diff --git a/cnnBase.ipynb b/cnnBase.ipynb
index ae1423952e51ad9ec17a8fcb8f04748746592573..7dcdbc9f255ea937aadfc0c946103cd096fe1d00 100644
--- a/cnnBase.ipynb
+++ b/cnnBase.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
@@ -25,7 +25,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Files already downloaded and verified\n",
       "Files already downloaded and verified\n",
       "GPU is available\n"
      ]
@@ -38,43 +37,49 @@
     "import torchvision.transforms.v2 as transforms\n",
     "import torchvision.datasets as datasets\n",
     "import torchvision\n",
-    "from torch.utils.data import DataLoader, random_split\n",
+    "from torch.utils.data import DataLoader, random_split, Subset\n",
     "import numpy as np\n",
     "# Define your data transformations\n",
     "transform_train = transforms.Compose([\n",
+    "    \n",
     "    transforms.RandomHorizontalFlip(),\n",
     "    transforms.RandomRotation(10),\n",
-    "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),\n",
+    "    torchvision.transforms.RandomCrop(32, padding=4, padding_mode='reflect'),\n",
     "    transforms.ToTensor(),\n",
-    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
+    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
     "])\n",
+    "full = True\n",
     "\n",
-    "# Load CIFAR-10 dataset\n",
+    "torch.manual_seed(1)\n",
+    "batch_size = 32\n",
     "train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)\n",
+    "if full:\n",
+    "    \n",
+    "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=6, pin_memory=True)\n",
     "\n",
-    "# Define data loaders\n",
-    "batch_size = 512\n",
-    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)\n",
-    "\n",
-    "\n",
-    "transform_test = transforms.Compose([\n",
-    "    transforms.ToTensor(),\n",
-    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
-    "])\n",
-    "\n",
-    "# Load CIFAR-10 dataset\n",
-    "test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n",
-    "val_size = int(0.5 * len(test_dataset))\n",
-    "test_size = len(test_dataset) - val_size\n",
-    "test_dataset, val_dataset = random_split(test_dataset, [test_size, val_size])\n",
-    "\n",
-    "# Define data loaders\n",
-    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)\n",
-    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)\n",
-    "\n",
+    "    transform_test = transforms.Compose([\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
+    "    ])\n",
     "\n",
+    "    # Load CIFAR-10 dataset\n",
+    "    test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n",
+    "    val_size = int(0.5 * len(test_dataset))\n",
+    "    test_size = len(test_dataset) - val_size\n",
+    "    test_dataset, val_dataset = random_split(test_dataset, [test_size, val_size])\n",
     "\n",
+    "    # Define data loaders\n",
+    "    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)\n",
+    "    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True)\n",
     "\n",
+    "else:\n",
+    "    subset_indices = np.random.choice(len(train_dataset), size=5000, replace=False)\n",
+    "    train_dataset = Subset(train_dataset, subset_indices)\n",
+    "    val_size = int(0.3 * len(train_dataset))\n",
+    "    train_size = len(train_dataset) - val_size\n",
+    "    train_dataset, val_dataset = random_split(train_dataset, [train_size, val_size])\n",
+    "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True)\n",
+    "    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)\n",
     "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
     "\n",
     "# get some random training images\n",
@@ -91,27 +96,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "truck car   plane dog   truck frog  truck cat   truck frog  bird  horse car   plane truck ship  car   cat   bird  cat   cat   horse deer  dog   horse ship  horse car   deer  deer  dog   plane cat   plane cat   plane deer  deer  car   deer  plane truck cat   frog  frog  dog   truck cat   plane horse frog  dog   dog   dog   cat   horse dog   car   bird  plane horse plane dog   bird  frog  cat   cat   dog   car   truck cat   truck horse plane car   horse truck ship  dog   horse truck cat   plane cat   deer  plane car   dog   frog  ship  car   dog   deer  car   plane plane ship  cat   car   car   frog  frog  truck cat   cat   deer  car   truck truck ship  horse ship  car   horse dog   ship  car   deer  car   frog  plane frog  horse plane deer  truck dog   car   ship  frog  dog   horse truck deer  cat   ship  cat   deer  frog  car   frog  ship  car   frog  car   frog  ship  dog   frog  frog  cat   cat   cat   plane cat   cat   plane cat   bird  ship  plane frog  dog   truck deer  deer  cat   dog   truck bird  ship  plane car   frog  truck ship  truck truck bird  dog   horse car   car   frog  car   frog  deer  cat   car   dog   dog   plane bird  ship  truck horse truck dog   bird  frog  plane frog  cat   cat   frog  bird  plane bird  deer  ship  deer  truck dog   cat   horse frog  horse plane dog   ship  horse cat   cat   truck plane deer  truck dog   horse cat   ship  horse car   bird  cat   horse bird  cat   truck plane car   deer  frog  truck plane car   cat   plane car   cat   frog  plane deer  frog  horse deer  deer  plane deer  horse horse plane ship  cat   truck plane bird  deer  cat   car   cat   dog   truck frog  bird  truck cat   bird  cat   truck horse horse deer  truck horse horse truck dog   car   deer  plane plane ship  frog  bird  dog   truck cat   car   frog  horse bird  truck horse plane dog   plane dog   deer  bird  car   ship  dog   plane deer  ship  truck ship  frog  ship  frog  car   car   plane horse car   cat   car   plane dog   deer  plane frog  cat   truck horse horse frog  car   truck plane car   deer  cat   dog   truck truck plane truck car   ship  cat   horse bird  car   cat   ship  plane horse bird  cat   truck car   ship  ship  horse deer  dog   