PyTorch_models.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c4138700",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbaab9b",
   "metadata": {},
   "source": [
    "If available GPU exists then use it (CUDA), otherwise use CPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27f8c783",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6bce88a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(device.type)\n",
    "\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d439f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import datasets, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7fbcacf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = datasets.MNIST(\n",
    "    root=\"./data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))])\n",
    ")\n",
    "\n",
    "test_dataset = datasets.MNIST(\n",
    "    root=\"./data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c84f8a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, random_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb084c04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the batch size for the data loader\n",
    "batch_size = 48\n",
    "\n",
    "# Split the training dataset into training and validation sets\n",
    "train_size = 55000\n",
    "val_size = 5000\n",
    "train_dataset, val_dataset = random_split(train_dataset,[train_size,val_size])\n",
    "\n",
    "# Create data loaders for the three sets\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f80971f",
   "metadata": {},
   "source": [
    "# Sequential 1:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec17867",
   "metadata": {},
   "source": [
    "##  Model with no hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "382a7105",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d1703dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the neural network model\n",
    "class Sequential_1(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc = nn.Linear(28*28, 10)\n",
    "        \n",
    "    # Define the forward propagation of the model, that is, \n",
    "    # how to return the required model output based on the input X\n",
    "    def forward(self, x):\n",
    "        x = x.reshape(-1, 28*28) #this is flatten\n",
    "        x = self.fc (x) #this is dense\n",
    "        return nn.functional.softmax(x, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "583e3ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2a1c60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# learning_rate for optimizer\n",
    "learning_rate = 0.01 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "99c0b9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential_1(\n",
       "  (fc): Linear(in_features=784, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an instance of the model\n",
    "model = Sequential_1().to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)\n",
    "\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c929737",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86bb9405",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                   [-1, 10]           7,850\n",
      "================================================================\n",
      "Total params: 7,850\n",
      "Trainable params: 7,850\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.03\n",
      "Estimated Total Size (MB): 0.03\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model, (1, 784), device=device.type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "661436e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "484e227c",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files/Graphviz/bin/'\n",
    "\n",
    "#check 'https://graphviz.org/download/' first, then add the bin path to the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b488ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchviz import make_dot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6c6817d",
   "metadata": {},
   "outputs": [
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   "source": [
    "x = torch.rand(1, 28*28).to(device)\n",
    "y_hat = model(x)\n",
    "\n",
    "make_dot(y_hat, params=dict(model.named_parameters()))\n",
    "#make_dot(y_hat, params=dict(model.named_parameters()), show_attrs=True, show_saved=True)"
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  {
   "cell_type": "code",
   "execution_count": 20,
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   "source": [
    "# pip install graphviz \n",
    "# pip install torchview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7ae614c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "from torchview import draw_graph"
   ]
  },
  {
   "cell_type": "code",
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   "id": "3edb6a0d",
   "metadata": {},
   "outputs": [
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       "</g>\n",
       "<!-- 3 -->\n",
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       "<title>3</title>\n",
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       "<text text-anchor=\"start\" x=\"57.5\" y=\"-76\" font-family=\"Linux libertine\" font-size=\"10.00\">output: </text>\n",
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       "</g>\n",
       "<!-- 2&#45;&gt;3 -->\n",
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       "<title>2&#45;&gt;3</title>\n",
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       "<text text-anchor=\"start\" x=\"25\" y=\"-19\" font-family=\"Linux libertine\" font-size=\"10.00\">output&#45;tensor</text>\n",
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       "</g>\n",
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       "<title>3&#45;&gt;4</title>\n",
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      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x2a053f8e250>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_graph = draw_graph(model, input_size=(1,28,28), expand_nested=True)\n",
    "\n",
    "model_graph.visual_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "036145ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train Loss: 1.665189, Valid Loss: 1.593383, Valid Accuracy=0.891400\n",
      "Epoch 2: Train Loss: 1.581613, Valid Loss: 1.576595, Valid Accuracy=0.904600\n",
      "Epoch 3: Train Loss: 1.570005, Valid Loss: 1.567957, Valid Accuracy=0.907000\n",
      "Epoch 4: Train Loss: 1.563885, Valid Loss: 1.565962, Valid Accuracy=0.909400\n",
      "Epoch 5: Train Loss: 1.559439, Valid Loss: 1.561511, Valid Accuracy=0.911200\n",
      "Epoch 6: Train Loss: 1.556422, Valid Loss: 1.560310, Valid Accuracy=0.914000\n",
      "Epoch 7: Train Loss: 1.554186, Valid Loss: 1.557373, Valid Accuracy=0.914400\n",
      "Epoch 8: Train Loss: 1.552088, Valid Loss: 1.557217, Valid Accuracy=0.914000\n",
      "Epoch 9: Train Loss: 1.550431, Valid Loss: 1.554116, Valid Accuracy=0.916600\n",
      "Epoch 10: Train Loss: 1.549028, Valid Loss: 1.555374, Valid Accuracy=0.912600\n",
      "\n",
      "Training is completed.\n"
     ]
    }
   ],
   "source": [
