    "plt.plot(epochs, test_acc, 's-', lw=2, markersize=8, label='Test Accuracy', color='firebrick')\n",
    "plt.title('Training and Test Accuracy', fontsize=15)\n",
    "plt.xlabel('Epochs', fontsize=12)\n",
    "plt.ylabel('Accuracy', fontsize=12)\n",
    "plt.grid(True, linestyle='--')\n",
    "plt.xticks(epochs)\n",
    "plt.yticks([i * 0.1 for i in range(11)]) # Y-axis in increments of 0.1\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0162cf9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 32/32 [02:45<00:00,  5.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 2.0784, Accuracy: 0.2375, F1-score: 0.1932, Precision: 0.2944, Recall: 0.2358\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2.0783711187541485,\n",
       " 0.2375,\n",
       " 0.19315301562881834,\n",
       " 0.29440831790261274,\n",
       " 0.23584127324962884)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_model(trained_vgg16_model, test_loader, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9450b3b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# Save the trained model\n",
    "torch.save(trained_vgg16_model.state_dict(), \"../data/trained_vgg_16_model.pt\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8392af",
   "metadata": {},
   "source": [
    "The fact that a larger VGG model (like VGG-16) might perform worse than a smaller one (like VGG-11) on the same data may seem counterintuitive, as larger models generally have more capacity to learn complex features. However, there are several reasons why this might happen:\n",
    "\n",
    "1. Overfitting: VGG-16 has more layers and parameters compared to VGG-11, which increases its capacity to fit complex patterns. While this can be a strength, it might also lead to overfitting, especially if the dataset is relatively small or lacks diversity. Overfitting would cause the model to perform well on training data but poorly on unseen test data.\n",
    "\n",
    "2. Not Enough Training: Larger models typically require more data and more training time to converge. If the VGG-16 model is not trained for enough epochs or with enough data, it might not reach a good optimum, resulting in subpar performance compared to the smaller VGG-11.\n",
    "\n",
    "3. Hyperparameter Tuning: Different models might require different hyperparameter settings, such as learning rate, batch size, or weight decay. If the hyperparameters are optimized for VGG-11 but applied without modification to VGG-16, the latter might not perform at its best.\n",
    "\n",
    "4. Dataset Complexity: If the dataset's underlying patterns are relatively simple, a more complex model like VGG-16 may not provide any advantage and might even perform worse. Simpler models like VGG-11 can sometimes generalize better when the underlying complexity of the data is low."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda3a8d0",
   "metadata": {},
   "source": [
    "# BERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d826fae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from nlp import Dataset\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d10fa17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Load and preprocess the data\n",
    "csv_path = '../data/disaster.csv'\n",
    "tweets = pd.read_csv(csv_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "931dcc02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id keyword location                                               text  \\\n",
       "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
       "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
       "\n",
       "   target  \n",
       "0       1  \n",
       "1       1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a95e48fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the trainer is expecting a 'label' (see the forward method in the docs)\n",
    "tweets['label'] = tweets['target']\n",
    "del tweets['target']\n",
    "\n",
    "tweet_dataset = Dataset.from_pandas(tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "35eaf88a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset(features: {'id': Value(dtype='int64', id=None), 'keyword': Value(dtype='string', id=None), 'location': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 7613)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "33b8b617",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "BERT_MODEL = 'bert-base-uncased'  # uncased will lowercase everything and remove accents\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "bert_tokenizer = AutoTokenizer.from_pretrained(BERT_MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2b46e7ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e573917f1c84210a4f2e2118e9d55cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# simple function to batch tokenize utterances with truncation\n",
    "def preprocess_function(examples):\n",
    "    return bert_tokenizer(examples[\"text\"], truncation=True)\n",
    "\n",
    "tweet_dataset = tweet_dataset.map(preprocess_function, batched=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6cfb6a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd58b9a171604426a25a83318fb8195d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc09eb0d61b9412fad3e71abb184eda6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweet_dataset = tweet_dataset.train_test_split(test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8f4fac60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset(features: {'id': Value(dtype='int64', id=None), 'keyword': Value(dtype='string', id=None), 'location': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'input_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'token_type_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'attention_mask': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}, num_rows: 6090)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_dataset['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "28a70d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset(features: {'id': Value(dtype='int64', id=None), 'keyword': Value(dtype='string', id=None), 'location': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None), 'input_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'token_type_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'attention_mask': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}, num_rows: 1523)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_dataset['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7e3f8eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'attention_mask': [1,\n",