horse horse car   horse plane frog  frog  dog   plane plane ship  cat   ship  bird  frog  plane deer  dog   dog   bird  plane frog  deer  ship  bird  dog   horse dog   frog  plane deer  car   horse deer  ship  deer  truck cat   frog  plane horse deer  dog   car   cat   plane deer  ship  cat   cat   frog  cat   cat   car   plane bird  bird  ship  deer  ship  plane frog  deer  horse frog  frog  dog   ship  cat   frog  ship  horse truck car   ship  frog  bird  horse dog   car   dog   car   bird  plane frog  deer  frog  dog   deer  cat   bird  deer  deer  plane frog  bird  plane dog   truck horse deer  dog   bird  truck dog   frog  plane frog  bird  bird  ship  dog   bird  car   ship  car   horse bird  bird  dog   bird  frog  car   ship  car   dog   bird  ship  ship  plane dog   horse dog   bird  frog  plane horse plane ship  plane deer  car   bird  deer \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -136,57 +123,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
     "\n",
+    "import torch.nn as nn\n",
+    "\n",
     "class Net(nn.Module):\n",
     "    def __init__(self, num_classes=10):\n",
     "        super(Net, self).__init__()\n",
     "\n",
-    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)\n",
-    "        self.bn1 = nn.BatchNorm2d(64)\n",
-    "        self.relu1 = nn.ReLU()\n",
-    "        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)\n",
-    "        self.bn2 = nn.BatchNorm2d(64)\n",
-    "        self.relu2 = nn.ReLU()\n",
-    "        self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
-    "\n",
-    "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)\n",
-    "        self.bn3 = nn.BatchNorm2d(128)\n",
-    "        self.relu3 = nn.ReLU()\n",
-    "        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)\n",
-    "        self.bn4 = nn.BatchNorm2d(128)\n",
-    "        self.relu4 = nn.ReLU()\n",
-    "        self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
-    "\n",
-    "        self.flatten = nn.Flatten()\n",
-    "        self.fc1 = nn.Linear(128 * 8 * 8, 256)\n",
-    "        self.bn5 = nn.BatchNorm1d(256)\n",
-    "        self.relu5 = nn.ReLU()\n",
-    "        self.dropout1 = nn.Dropout(0.5)\n",
-    "        self.fc2 = nn.Linear(256, num_classes)\n",
+    "        self.features = nn.Sequential(\n",
+    "            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.ReLU(),\n",
+    "            nn.BatchNorm2d(64),\n",
+    "            \n",
+    "            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.ReLU(),\n",
+    "            nn.BatchNorm2d(64),\n",
+    "            \n",
+    "\n",
+    "            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.ReLU(),\n",
+    "            nn.BatchNorm2d(128),\n",
+    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
+    "            nn.Dropout(0.1),\n",
+    "            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.ReLU(),\n",
+    "            nn.BatchNorm2d(128),\n",
+    "            nn.MaxPool2d(kernel_size=2, stride=2)\n",
+    "        )\n",
+    "\n",
+    "        self.classifier = nn.Sequential(\n",
+    "            nn.Flatten(),\n",
+    "            nn.Linear(128 * 8 * 8, 256),\n",
+    "            nn.ReLU(),\n",
+    "            nn.BatchNorm1d(256),\n",
+    "            nn.Linear(256, 256),\n",
+    "            nn.Dropout(0.1),\n",
+    "            nn.Linear(256, num_classes)\n",
+    "        )\n",
     "\n",
     "    def forward(self, x):\n",
-    "        x = self.relu1(self.bn1(self.conv1(x)))\n",
-    "        x = self.relu2(self.bn2(self.conv2(x)))\n",
-    "        x = self.maxpool1(x)\n",
-    "\n",
-    "        x = self.relu3(self.bn3(self.conv3(x)))\n",
-    "        x = self.relu4(self.bn4(self.conv4(x)))\n",
-    "        x = self.maxpool2(x)\n",
-    "\n",
-    "        x = self.flatten(x)\n",
-    "        x = self.relu5(self.bn5(self.fc1(x)))\n",
-    "        x = self.dropout1(x)\n",
-    "        x = self.fc2(x)\n",
-    "\n",
-    "        return x\n",
-    "\n",
+    "        x = self.features(x)\n",
+    "        x = self.classifier(x)\n",
+    "        return x\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "class EarlyStopping:\n",
     "    def __init__(self, patience=5, delta=0, verbose=False):\n",
     "        self.patience = patience\n",
@@ -217,6 +208,7 @@
     "            print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')\n",
     "        torch.save(model.state_dict(), 'checkpoint.pt')\n",
     "        self.val_loss_min = val_loss\n",
+    "\n",
     "def validate(model, dataloader, criterion):\n",
     "    model.eval()\n",
     "    total_loss = 0.0\n",
@@ -236,14 +228,12 @@
     "            correct += (predicted == labels).sum().item()\n",
     "\n",
     "    accuracy = correct / total\n",
-    "    return total_loss / len(dataloader), accuracy\n",
-    "\n",
-    "\n"
+    "    return total_loss / len(dataloader), accuracy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,13 +253,12 @@
     "\n",
     "        for inputs, labels in tqdm(train_loader):\n",
     "            inputs, labels = inputs.to(device), labels.to(device)\n",
-    "\n",
     "            optimizer.zero_grad()\n",
     "            outputs = model(inputs)\n",
     "            loss = criterion(outputs, labels)\n",
     "            loss.backward()\n",
     "            optimizer.step()\n",
-    "\n",
+    "            optimizer.zero_grad()\n",
     "            running_loss += loss.item()\n",
     "\n",
     "        model.eval()\n",
@@ -287,7 +276,7 @@
     "            print(\"Early stopping\")\n",
     "            break\n",
     "\n",
-    "        print(f'Epoch {epoch + 1}, Training Loss: {running_loss / len(train_loader)}, Validation Loss: {val_loss}, Validation Accuracy: {val_accuracy}, Learning Rate: {optimizer.param_groups[0][\"lr\"]}')\n",