    "train_loss_list = []\n",
    "val_loss_list = []\n",
    "val_accuracy_list = []\n",
    "\n",
    "num_epochs = 10\n",
    "\n",
    "# Train the model\n",
    "best_val_loss = float('inf')\n",
    "for epoch in range(num_epochs):\n",
    "    train_loss = 0\n",
    "    model.train()\n",
    "    for i, (data, target) in enumerate(train_loader):   # for data, target in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        data, target = data.to(device), target.to(device)  \n",
    "        output = model(data)\n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item() * len(data)\n",
    "\n",
    "    # Calculate validation loss\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    val_loss = 0\n",
    "    for data, target in val_loader:\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        with torch.no_grad():\n",
    "            output = model(data)\n",
    "            loss = criterion(output, target)\n",
    "        val_loss += loss.item() * len(data)\n",
    "        pred = output.argmax(dim=1, keepdim=True)\n",
    "        correct += pred.eq(target.view_as(pred)).sum().item()   #How many elements are correct? (predicted == labels).sum().item()\n",
    "\n",
    "    # Print training and validation loss , avg\n",
    "    with torch.no_grad():\n",
    "        train_loss /= len(train_loader.dataset)\n",
    "        val_loss /= len(val_loader.dataset)\n",
    "        val_accuracy = correct / len(val_loader.dataset)\n",
    "        print(f'Epoch {epoch+1}: Train Loss: {train_loss:.6f}, Valid Loss: {val_loss:.6f}, Valid Accuracy={val_accuracy:.6f}')\n",
    "\n",
    "        train_loss_list.append(train_loss)\n",
    "        val_loss_list.append(val_loss)\n",
    "        val_accuracy_list.append(val_accuracy)    \n",
    "\n",
    "        # Check if this is the best model so far based on validation loss\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            best_model_state = model.state_dict().copy()\n",
    "        \n",
    "print('\\nTraining is completed.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb6b94fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3436bafb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss_list, label='Train Loss')\n",
    "plt.plot(val_loss_list, c='r', label='Validation Loss')\n",
    "plt.title('Epoch versus Loss')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "88847986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(val_accuracy_list)\n",
    "plt.title('Epoch versus Validation Accuracy')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aa128826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the best model state\n",
    "model.load_state_dict(best_model_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2ad42e94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.548146, Test Accuracy: 0.920000\n"
     ]
    }
   ],
   "source": [
    "# Test the model on the test set\n",
    "test_loss = 0\n",
    "correct = 0\n",
    "\n",
    "model.eval()\n",
    "for data, target in test_loader:    \n",
    "    data, target = data.to(device), target.to(device)\n",
    "    with torch.no_grad():        \n",
    "        outputs = model(data)\n",
    "        loss = criterion(outputs, target)\n",
    "    test_loss += loss.item() * len(data)\n",
    "    pred = outputs.argmax(dim=1, keepdim=True)\n",
    "    correct += pred.eq(target.view_as(pred)).sum().item()  #(predicted == labels).sum().item()\n",
    "\n",
    "test_loss /= len(test_loader.dataset)\n",
    "test_acc = correct / len(test_loader.dataset)\n",
    "print(f'Test Loss: {test_loss:.6f}, Test Accuracy: {test_acc:.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeed5371",
   "metadata": {},
   "source": [
    "# Sequential 2: \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d84c35",
   "metadata": {},
   "source": [
    "## Model with single hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "78a36dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the neural network model with one hidden layer\n",
    "class Sequential_2(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()        \n",
    "        self.fc2 = nn.Linear(16, 10)\n",
    "        self.fc1 = nn.Linear(28*28, 16)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.reshape(-1, 28*28)\n",
    "        x = self.fc1(x)\n",
    "        x = torch.sigmoid(x) #sigmoid in the hidden layer\n",
    "        out = self.fc2(x) #no sigmoid on the output layer\n",
    "        return torch.softmax(out, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bf9566aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# learning_rate for optimizer\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "df24086a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of the model\n",
    "model = Sequential_2().to(device)\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d3926f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                   [-1, 16]          12,560\n",
      "            Linear-2                   [-1, 10]             170\n",
      "================================================================\n",
      "Total params: 12,730\n",
      "Trainable params: 12,730\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.05\n",
      "Estimated Total Size (MB): 0.05\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model, (1, 28*28), device=device.type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ca3213b6",
   "metadata": {},
   "outputs": [
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    "x = torch.rand(1, 28*28).to(device)\n",
    "y_hat = model(x)\n",
    "\n",
    "make_dot(y_hat, params=dict(model.named_parameters()))\n",
    "#make_dot(y_hat, params=dict(model.named_parameters()), show_attrs=True, show_saved=True)"
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    "model_graph = draw_graph(model, input_size=(1,28,28), expand_nested=True)\n",
    "model_graph.visual_graph"
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   "cell_type": "code",
   "execution_count": 35,
   "id": "ae9b2ace",
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     "text": [
      "Epoch 1: Train Loss=inf, Valid Loss=1.710035, Valid Accuracy=0.826800\n",
      "Epoch 2: Train Loss=inf, Valid Loss=1.654386, Valid Accuracy=0.835200\n",
      "Epoch 3: Train Loss=inf, Valid Loss=1.587789, Valid Accuracy=0.903000\n",
      "Epoch 4: Train Loss=inf, Valid Loss=1.574400, Valid Accuracy=0.912400\n",
      "Epoch 5: Train Loss=inf, Valid Loss=1.567073, Valid Accuracy=0.911600\n",
      "Epoch 6: Train Loss=inf, Valid Loss=1.558887, Valid Accuracy=0.917000\n",
      "Epoch 7: Train Loss=inf, Valid Loss=1.555129, Valid Accuracy=0.918600\n",
      "Epoch 8: Train Loss=inf, Valid Loss=1.556666, Valid Accuracy=0.917800\n",
      "Epoch 9: Train Loss=inf, Valid Loss=1.548217, Valid Accuracy=0.923600\n",
      "Epoch 10: Train Loss=inf, Valid Loss=1.545630, Valid Accuracy=0.925600\n",
      "Epoch 11: Train Loss=inf, Valid Loss=1.545749, Valid Accuracy=0.925600\n",