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       "  1,\n",
       "  1,\n",
       "  1,\n",
       "  1,\n",
       "  1,\n",
       "  1],\n",
       " 'id': 5371,\n",
       " 'input_ids': [101,\n",
       "  1005,\n",
       "  2019,\n",
       "  2324,\n",
       "  1011,\n",
       "  12819,\n",
       "  2234,\n",
       "  2247,\n",
       "  1998,\n",
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       "  2009,\n",
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       "  1998,\n",
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       "  2009,\n",
       "  2000,\n",
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       "  14376,\n",
       "  25619,\n",
       "  28061,\n",
       "  2080,\n",
       "  102],\n",
       " 'keyword': 'fire%20truck',\n",
       " 'label': 1,\n",
       " 'location': 'Here & There',\n",
       " 'text': \"'An 18-wheeler came along and struck the fire truck spinning it around 180 degrees and causing it to roll over' http://t.co/kmPfhGlhoo\",\n",
       " 'token_type_ids': [0,\n",
       "  0,\n",
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       "  0,\n",
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       "  0]}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_dataset['train'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eafb70ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DataCollatorWithPadding\n",
    "\n",
    "# DataCollatorWithPadding creates batch of data. It also dynamically pads text to the \n",
    "#  length of the longest element in the batch, making them all the same length. \n",
    "#  It's possible to pad your text in the tokenizer function with padding=True, dynamic padding is more efficient.\n",
    "data_collator = DataCollatorWithPadding(tokenizer=bert_tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7242c27e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias']\n",
      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0-11): 12 x BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification\n",
    "\n",
    "sequence_classification_model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    BERT_MODEL, num_labels=2,\n",
    ")\n",
    "\n",
    "sequence_classification_model.config.id2label = {0: 'NOT DISASTER', 1: 'DISASTER'}\n",
    "\n",
    "sequence_classification_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7be9fe60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertModel(\n",
       "  (embeddings): BertEmbeddings(\n",
       "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "    (position_embeddings): Embedding(512, 768)\n",
       "    (token_type_embeddings): Embedding(2, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder): BertEncoder(\n",
       "    (layer): ModuleList(\n",
       "      (0-11): 12 x BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pooler): BertPooler(\n",
       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (activation): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_classification_model.bert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "0447a762",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the compute_metrics function to calculate accuracy, f1, precision, and recall\n",
    "def compute_metrics(p):\n",
    "    preds = np.argmax(p.predictions, axis=1)\n",
    "    labels = p.label_ids\n",
    "    accuracy = np.mean(preds == labels)\n",
    "    precision = precision_score(labels, preds, average='weighted')\n",
    "    recall = recall_score(labels, preds, average='weighted')\n",
    "    return {'accuracy': accuracy, 'precision': precision, 'recall': recall}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b77a57d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 8\n",
    "epochs = 5\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir='./clf/results',\n",
    "    logging_dir='./clf/logs',\n",
    "    num_train_epochs=epochs,\n",
    "    per_device_train_batch_size=batch_size,\n",
    "    per_device_eval_batch_size=batch_size,\n",
    "    logging_strategy='steps',\n",
    "    logging_first_step=True,\n",
    "    load_best_model_at_end=True,\n",
    "    logging_steps=1,\n",
    "    evaluation_strategy='epoch',\n",
    "    eval_steps=1,\n",
    "    save_strategy='epoch',\n",
    "    report_to=\"wandb\",  # enable logging to W&B\n",
    "#     use_mps_device=True # might be neccesary on a mac\n",
    ")\n",
    "\n",
    "# Define the trainer: \n",
    "\n",
    "trainer = Trainer(\n",
    "    model=sequence_classification_model,\n",
    "    args=training_args,\n",
    "    train_dataset=tweet_dataset['train'],\n",
    "    eval_dataset=tweet_dataset['test'],\n",
    "    compute_metrics=compute_metrics,\n",
    "    data_collator=data_collator\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "aebab059",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='382' max='191' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [191/191 03:10]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.6799960732460022,\n",
       " 'eval_accuracy': 0.5449770190413658,\n",
       " 'eval_precision': 0.2969999512832131,\n",
       " 'eval_recall': 0.5449770190413658,\n",
       " 'eval_runtime': 8.9948,\n",
       " 'eval_samples_per_second': 169.32,\n",
       " 'eval_steps_per_second': 21.234}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d98f7184",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.10/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='3810' max='3810' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [3810/3810 15:50, Epoch 5/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.039000</td>\n",
       "      <td>0.473227</td>\n",
       "      <td>0.813526</td>\n",
       "      <td>0.815029</td>\n",
       "      <td>0.813526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.019900</td>\n",
       "      <td>0.601717</td>\n",
       "      <td>0.824032</td>\n",
       "      <td>0.834675</td>\n",
       "      <td>0.824032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.013300</td>\n",
       "      <td>0.674782</td>\n",
       "      <td>0.816809</td>\n",
       "      <td>0.818575</td>\n",
       "      <td>0.816809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.005600</td>\n",
       "      <td>0.902381</td>\n",
       "      <td>0.810900</td>\n",
       "      <td>0.814008</td>\n",
       "      <td>0.810900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>1.079001</td>\n",
       "      <td>0.806960</td>\n",
       "      <td>0.806711</td>\n",