+    "        print(f'Epoch {epoch + 1}/ {num_epochs}, Training Loss: {running_loss / len(train_loader)}, Validation Loss: {val_loss}, Validation Accuracy: {val_accuracy}, Learning Rate: {optimizer.param_groups[0][\"lr\"]}')\n",
     "\n",
     "    print('Finished Training')\n",
     "\n",
@@ -298,22 +287,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f557cccda88e462fa500e6eb6812350a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation loss decreased (inf --> 1.689897).  Saving model ...\n",
+      "Epoch 1/ 50, Training Loss: 1.8000634345141324, Validation Loss: 1.6898972582309804, Validation Accuracy: 0.37466666666666665, Learning Rate: 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cdb697e7298f4d04bbd78352f395878f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation loss decreased (1.689897 --> 1.545304).  Saving model ...\n",
+      "Epoch 2/ 50, Training Loss: 1.5932702367955989, Validation Loss: 1.5453043668828113, Validation Accuracy: 0.43466666666666665, Learning Rate: 0.001\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e81efede6eeb4462b1a02314b1807c9a",
+       "model_id": "1efd1e60f8ae4bab9982d7e1e53a188f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/98 [00:00<?, ?it/s]"
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Main\\Documents\\com3013-comp-intelligence\\cnnBase.ipynb Cell 7\u001b[0m line \u001b[0;36m2\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m scheduler_Adam \u001b[39m=\u001b[39m optim\u001b[39m.\u001b[39mlr_scheduler\u001b[39m.\u001b[39mReduceLROnPlateau(optimizer_Adam, mode\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mmin\u001b[39m\u001b[39m'\u001b[39m, patience\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, factor\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m, verbose\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m \u001b[39m# Usage example\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m train_losses_Adam, val_losses_Adam, val_accuracies_Adam \u001b[39m=\u001b[39m train_model(AdamModel, train_loader, val_loader, criterion, optimizer_Adam, scheduler_Adam)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m \u001b[39m#train_losses_SGD, val_losses_SGD, val_accuracies_SGD = train_model(SGDModel, train_loader, val_loader, criterion, optimizer_SGD, scheduler_SGD)\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m \u001b[39m#train_losses_RMSProp, val_losses_RMSProp, val_accuracies_RMSProp = train_model(RMSPropModel, train_loader, val_loader, criterion, optimizer_RMSProp, scheduler_RMSProp)\u001b[39;00m\n",
+      "\u001b[1;32mc:\\Users\\Main\\Documents\\com3013-comp-intelligence\\cnnBase.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m model\u001b[39m.\u001b[39mtrain()\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m running_loss \u001b[39m=\u001b[39m \u001b[39m0.0\u001b[39m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m \u001b[39mfor\u001b[39;00m inputs, labels \u001b[39min\u001b[39;00m tqdm(train_loader):\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m     inputs, labels \u001b[39m=\u001b[39m inputs\u001b[39m.\u001b[39mto(device), labels\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#W5sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m     optimizer\u001b[39m.\u001b[39mzero_grad()\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\tqdm\\notebook.py:249\u001b[0m, in \u001b[0;36mtqdm_notebook.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    247\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    248\u001b[0m     it \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39m(tqdm_notebook, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__iter__\u001b[39m()\n\u001b[1;32m--> 249\u001b[0m     \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m it:\n\u001b[0;32m    250\u001b[0m         \u001b[39m# return super(tqdm...) will not catch exception\u001b[39;00m\n\u001b[0;32m    251\u001b[0m         \u001b[39myield\u001b[39;00m obj\n\u001b[0;32m    252\u001b[0m \u001b[39m# NB: except ... [ as ...] breaks IPython async KeyboardInterrupt\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\tqdm\\std.py:1182\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1179\u001b[0m time \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_time\n\u001b[0;32m   1181\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 1182\u001b[0m     \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m iterable:\n\u001b[0;32m   1183\u001b[0m         \u001b[39myield\u001b[39;00m obj\n\u001b[0;32m   1184\u001b[0m         \u001b[39m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[0;32m   1185\u001b[0m         \u001b[39m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    628\u001b[0m     \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    629\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset()  \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 630\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_next_data()\n\u001b[0;32m    631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m    632\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m    633\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m    634\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:674\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    672\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    673\u001b[0m     index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index()  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 674\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset_fetcher\u001b[39m.\u001b[39;49mfetch(index)  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    675\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[0;32m    676\u001b[0m         data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:49\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_collation:\n\u001b[0;32m     48\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset, \u001b[39m\"\u001b[39m\u001b[39m__getitems__\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__:\n\u001b[1;32m---> 49\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset\u001b[39m.