      "Epoch 12: Train Loss=inf, Valid Loss=1.547785, Valid Accuracy=0.920800\n",
      "Epoch 13: Train Loss=inf, Valid Loss=1.541543, Valid Accuracy=0.928000\n",
      "Epoch 14: Train Loss=inf, Valid Loss=1.538880, Valid Accuracy=0.930400\n",
      "Epoch 15: Train Loss=inf, Valid Loss=1.538218, Valid Accuracy=0.930200\n",
      "Epoch 16: Train Loss=inf, Valid Loss=1.538152, Valid Accuracy=0.929200\n",
      "Epoch 17: Train Loss=inf, Valid Loss=1.538459, Valid Accuracy=0.928000\n",
      "Epoch 18: Train Loss=inf, Valid Loss=1.536330, Valid Accuracy=0.931200\n",
      "Epoch 19: Train Loss=inf, Valid Loss=1.535244, Valid Accuracy=0.930200\n",
      "Epoch 20: Train Loss=inf, Valid Loss=1.533938, Valid Accuracy=0.932800\n",
      "\n",
      "Training is completed.\n"
     ]
    }
   ],
   "source": [
    "# Train the model and select the best model based on validation loss\n",
    "train_loss_list = []\n",
    "val_loss_list = []\n",
    "val_accuracy_list = []\n",
    "\n",
    "num_epochs = 20\n",
    "\n",
    "best_val_loss = float('inf')\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    train_loss = float('inf')\n",
    "    for data, target in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        output = model(data)\n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item() * len(data)\n",
    "    \n",
    "    # Evaluate the model on the validation set\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    val_loss = 0\n",
    "    for data, target in val_loader:\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        with torch.no_grad():\n",
    "            output = model(data)\n",
    "            val_loss += criterion(output, target).item() * len(data)\n",
    "        pred = output.argmax(dim=1, keepdim=True)\n",
    "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "            \n",
    "    with torch.no_grad():\n",
    "        train_loss /= len(train_loader.dataset)\n",
    "        val_loss /= len(val_loader.dataset)\n",
    "        val_accuracy = correct / len(val_loader.dataset)        \n",
    "        print(f'Epoch {epoch+1}: Train Loss={train_loss:.6f}, Valid Loss={val_loss:.6f}, Valid Accuracy={val_accuracy:.6f}')\n",
    "\n",
    "        train_loss_list.append(train_loss)\n",
    "        val_loss_list.append(val_loss)\n",
    "        val_accuracy_list.append(val_accuracy)    \n",
    "\n",
    "        # Save the model if it has the best validation loss so far\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            best_model = model.state_dict().copy()\n",
    "    \n",
    "print('\\nTraining is completed.')    "
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss_list, label='Train Loss')\n",
    "plt.plot(val_loss_list, c='r', label='Validation Loss')\n",
    "plt.title('Epoch versus Loss')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "67bb2fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(val_accuracy_list)\n",
    "plt.title('Epoch versus Validation Accuracy')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "01dc15c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the best model and evaluate it on the test set\n",
    "model.load_state_dict(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a8e5ca28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.528277, Test Accuracy: 0.939300\n"
     ]
    }
   ],
   "source": [
    "# Test the model on the test set\n",
    "test_loss = 0\n",
    "correct = 0\n",
    "\n",
    "model.eval()\n",
    "for data, target in test_loader:    \n",
    "    data, target = data.to(device), target.to(device)\n",
    "    with torch.no_grad():        \n",
    "        outputs = model(data)\n",
    "        loss = criterion(outputs, target)\n",
    "    test_loss += loss.item() * len(data)\n",
    "    pred = outputs.argmax(dim=1, keepdim=True)\n",
    "    correct += pred.eq(target.view_as(pred)).sum().item()  #(predicted == labels).sum().item()\n",
    "\n",
    "test_loss /= len(test_loader.dataset)\n",
    "test_acc = correct / len(test_loader.dataset)\n",
    "print(f'Test Loss: {test_loss:.6f}, Test Accuracy: {test_acc:.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99256413",
   "metadata": {},
   "source": [
    "# Sequential 3: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c168ed",
   "metadata": {},
   "source": [
    "## Deep model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "db7efce6",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Define the neural network model\n",
    "class Sequential_3(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(28*28, 256)\n",
    "        self.fc2 = nn.Linear(256, 128)          \n",
    "        self.fc3 = nn.Linear(128, 96)\n",
    "        self.fc4 = nn.Linear(96, 64)\n",
    "        self.fc5 = nn.Linear(64, 32)\n",
    "        self.fc6 = nn.Linear(32, 16)\n",
    "        self.fc7 = nn.Linear(16, 10)      \n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.reshape(-1, 28*28)\n",
    "        x = self.fc1(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.fc4(x)\n",
    "        x = torch.relu(x)\n",
    "        x = self.fc5(x)\n",
    "        x = torch.relu(x)\n",
    "        x = torch.relu(self.fc6(x))\n",
    "        out = self.fc7(x)\n",
    "        return torch.softmax(out, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f91efb0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# learning_rate for optimizer\n",
    "learning_rate = 0.01 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6f3a6658",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of the model\n",
    "model = Sequential_3().to(device)\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)  # Adam and RMSprop did not perform well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b663cd1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                  [-1, 256]         200,960\n",
      "            Linear-2                  [-1, 128]          32,896\n",
      "            Linear-3                   [-1, 96]          12,384\n",
      "            Linear-4                   [-1, 64]           6,208\n",
      "            Linear-5                   [-1, 32]           2,080\n",
      "            Linear-6                   [-1, 16]             528\n",
      "            Linear-7                   [-1, 10]             170\n",
      "================================================================\n",
      "Total params: 255,226\n",
      "Trainable params: 255,226\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.97\n",
      "Estimated Total Size (MB): 0.98\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model, (1, 784), device=device.type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "55e5cb80",
   "metadata": {},
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   "source": [
    "x = torch.rand(1, 28*28).to(device)\n",
    "y_hat = model(x)\n",
    "\n",
    "make_dot(y_hat, params=dict(model.named_parameters()))\n",
    "#make_dot(y_hat, params=dict(model.named_parameters()), show_attrs=True, show_saved=True)"
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  {