       "      <td>0.806960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7b4e4e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='191' max='191' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [191/191 00:08]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.47322705388069153,\n",
       " 'eval_accuracy': 0.8135259356533159,\n",
       " 'eval_precision': 0.8150288673518512,\n",
       " 'eval_recall': 0.8135259356533159,\n",
       " 'eval_runtime': 8.3998,\n",
       " 'eval_samples_per_second': 181.313,\n",
       " 'eval_steps_per_second': 22.739,\n",
       " 'epoch': 5.0}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate()  # sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2ddf7246",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Loss and accuracy values for each epoch\n",
    "train_loss = [0.039000, 0.019900, 0.013300, 2.005600, 0.001100]\n",
    "val_loss = [0.473227, 0.601717, 0.674782, 0.902381, 1.079001]\n",
    "val_acc = [0.813526, 0.824032, 0.816809, 0.810900, 0.806960]\n",
    "\n",
    "# Number of epochs\n",
    "epochs = range(1, 6)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot for loss\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs, train_loss, 'o-', lw=2, markersize=8, label='Training Loss', color='dodgerblue')\n",
    "plt.plot(epochs, val_loss, 's-', lw=2, markersize=8, label='Validation Loss', color='darkorange')\n",
    "plt.title('Training and Validation Loss', fontsize=15)\n",
    "plt.xlabel('Epochs', fontsize=12)\n",
    "plt.ylabel('Loss', fontsize=12)\n",
    "plt.grid(True, linestyle='--')\n",
    "plt.xticks(epochs)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# Plot for accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "# plt.plot(epochs, train_acc, 'o-', lw=2, markersize=8, label='Training Accuracy', color='forestgreen')\n",
    "plt.plot(epochs, val_acc, 's-', lw=2, markersize=8, label='Validation Accuracy', color='firebrick')\n",
    "plt.title('Training and Validation Accuracy', fontsize=15)\n",
    "plt.xlabel('Epochs', fontsize=12)\n",
    "plt.ylabel('Accuracy', fontsize=12)\n",
    "plt.grid(True, linestyle='--')\n",
    "plt.xticks(epochs)\n",
    "plt.yticks([i * 0.05 for i in range(21)]) # Y-axis in increments of 0.05\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "536f4625",
   "metadata": {},
   "source": [
    "Woahhhhh that's interesting. Here's an analysis of what we're observing:\n",
    "\n",
    "1. Training Loss Decline: We see a drastic drop in the training loss during the first few epochs, which is typically indicative of the model learning well. However, there is an unexpected spike at epoch 4 before it drops again. This could be due to various factors such as an outlier in the training data, learning rate issues, or other stochastic behaviors related to the training process.\n",
    "\n",
    "2. Validation Loss Increase: On the other hand, the validation loss is increasing after the first epoch. This is typically indicative of overfitting, where the model is becoming too specialized to the training data and performs poorly on unseen data. The increasing complexity and fit to the training data may not generalize well to the validation data, causing this discrepancy between training and validation loss.\n",
    "\n",
    "3. Validation Accuracy Plateau: The validation accuracy shows a slight increase initially but then starts to decline, indicating that the model is not improving on the validation set. This behavior, coupled with the increasing validation loss, is a strong sign of overfitting.\n",
    "\n",
    "Potential Remedies:\n",
    "\n",
    "1. We want to consider early stopping, where the training is halted once the validation performance starts to degrade, to prevent overfitting.\n",
    "2. Regularization techniques like dropout, L2 regularization, etc., might also help in reducing overfitting.\n",
    "3. Adjusting the learning rate or using a learning rate scheduling policy could resolve the odd spike in the training loss.\n",
    "4. Analyzing the training data for inconsistencies or issues that might cause erratic training behavior may also be beneficial.\n",
    "5. The combination of these metrics and trends paints a picture of a model that is learning the training data quite well but struggling to generalize to unseen data, a classic sign of overfitting. Adjusting the training process with some of the strategies above might help in achieving a more balanced and robust model.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70b4c2f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0aa51dbd",
   "metadata": {},
   "source": [
    "# Tips going forward\n",
    "\n",
    "Choosing the right model, parameters, and regularization techniques is a crucial step in developing a successful machine learning pipeline. The optimal architecture depends on the specific nature of the task and the data. Here are some general tips:\n",
    "\n",
    "1. Understand the Data: Begin by exploring and understanding the data and the underlying problem. Simpler datasets might not require highly complex models, while intricate patterns might demand more layers or features.\n",
    "2. Grid or Random Search: Utilize grid search or random search to systematically explore different combinations of hyperparameters. Tools like Scikit-learn's GridSearchCV can be particularly helpful.\n",
    "3. Transfer Learning: If applicable, consider using pre-trained models and fine-tuning them to your specific task. This can leverage knowledge learned from related tasks and often reduce training time.\n",
    "4. Monitor Learning Curves: Plotting the training and validation loss and accuracy during training can provide insights into whether the model is underfitting, overfitting, or converging optimally.\n",
    "5. Consider Computational Constraints: Be mindful of the available computational resources, as large models require more memory and time to train. This might influence the choice of architecture.\n",
    "6. Iterate and Experiment: Building a model is often an iterative process. Experiment with different architectures, optimization techniques, and data augmentations to find the combination that works best for the task.\n",
    "7. Start Simple: It's often beneficial to start with a simpler model and progressively add complexity if needed. This can help in understanding how the complexity of the model affects performance.\n",
    "\n",
    "Remember, there is often no one-size-fits-all solution in machine learning. The process of finding the optimal architecture requires thoughtful experimentation, patience, and a solid understanding of both the data and the algorithms being used."
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