\u001b[39;49m__getitems__(possibly_batched_index)\n\u001b[0;32m     50\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     51\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataset.py:362\u001b[0m, in \u001b[0;36mSubset.__getitems__\u001b[1;34m(self, indices)\u001b[0m\n\u001b[0;32m    358\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitems__\u001b[39m(\u001b[39mself\u001b[39m, indices: List[\u001b[39mint\u001b[39m]) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m List[T_co]:\n\u001b[0;32m    359\u001b[0m     \u001b[39m# add batched sampling support when parent dataset supports it.\u001b[39;00m\n\u001b[0;32m    360\u001b[0m     \u001b[39m# see torch.utils.data._utils.fetch._MapDatasetFetcher\u001b[39;00m\n\u001b[0;32m    361\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mcallable\u001b[39m(\u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset, \u001b[39m\"\u001b[39m\u001b[39m__getitems__\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)):\n\u001b[1;32m--> 362\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset\u001b[39m.\u001b[39;49m__getitems__([\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindices[idx] \u001b[39mfor\u001b[39;49;00m idx \u001b[39min\u001b[39;49;00m indices])  \u001b[39m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m    363\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    364\u001b[0m         \u001b[39mreturn\u001b[39;00m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindices[idx]] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m indices]\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataset.py:364\u001b[0m, in \u001b[0;36mSubset.__getitems__\u001b[1;34m(self, indices)\u001b[0m\n\u001b[0;32m    362\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__([\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindices[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m indices])  \u001b[39m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m    363\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 364\u001b[0m     \u001b[39mreturn\u001b[39;00m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindices[idx]] \u001b[39mfor\u001b[39;49;00m idx \u001b[39min\u001b[39;49;00m indices]\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataset.py:364\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    362\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__([\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindices[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m indices])  \u001b[39m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m    363\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 364\u001b[0m     \u001b[39mreturn\u001b[39;00m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindices[idx]] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m indices]\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\datasets\\cifar.py:118\u001b[0m, in \u001b[0;36mCIFAR10.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    115\u001b[0m img \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39mfromarray(img)\n\u001b[0;32m    117\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 118\u001b[0m     img \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransform(img)\n\u001b[0;32m    120\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    121\u001b[0m     target \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform(target)\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\v2\\_container.py:53\u001b[0m, in \u001b[0;36mCompose.forward\u001b[1;34m(self, *inputs)\u001b[0m\n\u001b[0;32m     51\u001b[0m needs_unpacking \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(inputs) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m     52\u001b[0m \u001b[39mfor\u001b[39;00m transform \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms:\n\u001b[1;32m---> 53\u001b[0m     outputs \u001b[39m=\u001b[39m transform(\u001b[39m*\u001b[39;49minputs)\n\u001b[0;32m     54\u001b[0m     inputs \u001b[39m=\u001b[39m outputs \u001b[39mif\u001b[39;00m needs_unpacking \u001b[39melse\u001b[39;00m (outputs,)\n\u001b[0;32m     55\u001b[0m \u001b[39mreturn\u001b[39;00m outputs\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:688\u001b[0m, in \u001b[0;36mRandomCrop.forward\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m    685\u001b[0m     padding \u001b[39m=\u001b[39m [\u001b[39m0\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msize[\u001b[39m0\u001b[39m] \u001b[39m-\u001b[39m height]\n\u001b[0;32m    686\u001b[0m     img \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mpad(img, padding, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfill, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode)\n\u001b[1;32m--> 688\u001b[0m i, j, h, w \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_params(img, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msize)\n\u001b[0;32m    690\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mcrop(img, i, j, h, w)\n",
+      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:652\u001b[0m, in \u001b[0;36mRandomCrop.get_params\u001b[1;34m(img, output_size)\u001b[0m\n\u001b[0;32m    649\u001b[0m \u001b[39mif\u001b[39;00m w \u001b[39m==\u001b[39m tw \u001b[39mand\u001b[39;00m h \u001b[39m==\u001b[39m th:\n\u001b[0;32m    650\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39m0\u001b[39m, \u001b[39m0\u001b[39m, h, w\n\u001b[1;32m--> 652\u001b[0m i \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mrandint(\u001b[39m0\u001b[39;49m, h \u001b[39m-\u001b[39;49m th \u001b[39m+\u001b[39;49m \u001b[39m1\u001b[39;49m, size\u001b[39m=\u001b[39;49m(\u001b[39m1\u001b[39;49m,))\u001b[39m.\u001b[39mitem()\n\u001b[0;32m    653\u001b[0m j \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mrandint(\u001b[39m0\u001b[39m, w \u001b[39m-\u001b[39m tw \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m, size\u001b[39m=\u001b[39m(\u001b[39m1\u001b[39m,))\u001b[39m.\u001b[39mitem()\n\u001b[0;32m    654\u001b[0m \u001b[39mreturn\u001b[39;00m i, j, th, tw\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