   "cell_type": "code",
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   "id": "2ca4bb9f",
   "metadata": {},
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     "execution_count": 45,
     "metadata": {},
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   "source": [
    "model_graph = draw_graph(model, input_size=(1,28,28), expand_nested=True)\n",
    "model_graph.visual_graph"
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  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "987de777",
   "metadata": {},
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    {
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     "text": [
      "Epoch 1: Train Loss=2.301983, Valid Loss=2.301327, Valid Accuracy=0.114800\n",
      "Epoch 2: Train Loss=2.277719, Valid Loss=2.186384, Valid Accuracy=0.207400\n",
      "Epoch 3: Train Loss=2.059275, Valid Loss=1.813082, Valid Accuracy=0.673000\n",
      "Epoch 4: Train Loss=1.758133, Valid Loss=1.723493, Valid Accuracy=0.738000\n",
      "Epoch 5: Train Loss=1.724435, Valid Loss=1.713237, Valid Accuracy=0.746400\n",
      "Epoch 6: Train Loss=1.709397, Valid Loss=1.696284, Valid Accuracy=0.754600\n",
      "Epoch 7: Train Loss=1.603781, Valid Loss=1.554509, Valid Accuracy=0.908800\n",
      "Epoch 8: Train Loss=1.546641, Valid Loss=1.546195, Valid Accuracy=0.914600\n",
      "Epoch 9: Train Loss=1.538701, Valid Loss=1.550313, Valid Accuracy=0.910400\n",
      "Epoch 10: Train Loss=1.533366, Valid Loss=1.576638, Valid Accuracy=0.884000\n",
      "Epoch 11: Train Loss=1.531191, Valid Loss=1.529445, Valid Accuracy=0.932400\n",
      "Epoch 12: Train Loss=1.530470, Valid Loss=1.536772, Valid Accuracy=0.923600\n",
      "Epoch 13: Train Loss=1.527772, Valid Loss=1.530409, Valid Accuracy=0.929800\n",
      "Epoch 14: Train Loss=1.527945, Valid Loss=1.538425, Valid Accuracy=0.922400\n",
      "Epoch 15: Train Loss=1.523311, Valid Loss=1.540781, Valid Accuracy=0.920200\n",
      "Epoch 16: Train Loss=1.528801, Valid Loss=1.544795, Valid Accuracy=0.916000\n",
      "Epoch 17: Train Loss=1.521078, Valid Loss=1.520161, Valid Accuracy=0.940000\n",
      "Epoch 18: Train Loss=1.525068, Valid Loss=1.562722, Valid Accuracy=0.898400\n",
      "Epoch 19: Train Loss=1.522061, Valid Loss=1.530725, Valid Accuracy=0.930200\n",
      "Epoch 20: Train Loss=1.519842, Valid Loss=1.527803, Valid Accuracy=0.932800\n",
      "\n",
      "Training is completed.\n"
     ]
    }
   ],
   "source": [
    "# Train the model and select the best model based on validation loss\n",
    "train_loss_list = []\n",
    "val_loss_list = []\n",
    "val_accuracy_list = []\n",
    "\n",
    "num_epochs = 20\n",
    "\n",
    "best_val_loss = float('inf')\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "    for data, target in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        output = model(data)\n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item() * len(data)\n",
    "    \n",
    "    # Evaluate the model on the validation set\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    val_loss = 0\n",
    "    for data, target in val_loader:\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        with torch.no_grad():\n",
    "            output = model(data)\n",
    "            val_loss += criterion(output, target).item() * len(data)\n",
    "        pred = output.argmax(dim=1, keepdim=True)\n",
    "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        train_loss /= len(train_loader.dataset)\n",
    "        val_loss /= len(val_loader.dataset)\n",
    "        val_accuracy = correct / len(val_loader.dataset)        \n",
    "        print(f'Epoch {epoch+1}: Train Loss={train_loss:.6f}, Valid Loss={val_loss:.6f}, Valid Accuracy={val_accuracy:.6f}')\n",
    "    \n",
    "        train_loss_list.append(train_loss)\n",
    "        val_loss_list.append(val_loss)\n",
    "        val_accuracy_list.append(val_accuracy)    \n",
    "\n",
    "        # Save the model if it has the best validation loss so far\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            best_model = model.state_dict().copy()\n",
    "    \n",
    "print('\\nTraining is completed.')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d94fc9a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss_list, label='Train Loss')\n",
    "plt.plot(val_loss_list, c='r', label='Validation Loss')\n",
    "plt.title('Epoch versus Loss')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "248a0a74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(val_accuracy_list)\n",
    "plt.title('Epoch versus Validation Accuracy')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "633fdc83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the best model and evaluate it on the test set\n",
    "model.load_state_dict(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e0da8c94",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.523422, Test Accuracy: 0.937500\n"
     ]
    }
   ],
   "source": [
    "# Test the model on the test set\n",
    "test_loss = 0\n",
    "correct = 0\n",
    "\n",
    "model.eval()\n",
    "for data, target in test_loader:    \n",
    "    data, target = data.to(device), target.to(device)\n",
    "    with torch.no_grad():        \n",
    "        outputs = model(data)\n",
    "        loss = criterion(outputs, target)\n",
    "    test_loss += loss.item() * len(data)\n",
    "    pred = outputs.argmax(dim=1, keepdim=True)\n",
    "    correct += pred.eq(target.view_as(pred)).sum().item()  #(predicted == labels).sum().item()\n",
    "\n",
    "test_loss /= len(test_loader.dataset)\n",
    "test_acc = correct / len(test_loader.dataset)\n",
    "print(f'Test Loss: {test_loss:.6f}, Test Accuracy: {test_acc:.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235d1691",
   "metadata": {},
   "source": [
    "# Deep model using class extended from nn.Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "654ba042",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Define the neural network model\n",
    "class Complex_deep_model(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.hidden_left_1 = nn.Linear(28*28, 256)\n",
    "        self.hidden_left_2 = nn.Linear(256, 128)\n",
    "        self.hidden_left_3 = nn.Linear(128, 64)\n",
    "        self.hidden_left_4 = nn.Linear(64, 32)\n",
    "\n",
    "        self.hidden_right_1 = nn.Linear(28*28, 256)\n",
    "        self.hidden_right_2 = nn.Linear(256, 128)\n",
    "        self.hidden_right_3 = nn.Linear(128, 32)\n",
    "        \n",
    "        self.hidden_center_1 = nn.Linear(32+32, 24) \n",
    "        self.hidden_center_2 = nn.Linear(24, 16)\n",
    "        self.output = nn.Linear(16, 10)\n",
    "\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        \n",
    "        x_left = torch.relu(self.hidden_left_1(x))\n",
    "        x_left = torch.relu(self.hidden_left_2(x_left))\n",
    "        x_left = torch.relu(self.hidden_left_3(x_left))\n",
    "        x_left = torch.relu(self.hidden_left_4(x_left))\n",