     }
    ],
    "source": [
@@ -321,10 +389,11 @@
     "\n",
     "SGDModel = Net() #will use this initally for freezing\n",
     "RMSPropModel = Net()\n",
-    "\n",
-    "\n",
+    "AdamModel = Net()\n",
+    "print(device)\n",
     "SGDModel.to(device)\n",
     "RMSPropModel.to(device)\n",
+    "AdamModel.to(device)\n",
     "inputs, labels = images.to(device), labels.to(device)\n",
     "\n",
     "\n",
@@ -334,28 +403,58 @@
     "#RMSProp\n",
     "optimizer_RMSProp = optim.RMSprop(RMSPropModel.parameters(), lr=0.001)\n",
     "scheduler_RMSProp = optim.lr_scheduler.ReduceLROnPlateau(optimizer_RMSProp, mode='min', patience=2, factor=0.1, verbose=True)\n",
-    "#Swarm Optimisation from DEAP\n",
-    "\n",
-    "\n",
+    "#Adam\n",
+    "optimizer_Adam = optim.Adamax(AdamModel.parameters(), lr=0.001)\n",
+    "scheduler_Adam = optim.lr_scheduler.ReduceLROnPlateau(optimizer_Adam, mode='min', patience=2, factor=0.1, verbose=True)\n",
     "# Usage example\n",
+    "train_losses_Adam, val_losses_Adam, val_accuracies_Adam = train_model(AdamModel, train_loader, val_loader, criterion, optimizer_Adam, scheduler_Adam)\n",
     "#train_losses_SGD, val_losses_SGD, val_accuracies_SGD = train_model(SGDModel, train_loader, val_loader, criterion, optimizer_SGD, scheduler_SGD)\n",
-    "train_losses_RMSProp, val_losses_RMSProp, val_accuracies_RMSProp = train_model(RMSPropModel, train_loader, val_loader, criterion, optimizer_RMSProp, scheduler_RMSProp)"
+    "#train_losses_RMSProp, val_losses_RMSProp, val_accuracies_RMSProp = train_model(RMSPropModel, train_loader, val_loader, criterion, optimizer_RMSProp, scheduler_RMSProp)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "PATH = './cifar_BaseModelSGD.pth'\n",
+    "torch.save(SGDModel.state_dict(), PATH)\n",
+    "net = Net()\n",
+    "net.load_state_dict(torch.load(PATH))\n",
+    "model1 = copy.deepcopy(net)\n",
+    "model2 = copy.deepcopy(net)\n",
+    "model3 = copy.deepcopy(net)\n",
+    "# Move the models to the same device as the input tensor\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model1.to(device)\n",
+    "model2.to(device)\n",
+    "model3.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "______________________________________\n",
+      "Adam\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b85460b6c4244ac493718073f7565d08",
+       "model_id": "b6d62a32205c48c7a1001a65b62ef2c8",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/98 [00:00<?, ?it/s]"
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -365,19 +464,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation loss decreased (inf --> 0.492346).  Saving model ...\n",
-      "Epoch 1, Training Loss: 0.5311253800684091, Validation Loss: 0.4923455625772476, Validation Accuracy: 0.829, Learning Rate: 0.001\n"
+      "Validation loss decreased (inf --> 1.629724).  Saving model ...\n",
+      "Epoch 1/ 5, Training Loss: 1.6367928223176436, Validation Loss: 1.6297235737244289, Validation Accuracy: 0.43266666666666664, Learning Rate: 0.001\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "16f1726b36f64184af6d00fe211fbd43",
+       "model_id": "df33595130c74399ac183a85e5676ea0",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/55 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -387,19 +486,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation loss decreased (0.492346 --> 0.487910).  Saving model ...\n",
-      "Epoch 2, Training Loss: 0.5180915150107169, Validation Loss: 0.4879096269607544, Validation Accuracy: 0.828, Learning Rate: 0.001\n"
+      "Validation loss decreased (1.629724 --> 1.624665).  Saving model ...\n",
+      "Epoch 2/ 5, Training Loss: 1.6378082687204534, Validation Loss: 1.6246654242277145, Validation Accuracy: 0.43933333333333335, Learning Rate: 0.001\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b96b8129d1e42f6aa4ef6c1edb3e710",
+       "model_id": "851b93c2303247d184d7f5e6242dc128",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/55 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -409,77 +508,320 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation loss decreased (0.487910 --> 0.484739).  Saving model ...\n",
-      "Epoch 3, Training Loss: 0.5183646703252986, Validation Loss: 0.48473949134349825, Validation Accuracy: 0.829, Learning Rate: 0.001\n"
+      "Validation loss decreased (1.624665 --> 1.603106).  Saving model ...\n",
+      "Epoch 3/ 5, Training Loss: 1.6432774088599464, Validation Loss: 1.6031060020128887, Validation Accuracy: 0.432, Learning Rate: 0.001\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ac562c81c0484709a477e58951245f0a",
+       "model_id": "94bd74bc1a9b4498aa16d09d001ff39f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/55 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\Main\\Documents\\com3013-comp-intelligence\\cnnBase.ipynb Cell 7\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X16sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m scheduler_RMSProp \u001b[39m=\u001b[39m optim\u001b[39m.\u001b[39mlr_scheduler\u001b[39m.\u001b[39mReduceLROnPlateau(optimizer_RMSProp, mode\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mmin\u001b[39m\u001b[39m'\u001b[39m, patience\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, factor\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m, verbose\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X16sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m \u001b[39m# Train with different optimization algorithms\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X16sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m train_losses_SGD, val_losses_SGD, val_accuracies_SGD \u001b[39m=\u001b[39m train_model(model1, train_loader, val_loader, criterion, optimizer_SGD, scheduler_SGD)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X16sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m \u001b[39m#train_losses_RMSProp, val_losses_RMSProp, val_accuracies_RMSProp = train_model(SGDModel2, train_loader, val_loader, criterion, optimizer_RMSProp, scheduler_RMSProp)\u001b[39;00m\n",
-      "\u001b[1;32mc:\\Users\\Main\\Documents\\com3013-comp-intelligence\\cnnBase.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X16sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m model\u001b[39m.\u001b[39mtrain()\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X16sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m running_loss \u001b[39m=\u001b[39m \u001b[39m0.0\u001b[39m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X16sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m \u001b[39mfor\u001b[39;00m inputs, labels \u001b[39min\u001b[39;00m tqdm(train_loader):\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X16sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m     inputs, labels \u001b[39m=\u001b[39m inputs\u001b[39m.\u001b[39mto(device), labels\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Main/Documents/com3013-comp-intelligence/cnnBase.ipynb#X16sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m     optimizer\u001b[39m.\u001b[39mzero_grad()\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\tqdm\\notebook.py:249\u001b[0m, in \u001b[0;36mtqdm_notebook.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    247\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    248\u001b[0m     it \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39m(tqdm_notebook, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__iter__\u001b[39m()\n\u001b[1;32m--> 249\u001b[0m     \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m it:\n\u001b[0;32m    250\u001b[0m         \u001b[39m# return super(tqdm...) will not catch exception\u001b[39;00m\n\u001b[0;32m    251\u001b[0m         \u001b[39myield\u001b[39;00m obj\n\u001b[0;32m    252\u001b[0m \u001b[39m# NB: except ... [ as ...] breaks IPython async KeyboardInterrupt\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\tqdm\\std.py:1182\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1179\u001b[0m time \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_time\n\u001b[0;32m   1181\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 1182\u001b[0m     \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m iterable:\n\u001b[0;32m   1183\u001b[0m         \u001b[39myield\u001b[39;00m obj\n\u001b[0;32m   1184\u001b[0m         \u001b[39m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[0;32m   1185\u001b[0m         \u001b[39m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:438\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    436\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterator\n\u001b[0;32m    437\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 438\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_iterator()\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:386\u001b[0m, in \u001b[0;36mDataLoader._get_iterator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    384\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    385\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_worker_number_rationality()\n\u001b[1;32m--> 386\u001b[0m     \u001b[39mreturn\u001b[39;00m _MultiProcessingDataLoaderIter(\u001b[39mself\u001b[39;49m)\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1039\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter.__init__\u001b[1;34m(self, loader)\u001b[0m\n\u001b[0;32m   1032\u001b[0m w\u001b[39m.\u001b[39mdaemon \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m   1033\u001b[0m \u001b[39m# NB: Process.start() actually take some time as it needs to\u001b[39;00m\n\u001b[0;32m   1034\u001b[0m \u001b[39m#     start a process and pass the arguments over via a pipe.\u001b[39;00m\n\u001b[0;32m   1035\u001b[0m \u001b[39m#     Therefore, we only add a worker to self._workers list after\u001b[39;00m\n\u001b[0;32m   1036\u001b[0m \u001b[39m#     it started, so that we do not call .join() if program dies\u001b[39;00m\n\u001b[0;32m   1037\u001b[0m \u001b[39m#     before it starts, and __del__ tries to join but will get:\u001b[39;00m\n\u001b[0;32m   1038\u001b[0m \u001b[39m#     AssertionError: can only join a started process.\u001b[39;00m\n\u001b[1;32m-> 1039\u001b[0m w\u001b[39m.\u001b[39;49mstart()\n\u001b[0;32m   1040\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_index_queues\u001b[39m.\u001b[39mappend(index_queue)\n\u001b[0;32m   1041\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_workers\u001b[39m.\u001b[39mappend(w)\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\multiprocessing\\process.py:121\u001b[0m, in \u001b[0;36mBaseProcess.start\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mnot\u001b[39;00m _current_process\u001b[39m.\u001b[39m_config\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mdaemon\u001b[39m\u001b[39m'\u001b[39m), \\\n\u001b[0;32m    119\u001b[0m        \u001b[39m'\u001b[39m\u001b[39mdaemonic processes are not allowed to have children\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m    120\u001b[0m _cleanup()\n\u001b[1;32m--> 121\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_popen \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_Popen(\u001b[39mself\u001b[39;49m)\n\u001b[0;32m    122\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sentinel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_popen\u001b[39m.