    "        \n",
    "        x_right = torch.sigmoid(self.hidden_right_1(x))\n",
    "        x_right = torch.sigmoid(self.hidden_right_2(x_right))\n",
    "        x_right = torch.sigmoid(self.hidden_right_3(x_right))\n",
    "\n",
    "        x = torch.cat((x_left, x_right), dim=1) #concatenate parallel layers\n",
    "\n",
    "        x = torch.relu(self.hidden_center_1(x))\n",
    "        x = torch.relu(self.hidden_center_2(x))\n",
    "        x = self.output(x)\n",
    "        \n",
    "        return torch.softmax(x, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e653b7e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# learning_rate for optimizer\n",
    "learning_rate = 0.001 # very bad accuracy with 0.001 for 25 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "de580cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of the model\n",
    "model = Complex_deep_model().to(device)\n",
    "\n",
    "# Define the loss function and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1e2f144e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "           Flatten-1                  [-1, 784]               0\n",
      "            Linear-2                  [-1, 256]         200,960\n",
      "            Linear-3                  [-1, 128]          32,896\n",
      "            Linear-4                   [-1, 64]           8,256\n",
      "            Linear-5                   [-1, 32]           2,080\n",
      "            Linear-6                  [-1, 256]         200,960\n",
      "            Linear-7                  [-1, 128]          32,896\n",
      "            Linear-8                   [-1, 32]           4,128\n",
      "            Linear-9                   [-1, 24]           1,560\n",
      "           Linear-10                   [-1, 16]             400\n",
      "           Linear-11                   [-1, 10]             170\n",
      "================================================================\n",
      "Total params: 484,306\n",
      "Trainable params: 484,306\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.01\n",
      "Params size (MB): 1.85\n",
      "Estimated Total Size (MB): 1.86\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model, (1, 784), device=device.type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aea0f879",
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "x = torch.rand(1, 28*28).to(device)\n",
    "y_hat = model(x)\n",
    "\n",
    "make_dot(y_hat, params=dict(model.named_parameters()))\n",
    "#make_dot(y_hat, params=dict(model.named_parameters()), show_attrs=True, show_saved=True)"
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  {
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   "id": "5e98e23b",
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     "execution_count": 56,
     "metadata": {},
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   ],
   "source": [
    "model_graph = draw_graph(model, input_size=(1,28,28), expand_nested=True)\n",
    "model_graph.visual_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "743a91dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train Loss=1.830185, Valid Loss=1.667800, Valid Accuracy=0.800200\n",
      "Epoch 2: Train Loss=1.586349, Valid Loss=1.564192, Valid Accuracy=0.898400\n",
      "Epoch 3: Train Loss=1.554872, Valid Loss=1.551214, Valid Accuracy=0.910800\n",
      "Epoch 4: Train Loss=1.543750, Valid Loss=1.538541, Valid Accuracy=0.923000\n",
      "Epoch 5: Train Loss=1.538382, Valid Loss=1.553625, Valid Accuracy=0.907800\n",
      "Epoch 6: Train Loss=1.537542, Valid Loss=1.528653, Valid Accuracy=0.933000\n",
      "Epoch 7: Train Loss=1.533310, Valid Loss=1.536136, Valid Accuracy=0.925000\n",
      "Epoch 8: Train Loss=1.528861, Valid Loss=1.528965, Valid Accuracy=0.932600\n",
      "Epoch 9: Train Loss=1.533332, Valid Loss=1.547981, Valid Accuracy=0.912200\n",
      "Epoch 10: Train Loss=1.530188, Valid Loss=1.556681, Valid Accuracy=0.904200\n",
      "\n",
      "Training is completed.\n"
     ]
    }
   ],
   "source": [
    "# Train the model and select the best model based on validation loss\n",
    "train_loss_list = []\n",
    "val_loss_list = []\n",
    "val_accuracy_list = []\n",
    "\n",
    "num_epochs = 10\n",
    "\n",
    "best_val_loss = float('inf')\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "    for data, target in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        data, target = data.to(device),target.to(device)\n",
    "        outputs = model(data)\n",
    "        loss = criterion(outputs, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item() * len(data)\n",
    "    \n",
    "    # Evaluate the model on the validation set\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    val_loss = 0\n",
    "    for data, target in val_loader:\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        with torch.no_grad():\n",
    "            output = model(data)\n",
    "            val_loss += criterion(output, target).item() * len(data)\n",
    "        pred = output.argmax(dim=1, keepdim=True)\n",
    "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        train_loss /= len(train_loader.dataset)\n",
    "        val_loss /= len(val_loader.dataset)\n",
    "        val_accuracy = correct / len(val_loader.dataset)        \n",
    "        print(f'Epoch {epoch+1}: Train Loss={train_loss:.6f}, Valid Loss={val_loss:.6f}, Valid Accuracy={val_accuracy:.6f}')\n",
    "    \n",
    "        train_loss_list.append(train_loss)\n",
    "        val_loss_list.append(val_loss)\n",
    "        val_accuracy_list.append(val_accuracy)    \n",
    "\n",
    "        # Save the model if it has the best validation loss so far\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            best_model = model.state_dict().copy()\n",
    "    \n",
    "print('\\nTraining is completed.')    "
   ]
  },
  {
   "cell_type": "code",
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   "id": "3ce4c94a",
   "metadata": {},
   "outputs": [
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+vCDD3UO0vHEVEZGNSE1NNTzesGEDXn/9dZw/f96wz9nZ2fBYFEVotVrY2dX+K8/Hx8e8FSWzYQtLDeQyAWv2XcXRLBkuZuRbuzpERI1CFEUUlpQ2+iaKYp3r6O/vb9jc3NwgCILh+blz5+Di4oKtW7eie/fuUCqV2LNnDy5fvoyRI0fCz88Pzs7O6NmzJ/744w+jcv/ZJSQIAlatWoX7778fKpUK4eHh+Omnnxr0/f3+++8RHR0NpVKJkJAQvP/++0avf/zxxwgPD4eDgwP8/PwwZswYw2ubNm1Cp06d4OjoCC8vLwwePBgFBQUNqk9TwRaWGgiCgAh/F+y7fAvn0vLQLcTb2lUiIrK4Io0WUa//3ujnPfPmMKjszfdr6aWXXsJ7772HsLAweHh44Pr16xg+fDjeeustKJVKrFu3DnFxcTh//jzatGlTbTkLFizAu+++i8WLF2PZsmV46KGHcO3aNXh6eppcpyNHjmDcuHGYP38+xo8fj3379uGZZ56Bl5cXJk+ejMOHD2PmzJn46quv0LdvX9y6dQu7d+8GILUqTZgwAe+++y7uv/9+5OXlYffu3SYFvaaMgaUWkWWB5WxqnrWrQkREJnjzzTcxZMgQw3NPT0/ExMQYni9cuBCbN2/GTz/9hOnTp1dbzuTJkzFhwgQAwNtvv42PPvoIBw8exL333mtynT744APcc889eO211wAA7du3x5kzZ7B48WJMnjwZSUlJcHJywr/+9S+4uLggODgYXbt2BSAFltLSUjzwwAMIDg4GAHTq1MnkOjRVDCy1iPB3AQCcTWNgIaKWwVEhx5k3h1nlvObUo0cPo+f5+fmYP38+fvnlF8Mv/6KiIiQlJdVYTufOnQ2PnZyc4OrqioyMjHrV6ezZsxg5cqTRvn79+mHJkiXQarUYMmQIgoODERYWhnvvvRf33nuvoTsqJiYG99xzDzp16oRhw4Zh6NChGDNmDDw8POpVl6aGY1hqoQ8s59PzW0yzGxG1bIIgQGVv1+ibue9h5OTkZPT8hRdewObNm/H2229j9+7dSEhIQKdOnVBSUlJjOf+ccCEIAnQ6nVnrqufi4oKjR4/im2++QUBAAF5//XXExMQgOzsbcrkc8fHx2Lp1K6KiorBs2TJ06NABiYmJFqmLrWFgqUWYtxPkgoi84lLcuF1k7eoQEVE97d27F5MnT8b999+PTp06wd/fH1evXm3UOkRGRmLv3r2V6tW+fXvI5VILk52dHQYPHox3330XJ06cwNWrV/Hnn38CkMJSv379sGDBAhw7dgz29vbYvHlzo34Ga2GXUC3s7WTwdwSSC4GzqbkI8uTNwYiImqLw8HD88MMPiIuLgyAIeO211yzWUnLz5k0kJCRAp9OhoKAATk5OaNWqFZ5//nn07NkTCxcuxPjx4/H3339j+fLl+PjjjwEAP//8M65cuYIBAwbAw8MDv/76K3Q6HTp06IADBw5g+/btGDp0KHx9fXHgwAHcvHkTkZGRFvkMtoaBpQ5aOYlILhRwJjUXQ6P9rV0dIiKqhw8++ABTpkxB37594e3tjblz5yI3N9ci51q/fj3Wr19vtG/hwoV49dVX8d133+H111/HwoULERAQgDfffBOTJ08GALi7u+OHH37A/PnzUVxcjPDwcHzzzTeIjo7G2bNnsWvXLixZsgS5ubkIDg7G+++/j9jYWIt8BlvDwFIHgSpp7MrZVMv8YBMRUf1NnjzZ8AsfkFaarWrMYUhIiKFrRW/atGlGz//ZRVRVOdnZ2TXW56+//jI81ul0yM3Nhaurq+GOxaNHj8bo0aOrfG///v2N3l9RZGQkfvvttxrP3ZxxDEsdtCobt8WpzURERNbBwFIHrcpaWJJuFSKvWGPl2hAREbU8DCx14KQA/F2VAIDzXI+FiIio0TGw1JF+PZYzHMdCRETU6BhY6ihSv+ItAwsREVGjY2Cpo8gAfQsLu4SIiIgaGwNLHRmW6E/LhVbHJfqJiIgaEwNLHbXxVMFRIUexRoerWQXWrg4REVGLwsBSR3KZgA76gbcpHMdCRETUmBhYTBAZ4AqAA2+JiJqLgQMHYtasWYbnISEhWLJkSY3vEQQBW7ZsafC5zVVOS8HAYoKoQAYWIiJbEBcXh3vvvbfK13bv3g1BEHDixAmTyz106BCeeOKJhlbPyH//+19069at0v7U1FSL3wdo7dq1cHd3t+g5GgsDiwmiAvRTmzlTiIjImqZOnYr4+HjcuHGj0mtffPEFevTogc6dO5tcro+PD1QqlTmqWCt/f38olcpGOVdzwMBigg7+UgtLWm4xbhWUWLk2REQWIopAQUHjb1XcaLA6//rXv+Dj44O1a9ca7c/Pz8fGjRsxdepUZGVlYcKECWjVqhVUKhU6deqEb775psZy/9kldPHiRQwYMAAODg6IiopCfHx8pffMnTsX7du3h0qlQlhYGF577TVoNNJtXNauXYt33nkHx48fhyAIEATBUOd/dgmdPHkSd999NxwdHeHl5YUnnngC+fn5htcnT56MUaNG4b333kNAQAC8vLwwbdo0w7nqIykpCSNHjoSzszNcXV0xbtw4pKenG14/fvw4Bg0aBBcXF7i6uqJ79+44fPgwAODatWuIi4uDh4cHnJycEB0djV9//bXedakN79ZsAmelHYK9VLiWVYizqbno187b2lUiIjK/wkLA2bnxz5ufDzg51elQOzs7TJw4EWvXrsUrr7wCQRAAABs3boRWq8WECROQn5+P7t27Y+7cuXB1dcUvv/yCRx55BG3btkWvXr1qPYdOp8MDDzwAPz8/HDhwADk5OUbjXfRcXFywdu1aBAYG4uTJk3j88cfh4uKCF198EePHj8exY8ewY8cO/PHHHwAANze3SmUUFBRg2LBh6NOnDw4dOoSMjAw89thjmD59ulEo27FjBwICArBjxw5cunQJ48ePR5cuXfD444/X6fv2z8+nDys7d+5EaWkppk2bhvHjxxvuGP3QQw+ha9eu+OSTTyCXy5GQkACFQgFAutN1SUkJdu3aBScnJ5w5cwbOFvy5YWAxUaS/KwMLEZENmDJlChYvXoydO3di4MCBAKTuoNGjR8PNzQ1ubm544YUXDMfPmDEDv//+O7777rs6BZY//vgD586dw++//47AwEAAwNtvv11p3Mmrr75qeBwSEoIXXngB3377LV588UU4OjrCyckJdnZ28Pf3r/Zc69evR3FxMdatWwenstC2fPlyxMXF4Z133oGfnx8AwMPDA8uXL4dcLkdERARGjBiB7du31yuwbN++HSdPnkRiYiKCgoIAAOvWrUN0dDQOHTqEnj17IikpCXPmzEFERAQAIDw83PD+pKQkjB49Gp06dQIAhIWFmVwHUzCwmCgq0BW/nU7jPYWIqPlSqaTWDmuc1wQRERHo27cv1qxZg4EDB+LSpUvYvXs33nzzTQCAVqvF22+/je+++w7JyckoKSmBWq2u8xiVs2fPIigoyBBWAKBPnz6VjtuwYQM++ugjXL58Gfn5+SgtLYWrq6tJn+Xs2bOIiYkxhBUA6NevH3Q6Hc6fP28ILNHR0ZDL5YZjAgICcPLkSZPOVfGcQUFBhrACAFFRUXB3d8fZs2fRs2dPzJ49G4899hi++uorDB48GGPHjkXbtm0BADNnzsTTTz+Nbdu2YfDgwRg9enS9xg3VFcewmKh8ajMH3hJRMyUIUtdMY29l3TqmmDp1Kr7//nvk5eXhiy++QNu2bXHXXXcBABYvXoylS5di7ty52LFjBxISEjBs2DCUlJhvDOLff/+Nhx56CMOHD8fPP/+MY8eO4ZVXXjHrOSrSd8foCYIAnU5nkXMBwPz583H69GmMGDECf/75J6KiorB582YAwGOPPYYrV67gkUcewcmTJ9GjRw8sW7bMYnVhYDGR/p5ClzLyUFJquR8SIiKq3bhx4yCTybB+/XqsW7cOU6ZMMYxn2bt3L0aOHImHH34YMTExCAsLw4ULF+pcdmRkJK5fv47U1FTDvv379xsds2/fPgQHB+OVV15Bjx49EB4ejmvXrhkdo1AooNVqaz3X8ePHUVBQvpL63r17IZPJ0KFDhzrX2RT6z3f9+nXDvjNnziA7OxtRUVGGfe3bt8dzzz2Hbdu24YEHHsAXX3xheC0oKAhPPfUUfvjhBzz//PP4/PPPLVJXgIHFZK3cHeHqYAeNVsSlDCs0mRIRkYGzszPGjx+PefPmITU1FZMnTza8Fh4ejvj4eOzbtw9nz57Fk08+aTQDpjaDBw9G+/btMWnSJBw/fhy7d+/GK6+8YnRMeHg4kpKS8O233+Ly5cv46KOPDC0Qem3atEFiYiISEhKQmZkJtVpd6VwPPfQQHBwcMGnSJJw6dQo7duzAjBkz8Mgjjxi6g+pLq9UiISHBaDt79iwGDx6MTp064aGHHsLRo0dx8OBBTJw4EXfddRd69OiBoqIiTJ8+HX/99ReuXbuGvXv34tChQ4iMjAQAzJo1C7///jsSExNx9OhR7Nixw/CaJTCwmEgQBERwxVsiIpsxdepU3L59G8OGDTMab/Lqq6+iW7duGDZsGAYOHAh/f3+MGjWqzuXKZDJs3rwZRUVF6NWrFx577DG89dZbRsfcd999eO655zB9+nR06dIF+/btw2uvvVbpmGHDhmHQoEHw8fGpcmq1SqXC77//jlu3bqFnz54YM2YM7rnnHixfvty0b0YV8vPz0bVrV6MtLi4OgiDgxx9/hIeHBwYMGIDBgwcjLCwMGzZsAADI5XJkZWVh4sSJaN++PcaNG4fY2FgsWLAAgBSEpk2bhsjISNx7771o3749Pv744wbXtzqCKJow8d2G5ebmws3NDTk5OSYPdqqJRqPBr7/+iuHDhxv6Duf/dBpr913FY/1D8eq/omopgcypqutB1sVrYltMvR7FxcVITExEaGgoHBwcGqGGLYtOp0Nubi5cXV0hk7XcNoKafs7q+vvb5O/erl27EBcXh8DAwDrfB+Hrr79GTEwMVCoVAgICMGXKFGRlZRkds3HjRkRERMDBwQGdOnWy6OIzDRWlb2FJYwsLERFRYzA5sBQUFCAmJgYrVqyo0/F79+7FxIkTMXXqVJw+fRobN27EwYMHjeaM79u3DxMmTMDUqVNx7NgxjBo1CqNGjcKpU6dMrV6j0M8UOpOSi2bSQEVERGTTTF6HJTY21qSbNf39998ICQnBzJkzAQChoaF48skn8c477xiOWbp0Ke69917MmTMHALBw4ULEx8dj+fLl+PTTT02tosWF+zlDLhNwu1CD9Fw1/N3YjEpERGRJFl84rk+fPnj55Zfx66+/IjY2FhkZGdi0aROGDx9uOObvv//G7Nmzjd43bNiwGrub1Gq10Ujr3Fype0aj0TTovgr/pC+rYplyAKFeKly6WYCTN27BS+VjtvNRzaq6HmRdvCa2xdTrodFoIIoidDqdRdfzaKn0rfD673FLpdPpIIoiNBqN0cJ3QN1/Vi0eWPr164evv/4a48ePR3FxMUpLSxEXF2fUpZSWllZp2pafnx/S0tKqLXfRokWGkcoVbdu2zSJ32vznDa/cRBkAGX7ceRiFl9gt1NiqugEZWReviW2p6/XQLxmfl5dnscXOCMjLa9mLjarVahQVFWHXrl0oLS01eq2wsLBOZVg8sJw5cwbPPvssXn/9dQwbNgypqamYM2cOnnrqKaxevbre5c6bN8+oVSY3NxdBQUEYOnSo2WcJxcfHY8iQIUYj7m/sTsSRbRchurbC8OGWW4qYjFV3Pch6eE1si6nXQ6vV4sqVK5DJZGb9v5MkoigiLy8PLi4uhgXtWqKsrCw4OjrinnvuqdTCou8hqY3FA8uiRYvQr18/w/iUzp07w8nJCXfeeSf+85//ICAgAP7+/pUW80lPT6/xRlFKpRJKpbLSfoVCYZH/NP9ZbsfWHgCAs+l5/E/aCix1nan+eE1sS12vh0KhgIeHBzIzMyGTyaBSqVr0L1Zz0+l0hnsYtcRpzaIoorCwEJmZmfDw8Khy6nxd/9+weGApLCyEnZ3xafTpSt+316dPH2zfvt3ott3x8fFV3mTKVuiX6L+aWYCiEi0c7eW1vIOIyDbp/zjMyMiwck2aH1EUUVRUBEdHxxYdBN3d3WtshKgLkwNLfn4+Ll26ZHiuX27Y09MTbdq0wbx585CcnIx169YBAOLi4vD444/jk08+MXQJzZo1C7169TKsSPjss8/irrvuwvvvv48RI0bg22+/xeHDh7Fy5coGfThL8nVxgLezPTLzS3A+PQ9dgtytXSUionoRBAEBAQHw9fXl4Gkz02g02LVrFwYMGNBiWyAVCkWlbqD6MDmwHD58GIMGDTI8148jmTRpEtauXYvU1FQkJSUZXp88eTLy8vKwfPlyPP/883B3d8fdd99tNK25b9++WL9+PV599VW8/PLLCA8Px5YtW9CxY8eGfDaLiwxwxe6LmTibmsvAQkRNnlwuN8svFionl8tRWloKBweHFhtYzMXkwDJw4MAaF0tbu3ZtpX0zZszAjBkzaix37NixGDt2rKnVsaqoCoGFiIiILKfljQAyo4or3hIREZHlMLA0gD6wnEvLg07HtViIiIgshYGlAcJ8nGAvlyFfXYobt4usXR0iIqJmi4GlARRyGdr7OwMAznAcCxERkcUwsDRQpL/ULcSBt0RERJbDwNJAhoG3DCxEREQWw8DSQPrAwhYWIiIiy2FgaaCossBy43YRcou5QiQREZElMLA0kJtKgVbujgCAc6kt+/bhRERElsLAYgb6GyGeScmxck2IiIiaJwYWMygfx8IWFiIiIktgYDEDQ2BJ48BbIiIiS2BgMQN9YDmflodSrc7KtSEiImp+GFjMINhTBZW9HOpSHa5mFVi7OkRERM0OA4sZyGQCIvylgbeneedmIiIis2NgMRMOvCUiIrIcBhYz4Yq3RERElsPAYiYMLERERJbDwGImEf4uEAQgI0+NzHy1tatDRETUrDCwmImT0g4hXk4A2MpCRERkbgwsZqRfop+BhYiIyLwYWMwo0p8zhYiIiCyBgcWMOPCWiIjIMhhYzCgqUAoslzLyoS7VWrk2REREzQcDixkFuDnAzVGBUp2Ii+n51q4OERFRs8HAYkaCIHDgLRERkQUwsJgZl+gnIiIyPwYWM4viwFsiIiKzY2AxM0MLS1ouRFG0cm2IiIiaBwYWMwv3c4adTEB2oQapOcXWrg4REVGzwMBiZko7Odr6OANgtxAREZG5MLBYAGcKERERmRcDiwXoF5DjTCEiIiLzYGCxAC7RT0REZF4MLBagDyyJWQUoLCm1cm2IiIiaPgYWC/B2VsLHRQlRBM6lsVuIiIiooRhYLITdQkRERObDwGIhXPGWiIjIfBhYLEQ/tflMCgMLERFRQzGwWIi+heVcWh50Oi7RT0RE1BAMLBYS6u0EezsZCku0SLpVaO3qEBERNWkMLBZiJ5ehgx9XvCUiIjIHBhYL4sBbIiIi82BgsSDDwFsGFiIiogZhYLGg8rVYuHgcERFRQ5gcWHbt2oW4uDgEBgZCEARs2bKlxuMnT54MQRAqbdHR0YZj5s+fX+n1iIgIkz+MrYkoCyzJ2UXIKdRYuTZERERNl8mBpaCgADExMVixYkWdjl+6dClSU1MN2/Xr1+Hp6YmxY8caHRcdHW103J49e0ytms1xc1SglbsjAOBsGruFiIiI6svO1DfExsYiNja2zse7ubnBzc3N8HzLli24ffs2Hn30UeOK2NnB39/f1OrYvKhAVyRnF+Fsai7uCPOydnWIiIiaJJMDS0OtXr0agwcPRnBwsNH+ixcvIjAwEA4ODujTpw8WLVqENm3aVFuOWq2GWq02PM/NlVowNBoNNBrzdb/oy6pvmR18nRB/BjiVnG3WerVUDb0eZH68JraF18O28HrUrq7fG0EUxXovwyoIAjZv3oxRo0bV6fiUlBS0adMG69evx7hx4wz7t27divz8fHTo0AGpqalYsGABkpOTcerUKbi4uFRZ1vz587FgwYJK+9evXw+VSlWvz2MJx7MErLkgR2snEXM6a