\u001b[39msentinel\n\u001b[0;32m    123\u001b[0m \u001b[39m# Avoid a refcycle if the target function holds an indirect\u001b[39;00m\n\u001b[0;32m    124\u001b[0m \u001b[39m# reference to the process object (see bpo-30775)\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\multiprocessing\\context.py:224\u001b[0m, in \u001b[0;36mProcess._Popen\u001b[1;34m(process_obj)\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[0;32m    223\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_Popen\u001b[39m(process_obj):\n\u001b[1;32m--> 224\u001b[0m     \u001b[39mreturn\u001b[39;00m _default_context\u001b[39m.\u001b[39;49mget_context()\u001b[39m.\u001b[39;49mProcess\u001b[39m.\u001b[39;49m_Popen(process_obj)\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\multiprocessing\\context.py:336\u001b[0m, in \u001b[0;36mSpawnProcess._Popen\u001b[1;34m(process_obj)\u001b[0m\n\u001b[0;32m    333\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[0;32m    334\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_Popen\u001b[39m(process_obj):\n\u001b[0;32m    335\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mpopen_spawn_win32\u001b[39;00m \u001b[39mimport\u001b[39;00m Popen\n\u001b[1;32m--> 336\u001b[0m     \u001b[39mreturn\u001b[39;00m Popen(process_obj)\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\multiprocessing\\popen_spawn_win32.py:94\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[1;34m(self, process_obj)\u001b[0m\n\u001b[0;32m     92\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     reduction\u001b[39m.\u001b[39mdump(prep_data, to_child)\n\u001b[1;32m---> 94\u001b[0m     reduction\u001b[39m.\u001b[39;49mdump(process_obj, to_child)\n\u001b[0;32m     95\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     96\u001b[0m     set_spawning_popen(\u001b[39mNone\u001b[39;00m)\n",
-      "File \u001b[1;32mc:\\Users\\Main\\anaconda3\\envs\\PyTorch\\Lib\\multiprocessing\\reduction.py:60\u001b[0m, in \u001b[0;36mdump\u001b[1;34m(obj, file, protocol)\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdump\u001b[39m(obj, file, protocol\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m     59\u001b[0m \u001b[39m    \u001b[39m\u001b[39m'''Replacement for pickle.dump() using ForkingPickler.'''\u001b[39;00m\n\u001b[1;32m---> 60\u001b[0m     ForkingPickler(file, protocol)\u001b[39m.\u001b[39;49mdump(obj)\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EarlyStopping counter: 1 out of 5\n",
+      "Epoch 4/ 5, Training Loss: 1.6412580858577381, Validation Loss: 1.6224672695000966, Validation Accuracy: 0.42733333333333334, Learning Rate: 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48a6b65b9cda4727b04fa021a8746a83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EarlyStopping counter: 2 out of 5\n",
+      "Epoch 5/ 5, Training Loss: 1.6375105966221202, Validation Loss: 1.6330074965953827, Validation Accuracy: 0.4126666666666667, Learning Rate: 0.001\n",
+      "Finished Training\n",
+      "SGD\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9b3482fd49941839fe80a4029293689",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation loss decreased (inf --> 1.628651).  Saving model ...\n",
+      "Epoch 1/ 5, Training Loss: 1.649433690851385, Validation Loss: 1.628651464978854, Validation Accuracy: 0.422, Learning Rate: 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14dfcd65de9041a1b9630955220d7159",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation loss decreased (1.628651 --> 1.610876).  Saving model ...\n",
+      "Epoch 2/ 5, Training Loss: 1.6398949428038163, Validation Loss: 1.610875775416692, Validation Accuracy: 0.42533333333333334, Learning Rate: 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8347a3ece740420b8fbebd9c97ff3e38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EarlyStopping counter: 1 out of 5\n",
+      "Epoch 3/ 5, Training Loss: 1.6276560068130492, Validation Loss: 1.623688777287801, Validation Accuracy: 0.4066666666666667, Learning Rate: 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1435a23c3e148e6b9f6e61e311746c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EarlyStopping counter: 2 out of 5\n",
+      "Epoch 4/ 5, Training Loss: 1.6404943401163274, Validation Loss: 1.6387872894605, Validation Accuracy: 0.41933333333333334, Learning Rate: 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aeb02664758d438db7594d23cc90e9d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation loss decreased (1.610876 --> 1.608744).  Saving model ...\n",
+      "Epoch 5/ 5, Training Loss: 1.6563103805888784, Validation Loss: 1.60874438782533, Validation Accuracy: 0.43066666666666664, Learning Rate: 0.001\n",
+      "Finished Training\n",
+      "______________________________________\n",
+      "RMSProp\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c82e72274144ee0bac92435b9116791",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation loss decreased (inf --> 1.606133).  Saving model ...\n",
+      "Epoch 1/ 5, Training Loss: 1.630264459956776, Validation Loss: 1.6061329593261082, Validation Accuracy: 0.42333333333333334, Learning Rate: 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81dd25c17aa8449da65dd1f57deba03d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EarlyStopping counter: 1 out of 5\n",
+      "Epoch 2/ 5, Training Loss: 1.6349259051409635, Validation Loss: 1.6131643205881119, Validation Accuracy: 0.43333333333333335, Learning Rate: 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d4a54b4b2aa498dad101f06a079d186",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EarlyStopping counter: 2 out of 5\n",
+      "Epoch 3/ 5, Training Loss: 1.633270805532282, Validation Loss: 1.6116455644369125, Validation Accuracy: 0.43133333333333335, Learning Rate: 0.001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "edbac7182ad24c4199d05f277bd50c21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 00004: reducing learning rate of group 0 to 1.0000e-04.\n",
+      "EarlyStopping counter: 3 out of 5\n",
+      "Epoch 4/ 5, Training Loss: 1.6294963294809515, Validation Loss: 1.6126659065485, Validation Accuracy: 0.43333333333333335, Learning Rate: 0.0001\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff37a2571d25475f86760319aaeecf53",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/55 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EarlyStopping counter: 4 out of 5\n",
+      "Epoch 5/ 5, Training Loss: 1.6229591326280073, Validation Loss: 1.6171564360459645, Validation Accuracy: 0.42866666666666664, Learning Rate: 0.0001\n",
+      "Finished Training\n"
      ]
     }
    ],
    "source": [
     "import torch.nn.init as init\n",
-    "import copy\n",
+    "\n",
     "def freeze_layers(model, num_layers_to_freeze):\n",
+    "    # Freeze all layers\n",
     "    for param in model.parameters():\n",
     "        param.requires_grad = False\n",
     "\n",
-    "    # Convert the generator to a list and then unfreeze the last num_layers_to_freeze layers\n",
+    "    # Unfreeze the last num_layers_to_freeze layers\n",
     "    for param in list(model.parameters())[-num_layers_to_freeze:]:\n",
     "        param.requires_grad = True\n",
     "\n",
     "def randomize_last_layer_weights(model):\n",
-    "    for param in model.parameters():\n",
-    "        if param.requires_grad:\n",
-    "            if len(param.data.shape) > 1:\n",
-    "                # Apply weight initialization for layers with weights (excluding biases)\n",
-    "                init.normal_(param.data, mean=0, std=0.01)\n",
+    "    last_layer = None\n",
     "\n",
-    "freeze_layers(SGDModel, num_layers_to_freeze=1)\n",
-    "randomize_last_layer_weights(SGDModel)\n",
+    "    # Find the last layer in the model\n",
+    "    for layer in model.children():\n",
+    "        last_layer = layer\n",
+    "\n",
+    "    # Randomize the weights of the last layer\n",
+    "    if last_layer is not None:\n",
+    "        for param in last_layer.parameters():\n",
+    "            if param.requires_grad:\n",
+    "                if len(param.data.shape) > 1:\n",
+    "                    # Apply weight initialization for layers with weights (excluding biases)\n",
+    "                    init.normal_(param.data, mean=0, std=0.01)\n",
+    "\n",
+    "freeze_layers(model1, num_layers_to_freeze=1)\n",
+    "randomize_last_layer_weights(model1)\n",
+    "freeze_layers(model2, num_layers_to_freeze=1)\n",
+    "randomize_last_layer_weights(model2)\n",
+    "freeze_layers(model3, num_layers_to_freeze=1)\n",
+    "randomize_last_layer_weights(model3)\n",
     "\n",
-    "PATH = './cifar_net.pth'\n",
-    "torch.save(RMSPropModel.state_dict(), PATH)\n",
     "\n",
     "# Continue with your training code as before\n",
     "criterion = nn.CrossEntropyLoss()\n",
     "\n",
-    "model1 = copy.deepcopy(RMSPropModel)\n",
-    "model2 = copy.deepcopy(RMSPropModel)\n",
-    "\n",
     "# SGD\n",
     "optimizer_SGD = optim.SGD(model1.parameters(), lr=0.001)\n",
     "scheduler_SGD = optim.lr_scheduler.ReduceLROnPlateau(optimizer_SGD, mode='min', patience=2, factor=0.1, verbose=True)\n",
@@ -488,19 +830,29 @@
     "optimizer_RMSProp = optim.RMSprop(model2.parameters(), lr=0.001)\n",
     "scheduler_RMSProp = optim.lr_scheduler.ReduceLROnPlateau(optimizer_RMSProp, mode='min', patience=2, factor=0.1, verbose=True)\n",
     "\n",
+    "# Adam\n",
+    "optimizer_Adam = optim.Adam(model3.parameters(), lr=0.001)\n",
+    "scheduler_Adam = optim.lr_scheduler.ReduceLROnPlateau(optimizer_Adam, mode='min', patience=2, factor=0.1, verbose=True)\n",
+    "\n",
     "# Train with different optimization algorithms\n",
-    "train_losses_SGD, val_losses_SGD, val_accuracies_SGD = train_model(model1, train_loader, val_loader, criterion, optimizer_SGD, scheduler_SGD)\n",
-    "#train_losses_RMSProp, val_losses_RMSProp, val_accuracies_RMSProp = train_model(SGDModel2, train_loader, val_loader, criterion, optimizer_RMSProp, scheduler_RMSProp)\n"
+    "print(\"______________________________________\")\n",
+    "print(\"Adam\")\n",
+    "train_losses_Adam, val_losses_Adam, val_accuracies_Adam = train_model(model3, train_loader, val_loader, criterion, optimizer_Adam, scheduler_Adam,num_epochs=5)\n",
+    "print(\"SGD\")\n",
+    "train_losses_SGD, val_losses_SGD, val_accuracies_SGD = train_model(model1, train_loader, val_loader, criterion, optimizer_SGD, scheduler_SGD,num_epochs=5)\n",
+    "print(\"______________________________________\")\n",
+    "print(\"RMSProp\")\n",
+    "train_losses_RMSProp, val_losses_RMSProp, val_accuracies_RMSProp = train_model(model2, train_loader, val_loader, criterion, optimizer_RMSProp, scheduler_RMSProp,num_epochs=5)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x400 with 2 Axes>"
       ]
@@ -513,14 +865,17 @@
     "# Plotting\n",
     "plt.figure(figsize=(12, 4))\n",
     "plt.subplot(1, 2, 1)\n",
-    "plt.plot(train_losses, label='Training Loss')\n",
-    "plt.plot(val_losses, label='Validation Loss')\n",
+    "plt.plot(train_losses_SGD, label='Training Loss, SGD')\n",
+    "plt.plot(val_losses_SGD, label='Validation Loss, SGD')\n",
+    "plt.plot(train_losses_RMSProp, label='Training Loss, RMSProp')\n",
+    "plt.plot(val_losses_RMSProp, label='Validation Loss, RMSProp')\n",
     "plt.xlabel('Epoch')\n",
     "plt.ylabel('Loss')\n",
     "plt.legend()\n",
     "\n",
     "plt.subplot(1, 2, 2)\n",
-    "plt.plot(val_accuracies, label='Validation Accuracy')\n",
+    "plt.plot(val_accuracies_SGD, label='Validation Accuracy, SGD')\n",
+    "plt.plot(val_accuracies_RMSProp, label='Validation Accuracy, RMSProp')\n",
     "plt.xlabel('Epoch')\n",
     "plt.ylabel('Accuracy')\n",
     "plt.legend()\n",
@@ -534,8 +889,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "PATH = './cifar_net.pth'\n",
-    "torch.save(model.state_dict(), PATH)"
+    "torch.save(model1.state_dict(), \"./cifar_SGDModelPost.pth\")\n",
+    "torch.save(model2.state_dict(), \"./cifar_RMSPropModelPost.pth\")"
    ]
   },
   {