61dHSIiIptSWFiIBx98EDk5OXB1da32uEYNLIsWLcL777+PlJQU2NvbV3tcdnY2goOD8cEHH2Dq1KlVHlNVC0tQUBAyMzNr/MCm0mg0iI+Px5AhQ6BQKEx+/7VbhRj84R7Y28lw/NW7YSfnxKyGaOj1IPPjNbEtvB62hdejdrm5ufD29q41sDRal5AoilizZg0eeeSRGsMKALi7u6N9+/a4dOlStccolUoolcpK+xUKhUV+KOpbbpiPK5zs5Sgo0eJ6Tgna+1XdYkSmsdR1pvrjNbEtvB62hdejenX9vjTan/s7d+7EpUuXqm0xqSg/Px+XL19GQEBAI9TMsmQyocJ6LJwpREREVB8mB5b8/HwkJCQgISEBAJCYmIiEhAQkJSUBAObNm4eJEydWet/q1avRu3dvdOzYsdJrL7zwAnbu3ImrV69i3759uP/++yGXyzFhwgRTq2eT9IHlTAoDCxERUX2Y3CV0+PBhDBo0yPB89uzZAIBJkyZh7dq1SE1NNYQXvZycHHz//fdYunRplWXeuHEDEyZMQFZWFnx8fNC/f3/s378fPj4+plbPJhkCC1tYiIiI6sXkwDJw4EDUNE537dq1lfa5ubmhsLCw2vd8++23plajSdHfU4hL9BMREdUPp6w0gg7+LhAEIDNfjZt56trfQEREREYYWBqByt4Ood5OADjwloiIqD4YWBoJx7EQERHVHwNLI4ni1GYiIqJ6Y2BpJOUDbxlYiIiITMXA0kj0XUKXbxagWMN7ChEREZmCgaWR+Ls6wEOlgFYn4lJGvrWrQ0RE1KQwsDQSQRC44i0REVE9MbA0Is4UIiIiqh8GlkbEmyASERHVDwNLI6o4U6im2xsQERGRMQaWRhTu6wKFXEBucSmSs4usXR0iIqImg4GlEdnbydDWxxkAb4RIRERkCgaWRsYVb4mIiEzHwNLIOPCWiIjIdAwsjYyBhYiIyHQMLI1MP1PoalYh8tWlVq4NERFR08DA0si8nJXwc1UCAM6nsZWFiIioLhhYrKB8xVvOFCIiIqoLBhYr4DgWIiIi0zCwWAEDCxERkWkYWKxAvxbLudQ8aHVcop+IiKg2DCxWEOrtBAeFDEUaLa5lFVi7OkRERDaPgcUK5DIBHfz0N0LkwFsiIqLaMLBYCcexEBER1R0Di5UwsBAREdUdA4uVRAXq12JhYCEiIqoNA4uVRPhLY1hSc4qRXVhi5doQERHZNgYWK3FxUCDI0xEAW1mIiIhqw8BiRZH++nEsnClERERUEwYWK9KPY+HAWyIiopoxsFiR4SaIKQwsRERENWFgsSL9Ev2XMvKh0eqsXBsiIiLbxcBiRa09HOGitEOJVofLN/OtXR0iIiKbxcBiRYIgICJAv0Q/u4WIiIiqw8BiZVEBnClERERUGwYWK+PAWyIiotoxsFhZxXsKiaJo5doQERHZJgYWK+vg7wKZAGQVlOBmntra1SEiIrJJDCxW5qCQI9TbCQCX6CciIqoOA4sNiAp0A8CBt0RERNVhYLEBkWVTm9nCQkREVDUGFhtQceAtERERVcbAYgP0a7FcuZmPYo3WyrUhIiKyPQwsNsDXRQlPJ3voROBCOsexEBER/ZPJgWXXrl2Ii4tDYGAgBEHAli1bajx+8uTJEASh0hYdHW103IoVKxASEgIHBwf07t0bBw8eNLVqTZYgCIZWFi4gR0REVJnJgaWgoAAxMTFYsWJFnY5funQpUlNTDdv169fh6emJsWPHGo7ZsGEDZs+ejTfeeANHjx5FTEwMhg0bhoyMDFOr12RF8p5CRERE1bIz9Q2xsbGIjY2t8/Fubm5wc3MzPN+yZQtu376NRx991LDvgw8+wOOPP27Y9+mnn+KXX37BmjVr8NJLL5laxSYpkvcUIiIiqpbJgaWhVq9ejcGDByM4OBgAUFJSgiNHjmDevHmGY2QyGQYPHoy///672nLUajXU6vKVYXNzpZYJjUYDjUZjtvrqyzJnmVUJ91EBAM6m5aKkpASCIFj0fE1VY10PqjteE9vC62FbeD1qV9fvTaMGlpSUFGzduhXr16837MvMzIRWq4Wfn5/RsX5+fjh37ly1ZS1atAgLFiyotH/btm1QqVTmq3SZ+Ph4s5dZUakOkAty5BWX4v82b4WXg0VP1+RZ+nqQ6XhNbAuvh23h9aheYWFhnY5r1MDy5Zdfwt3dHaNGjWpwWfPmzcPs2bMNz3NzcxEUFIShQ4fC1dW1weXraTQaxMfHY8iQIVAoFGYrtyqrrv2Ns2l58I/ogSFRvhY9V1PVmNeD6obXxLbwetgWXo/a6XtIatNogUUURaxZswaPPPII7O3tDfu9vb0hl8uRnp5udHx6ejr8/f2rLU+pVEKpVFbar1AoLPJDYalyK4oKdMPZtDxcuFmA4fzBrlFjXA8yDa+JbeH1sC28HtWr6/el0dZh2blzJy5duoSpU6ca7be3t0f37t2xfft2wz6dToft27ejT58+jVU9m8CZQkRERFUzuYUlPz8fly5dMjxPTExEQkICPD090aZNG8ybNw/JyclYt26d0ftWr16N3r17o2PHjpXKnD17NiZNmoQePXqgV69eWLJkCQoKCoxmErUEUZwpREREVCWTA8vhw4cxaNAgw3P9OJJJkyZh7dq1SE1NRVJSktF7cnJy8P3332Pp0qVVljl+/HjcvHkTr7/+OtLS0tClSxf89ttvlQbiNnf6qc1JtwqRV6yBiwObD4mIiIB6BJaBAwdCFMVqX1+7dm2lfW5ubrWOAp4+fTqmT59uanWaFQ8newS4OSA1pxjn0vLQM8TT2lUiIiKyCbyXkI3hnZuJiIgqY2CxMRx4S0REVBkDi43Rt7Cc4cBbIiIiAwYWG6MPLOfTcqHVVT9WiIiIqCVhYLExIV5OcFTIUazRITGzwNrVISIisgkMLDZGLhPQwZ/jWIiIiCpiYLFBnClERERkjIHFBkVxphAREZERBhYbFMkl+omIiIwwsNigiLLAkpZbjFsFJVauDRERkfUxsNggZ6Udgr1UANgtREREBDCw2KxIfw68JSIi0mNgsVHlK94ysBARETGw2Kjyewpx4C0REREDi42KCpRaWC5l5KGkVGfl2hAREVkXA4uNauXuCFcHO2i0Ii5l5Fu7OkRERFbFwGKjBEEwTG/mwFsiImrpGFhsWBQDCxEREQAGFpumH3jLmUJERNTSMbDYsKgANwBSC4soilauDRERkfUwsNiwcD9nyGUCbhdqkJ6rtnZ1iIiIrIaBxYY5KOQI83YCwHEsRETUsjGw2DiueEtERMTAYvMYWIiIiBhYbJ5+xVt2CRERUUvGwGLj9FObr2YWoKhEa+XaEBERWQcDi43zdXGAt7M9dCJwPp03QiQiopaJgaUmRUWQffQR+rzxBqC1XutGJFe8JSKiFo6BpSaiCNnbb8P3+HEIP/9stWoYBt6mMLAQEVHLxMBSE5UKuqlTAQCy5cutVg3eU4iIiFo6BpZa6J56CjqZDLKdO4ETJ6xSB30Ly7m0POh0XKKfiIhaHgaW2gQFIbVPH+nx0qVWqUKYjxPs5TLkq0tx43aRVepARERkTQwsdXA5Lk568PXXwM2bjX5+hVyGcD9nAFxAjoiIWiYGljq43aEDdN27A2o1sHKlVeoQxRVviYioBWNgqQtBgG76dOnxxx8DGk2jV4FTm4mIqCVjYKkjcexYwN8fSEkBNm1q9PMzsBARUUvGwFJX9vbA009Lj60w+FbfJXTjdhFyixu/hYeIiMiaGFhM8eSTUnA5cEDaGpGbSoFANwcAwLlULtFPREQtCwOLKfz8gAcflB5bo5UlUL/ibU6jn5uIiMiaGFhM9eyz0teNG4Hk5EY9dfk4FrawEBFRy8LAYqouXYABA4DSUmnGUCMyBJY0DrwlIqKWhYGlPvStLJ99BhQ13sqz+sByPi0PpVpdo52XiIjI2hhY6mPkSCA4GMjKAtavb7TTBnuqoLKXQ12qw9WsgkY7LxERkbUxsNSHXA7oF5JbuhQQG+eGhDKZgAh/FwDA6RR2CxERUcthcmDZtWsX4uLiEBgYCEEQsGXLllrfo1ar8corryA4OBhKpRIhISFYs2aN4fW1a9dCEASjzcHBwdSqNa6pUwGVCjh5Evjrr0Y7LQfeEhFRS2Rn6hsKCgoQExODKVOm4IEHHqjTe8aNG4f09HSsXr0a7dq1Q2pqKnQ64zEYrq6uOH/+vOG5IAimVq1xeXgAkyYBn3witbIMGtQop+WKt0RE1BKZHFhiY2MRGxtb5+N/++037Ny5E1euXIGnpycAICQkpNJxgiDA39/f1OpY18yZUmD56SfgyhUgLMzip2RgISKilsjkwGKqn376CT169MC7776Lr776Ck5OTrjvvvuwcOFCODo6Go7Lz89HcHAwdDodunXrhrfffhvR0dHVlqtWq6FWqw3Pc3OlX+AajQYaM96cUF9WlWW2bQv50KGQbdsG7UcfQbd4sdnOW522Xg4QBCAjT4202/nwclZa/Jy2pMbrQVbBa2JbeD1sC69H7er6vbF4YLly5Qr27NkDBwcHbN68GZmZmXjmmWeQlZWFL774AgDQoUMHrFmzBp07d0ZOTg7ee+899O3bF6dPn0br1q2rLHfRokVYsGBBpf3btm2DSqUy++eIj4+vcr9v797os20bdJ9/jm133IHSCiHMUryVctwsFvDlT38iwr1xBvzamuquB1kPr4lt4fWwLbwe1SssLKzTcYIo1n+KiyAI2Lx5M0aNGlXtMUOHDsXu3buRlpYGNzc3AMAPP/yAMWPGoKCgwKiVRU+j0SAyMhITJkzAwoULqyy3qhaWoKAgZGZmwtXVtb4fqcq6xMfHY8iQIVAoFJUP0Olg16kThIsXoV2yBLpnnjHbuasz49vj+O10OuYOa4/H+odY/Hy2pNbrQY2O18S28HrYFl6P2uXm5sLb2xs5OTk1/v62eAtLQEAAWrVqZQgrABAZGQlRFHHjxg2Eh4dXeo9CoUDXrl1x6dKlastVKpVQKit3hygUCov8UNRY7qxZwLRpkK9YAfmMGYDMsrPFowPd8NvpdFzIKGix/wAsdZ2p/nhNbAuvh23h9aheXb8vFl+HpV+/fkhJSUF+fr5h34ULFyCTyart7tFqtTh58iQCAgIsXT3zmDgRcHMDLl4Etm61+Ok48JaIiFoakwNLfn4+EhISkJCQAABITExEQkICkpKSAADz5s3DxIkTDcc/+OCD8PLywqOPPoozZ85g165dmDNnDqZMmWLoDnrzzTexbds2XLlyBUePHsXDDz+Ma9eu4bHHHjPDR2wEzs6Avq6NcBfnyLK7Nl/KyIe6VGvx8xEREVmbyYHl8OHD6Nq1K7p27QoAmD17Nrp27YrXX38dAJCammoILwDg7OyM+Ph4ZGdno0ePHnjooYcQFxeHjz76yHDM7du38fjjjyMyMhLDhw9Hbm4u9u3bh6ioqIZ+vsYzfbrUFRQfD5w5Y9FTBbo5wM1RgVKdiIvp+bW/gYiIqIkzeQzLwIEDUdM43bVr11baFxERUeMI6Q8//BAffvihqVWxLSEh0j2GNm8GPvoI+PRTi51KEAREBrhg/5VbOJuai46t3Gp/ExERURPGewmZk/4uzuvWAbduWfRUXKKfiIhaEgYWcxowAIiJAYqKgFWrLHoqDrwlIqKWhIHFnAShvJVl+XKgtNRip4oqCyxnUnNr7KIjIiJqDhhYzG3CBMDHB7h+HajDnazrq52vM+xkAnKKNEjNKbbYeYiIiGwBA4u5OTgATz4pPbbgFGcHhRxtfZwBsFuIiIiaPwYWS3j6acDODtizBzhyxGKniQxwAcDAQkREzR8DiyUEBgLjx0uPLdjKwplCRETUUjCwWIp+8O233wJpaRY5RWSFgbdERETNGQOLpfTsCfTpA2g0FltETh9YrmYVoLDEcjOSiIiIrI2BxZL0rSyffAKo1WYv3sdFCR8XJUQROJfGbiEiImq+GFgs6YEHgFatgIwMYMMGi5yCC8gREVFLwMBiSQoFMG2a9HjpUsACC7xxphAREbUEDCyW9sQT0tosR48Ce/eavXjDircpDCxERNR8MbBYmpcX8PDD0mMLTHHWB5ZzaXnQ6bhEPxERNU8MLI1h5kzp6+bNQFKSWYsO9XaCvZ0MhSVaJN0qNGvZREREtoKBpTF06gTcfTeg1QIrVpi1aDu5DB38OI6FiIiaNwaWxqKf4rxyJVBQYNaiOfCWiIiaOwaWxjJiBNC2LZCdDXz1lVmL5oq3RETU3DGwNBa5HJgxQ3r80UdmneIcxXsKERFRM8fA0pgefRRwcQHOngXi481WbERZYEnOLkJOocZs5RIREdkKBpbG5OoqhRbArFOc3RwVaOXuCAA4m8ZuISIian4YWBrbjBmAIAC//gpcuGC2YiO5gBwRETVjDCyNrV07aQAuACxbZrZiozhTiIiImjEGFmvQT3FeuxbIyTFLkVGBZQNv2SVERETNEAOLNdxzDxAdDeTnA2vWmKVIfZfQhfR8lGp1ZimTiIjIVjCwWIMglC/Xv2yZtAJuAwV5qOBkL0dJqQ5XMs27MB0REZG1MbBYy8MPA56eQGIi8PPPDS5OJhMM05s58JaIiJobBhZrUamAxx+XHi9ZYpYiuUQ/ERE1Vwws1jRtmrQC7l9/ASdONLi4qAA3AFyin4iImh8GFmsKCgJGj5Yem2EhufIWFi7RT0REzQsDi7Xppzh//TVw82aDiurg7wJBADLz1cjIKzZD5YiIiGwDA4u19ekD9OgBqNXAypUNKkplb4dQLycAbGUhIqLmhYHF2gShvJXl448BTcNuXhhpuHMzx7EQEVHzwcBiC8aNA/z9gZQUYNOmBhVlWPGWgYWIiJoRBhZbYG8PPP209LiBg285tZmIiJojBhZb8eSTUnA5cEDa6knfJXT5ZgGKNQ1fQZeIiMgWMLDYCj8/YMIE6XEDWln8XR3grlJAqxNxMT3fTJUjIiKyLgYWW6IffLtxI5CcXK8iBEFAFAfeEhFRM8PAYku6dgXuvBMoLZVmDNWTvluIK94SEVFzwcBia2bNkr5+9hlQVFSvIji1mYiImhsGFlszciQQHAxkZQHr19eriIozhURRNGftiIiIrIKBxdbI5cD06dLjpUuBegSOdr7OsJMJyC0uRXJ2/VppiIiIbAkDiy2aOhVQqYCTJ6U7OZtIaSdHO19nAFyin4iImgcGFlvk4QFMmiQ9rucUZ84UIiKi5oSBxVbNnCl9/ekn4MoVk9/OgbdERNScmBxYdu3ahbi4OAQGBkIQBGzZsqXW96jVarzyyisIDg6GUqlESEgI1qxZY3TMxo0bERERAQcHB3Tq1Am//vqrqVVrXiIigGHDpDEsy5eb/HYGFiIiak5MDiwFBQWIiYnBihUr6vyecePGYfv27Vi9ejXOnz+Pb775Bh06dDC8vm/fPkyYMAFTp07FsWPHMGrUKIwaNQqnTp0ytXrNi34hudWrgTzTxqLoZwpdzSpEvrrU3DUjIiJqVHamviE2NhaxsbF1Pv63337Dzp07ceXKFXh6egIAQkJCjI5ZunQp7r33XsyZMwcAsHDhQsTHx2P58uX49NNPTa1i8zFsGNC+PXDhAvDll+Wzh+rAy1kJP1cl0nPVOJ+Wi+7BnhasKBERkWWZHFhM9dNPP6FHjx5499138dVXX8HJyQn33XcfFi5cCEdHRwDA33//jdmzZxu9b9iwYTV2N6nVaqjVasPz3Fyp60Oj0UCj0Zit/vqyzFmmKWTTpkH+7LMQly5F6eOPA7K6N4pF+LkgPVeNkzey0TnQxYK1bDzWvh5UGa+JbeH1sC28HrWr6/fG4oHlypUr2LNnDxwcHLB582ZkZmbimWeeQVZWFr744gsAQFpaGvz8/Ize5+fnh7S0tGrLXbRoERYsWFBp/7Zt26BSqcz7IQDEx8ebvcy6kPv6YphKBcWlSzjyn/8gvUePOr9XUSADIMO2g6fhkXnScpW0AmtdD6oer4lt4fWwLbwe1SssLKzTcRYPLDqdDoIg4Ouvv4abmxsA4IMPPsCYMWPw8ccfG1pZTDVv3jyjVpnc3FwEBQVh6NChcHV1NUvdASn5xcfHY8iQIVAoFGYr1xSygweBDz9Er/37oX399Tq/T3ciFX9sPIkChQeGD+9twRo2Hlu4HmSM18S28HrYFl6P2ul7SGpj8cASEBCAVq1aGcIKAERGRkIURdy4cQPh4eHw9/dHenq60fvS09Ph7+9fbblKpRJKpbLSfoVCYZEfCkuVWyczZwJLl0L2xx+QXbwIREXV6W2dgjwAABfS8yGT20EuEyxZy0Zl1etBVeI1sS28HraF16N6df2+WHwdln79+iElJQX5+fmGfRcuXIBMJkPr1q0BAH369MH27duN3hcfH48+ffpYunpNQ0iIdI8hAPjoozq/LdTbGQ4KGYo0WlzLKrBM3YiIiBqByYElPz8fCQkJSEhIAAAkJiYiISEBSUlJAKSumokTJxqOf/DBB+Hl5YVHH30UZ86cwa5duzBnzhxMmTLF0B307LPP4rfffsP777+Pc+fOYf78+Th8+DCmmzArptnTT3Fetw64datOb5HLBHTw098IkUv0ExFR02VyYDl8+DC6du2Krl27AgBmz56Nrl274vWysRWpqamG8AIAzs7OiI+PR3Z2Nnr06IGHHnoIcXFx+KhCS0Hfvn2xfv16rFy5EjExMdi0aRO2bNmCjh07NvTzNR8DBgAxMUBREbBqVZ3fxgXkiIioOTB5DMvAgQMh1nAH4bVr11baFxERUesI6bFjx2Ls2LGmVqflEASplWXKFGnl29mzAbvaL58+sJxhYCEioiaM9xJqSiZMAHx8gOvXgTrcEgFgCwsRETUPDCxNiYMD8OST0uM63sU5omyJ/tScYmQXlliqZkRERBbFwNLUPP201BW0Zw9w5Eith7s6KBDkKQ1uZrcQERE1VQwsTU1gIDBunPS4jq0skf76biHOFCIioqaJgaUp0k9x/vZboIbbF+gZBt6msIWFiIiaJgaWpqhXL6BPH0CjAepwN2sOvCUioqaOgaWp0reyfPIJUOGu1VWJDpQCy6WMfGi0OkvXjIiIyOwYWJqqBx4AWrUCMjKADRtqPLS1hyNclHYo0eqw52JmI1WQiIjIfBhYmiqFApg2TXq8dClQw2J+giCgf7g3AGDql4fwwbbzbGkhIqImhYGlKXviCWltlqNHgb17azz0nTGdcX/XVtCJwEd/XsKYT/bhys38Gt9DRERkKxhYmjIvL+Dhh6XHtUxxdnVQ4MPxXbBsQle4Otjh+I0cDP9oN/5v/7Uab7VARERkCxhYmrqZM6WvmzcDFW46WZ24mED8/twA9G3rhWKNDq9uOYWpXx7GzbyaB+4SERFZEwNLU9epE3D33YBWC6xYUae3BLg54v+m9sarIyJhbyfDn+cyMGzJLmw7XfuaLkRERNbAwNIc6Kc4f/45UFBQp7fIZAIeuzMM/5veHxH+LrhVUIInvjqCuZtOoEBdasHKEhERmY6BpTkYMQIICwNu3wa++sqkt3bwd8GP0/vhyQFhEARgw+HrGP7Rbhy5dttClSUiIjIdA0tzIJcDM2ZIjz/6qMYpzlVR2skxb3gk1j92BwLdHHAtqxBjP93H6c9ERGQzGFiaiylTABcX4OxZID6+XkX0aeuFrbMGYFSXQE5/JiIim8LA0ly4ugKPPio9ruNdnKvi5qjAkn93NZr+POKjPZz+TEREVsXA0pzMmAEIAvDrr8CFCw0qquL05yKNltOfiYjIqhhYmpN27aQBuACwbFmDi+P0ZyIishUMLM2Nforz2rVATk6Di6tu+vNL33P6MxERNR4GlubmnnuA6GggPx9Ys8Zsxf5z+vO3hzj9mYiIGg8DS3MjCOXL9S9bJq2Aayac/kxERNbCwNIcPfww4OkJJCYCP/9s9uI5/ZmIiBobA0tzpFIBjz8uPW7AFOea6Kc/f8Tpz0RE1AgYWJqradOkFXB37ABOnLDYae6LCcRvszj9mYiILIuBpbkKCgIeeEB6bKFWFr1A98rTn+9dsgvxZ9Itel4iImo5GFias1mzpK9ffw3cvGnRU+mnP/80vR8i/F2QVVCCx9cd5vRnIiIyCwaW5qxPH6BHD0CtBlaubJRTRvi74sfp/fDEP6Y/H03i9GciIqo/BpbmTBDKF5L7+GNAo2mU0yrt5Hi50vTnv/FB/AVOfyYionphYGnuxo0D/P2BlBRg06ZGPbV++vPILoHQ6kR8tP0ipz8TEVG9MLA0d/b2wNNPS48tPPi2Km6OCiytYvrz1wc4/ZmIiOqOgaUlePJJKbgcOAB06AA89xwQHy+NbWkk/5z+/MrmU3iM05+JiKiOGFhaAj8/4K23ADs74MIFYMkSYOhQwMsLGDkS+OwzICnJ4tUwmv4sl2E7pz8TEVEdMbC0FC+8AGRmSuNYpkyRxrUUFAA//QQ89RQQHAx07Ai8+CLw118WG6BrmP48w3j687wfOP2ZiIiqx8DSkri5AaNHA6tXS4Nwjx4F/vMfoF8/QCYDTp8GFi8GBg0CvL2BMWOkOz6npJi9Kv+c/vzNwesYwenPRERUDQaWlkoQgK5dgVdeAfbskRaW++Yb4JFHpLCSmwt8/z0wdSrQqhXQrRvw6qvA3r1AqXlaQvTTn79+rDcC3RxwldOfiYioGgwsJPH0BP79b2DdOiA9XRqg+8YbQM+eUrg5dkwaB9O/P+DrC0yYAHz1lVlW0O3b1pvTn4mIqEYMLFSZTAb06gXMnw8cPAikpUlB5t//Bjw8gNu3gW+/BSZOlAb09u4NLFgAHDoE6OrXMqKf/rz0313gwunPRET0DwwsVDtfX6mr6JtvgIwMqQvp5ZeBLl0AUZRCzfz5Usjx9wcmTQI2bJCCjYlGdmmF32cNQJ8wTn8mIqJyDCxkGjs7aZDuW29J3UQ3bgCrVkmDeV1cpC4ifWuMt7fUhfT220BCghRu6iDQ3RFfP8bpz0REViOKQGKiNJbx1VeB4cOBdu0ArdZqVbKz2pmpeWjVShqYO3UqUFIC7NsH/PqrtJ0+LQ3S3btXGtwbGAjExko/+IMHA66u1Rarn/7cP9wbs75NwLm0PDy+7jDG92iF7ozZRETmo9UC589Lf4QePSp9PXYMyM6ufOyFC0BkZKNXEWBgIXOytwcGDpS2d98Frl0Dtm6Vwsv27dL06NWrpc3ODrjzzvIAExUlDe79hwh/V2yZ1g/vbzuPVXsSseFwMv5QynFZeRF92nmjR4gnnJX8MSYiqhO1WvpjsmI4OX4cKCysfKy9vbQ+V7du0qzSbt2AsLDGr3MZ/k9PlhMcLC1K99RTQHExsHt3eevLhQvAjh3S9uKLQJs2UnAZPhy4+27AyclQjINCjldGRGFQhC+e/+44UnOK8dnuRHy2OxFymYCOrdxwR6gneod5okeIJ1wdFFb80C1McjKEgwehyOeMLiKbU1AghRF9MDl6VAorVS0M6uQkjUvUB5OuXaU/JO3tG73a1WFgocbh4AAMGSJtH34IXLpU3vqyY4d0a4BPP5U2fUuNPsCEhwOQpj//Mr0v3vs2HsWuQTh07Tau3yrC8evZOH49G5/tugKZAEQHuqF3qCfuCPNCz1BPuDkywJiV/s7fGzcCe/bADsAQlQqylBTg2Wela01EjevWrfKuHH1AOX++6rGDnp7GwaRbN2l8ilze+PU2gcmBZdeuXVi8eDGOHDmC1NRUbN68GaNGjar2+L/++guDBg2qtD81NRX+/v4AgPnz52PBggVGr3fo0AHnzp0ztXrUVLRrB8yYIW2FhVJo2boV+OUX4OpVYNs2aZs1C2jb1hBeXPr2RW9fEcOHd4RCoUBydhEOXMnCgSu3sD8xC9eyCnEyOQcnk3Owak8iBAGI9HfFHWFe6B3mid6hnnBX2c5fDE1Gaqo0+O6776RZYhX+ExQDAqBITQVeekkKnIsWAePHV9nFR0RmkJpq3Gpy7Jj0/2ZVAgONg0nXrlKLdhP892lyYCkoKEBMTAymTJmCBx54oM7vO3/+PFwrDLL09fU1ej06Ohp//PFHecXs2PjTYqhUwIgR0rZsmfRXgb7raNcu4PJlaf+yZbBzdETv6GjIrl4FRoxAq3bt8EC31nigW2sAQFpOMQ4kZmF/WYi5klmAM6m5OJOaizV7EwEAEf4uUoAJ9USvUE94OSut+OFtWFoa8MMPUkjZtcv4L7W+fYFx44DRo1Hq7Y2Tc+ei6/ffQ7h6VVpU8MMPgfffl2aJEVH96GfqVAwmR49Ki3tWJSyscjjx82vcOluQyakgNjYWsbGxJp/I19cX7u7u1VfEzs7Q4kItmCAAERHSNns2kJcnDdgtCzBCcjL8Dx8GDh+WWl/atQPuvVfaBg6Ev5sTRnZphZFdWgEAMnKLsT/xltQKk3gLlzLycS4tD+fS8rB231UAQHs/Z/QO9cIdYV7oFeoJH5cWHGDS06WQsnEjsHOn8UKAd9whhZQxY4CgoPL9Gg2u3303Or35JhTLlgH//a+0Ns+ddwIPPCA9L+vWI6Jq6GfqVAwmCQlVz9SRyaSZOhWDSZcuQA2/Y5uDRmvG6NKlC9RqNTp27Ij58+ejX79+Rq9fvHgRgYGBcHBwQJ8+fbBo0SK0adOm2vLUajXU6vLFxHJzcwEAGo0GGjPeaVhfljnLJBM4OJS3vogiShMScGXFCkRcvQrZvn0QLl0Cli8Hli+HaG8P8c47IQ4bBt3QoUBkJDwc5YiN8kFslA8AIDNfjUNXb+Pg1ds4kHgLFzMKcCE9HxfS8/HV/msAgDBvJ/QO9UCvEA/0CvWEb3MPMDdvQrZlC4RNmyDs3AmhQkjR9ewJccwY6B54QBpErVfh34Ph34hCAcydC0yaBNnChZCtXg3hhx8g/vQTdE89Bd3LL0tr85BF8f8s21Ll9VCrgTNnIBw7Jm0JCRBOnIBQVFTp/aK9PcSOHYEuXSB27SptHTtKLdOVT2apj2FRdf1ZFcQGrHsuCEKtY1jOnz+Pv/76Cz169IBarcaqVavw1Vdf4cCBA+jWrRsAYOvWrcjPz0eHDh2QmpqKBQsWIDk5GadOnYKLi0uV5VY17gUA1q9fD1VVF5KaHbuiInifOAHfY8fgd+QIVP+4r1GhtzcyunZFRrduuNm5M0orzDzSy9cAl3MFXCrbUgor9+v6Ooho6yqiXdnm3gzyi31uLgL270fg3r3wPnkSsgoh5XZ4OJL79UNKnz4oakBzsktSEqK+/BL+R44AADQqFS6MHYsrI0ZAZ0MzD4gsSa5Ww+3KFaPNNSkJsioWYCt1cEBOaChywsKQExaG7LAw5LVuDVHRvCcOFBYW4sEHH0ROTo7R0JF/snhgqcpdd92FNm3a4Kuvvqry9ezsbAQHB+ODDz7A1KlTqzymqhaWoKAgZGZm1viBTaXRaBAfH48hQ4ZA0cx/aJqCaq+HKAIXLkD2++8Qtm2TWgoq/HyIdnYQ+/SBOHSo1PoSEyM1q/7D7cISHL6ajYNXb+Hg1ds4m5ZXaZB9G09H9ArxNLTCBLo7WurjmldWFoQff4Ts++8h/PknhAr/Yeq6dZNaUkaPBkJDTSq2tn8jwvbtkM+dC+HECQCAGBIC7cKFEMeNa5ID/2wd/8+yEWfPQvbpp5B99RWEKqb9i56eUmtJTIz0tUuXJjFTxxJyc3Ph7e1da2CxysjWXr16Yc+ePdW+7u7ujvbt2+PSpUvVHqNUKqFUVv5TV6FQWOQfqaXKpfqp8np07Chtzz8vzTzatQv47Tfgt98gnD8PYfduYPduyF97TRqINmyYNPZl6FDAywsA4OumwPAYJwyPkcbA5BRqcOjqrbKBvLdwOiUHSbeKkHQrGZuOJgMAWns4Ggbx3hHmhdYejhBs5RfxrVvAli3SwNnt24HS0vLXunaVxqSMHQtZ27YAgIb8V1ntv5F775Wms//f/wGvvALh6lXYPfKINJD6vfeksS5kdvw/ywq0WuB//5O6qbdvN+wu9vCAfZ8+kHXvbhhzIrRpYzv/T1hZXX9OrRJYEhISEBAQUO3r+fn5uHz5Mh555JFGrBU1KypV+WBcQBppXxZesH27NLh03TppEwTpxo3643v2NPyV46ZSYHCUHwZHSV0jucUaHLl6G/uvZGF/4i2cSs7BjdtF2HTkBjYduQEAaOXuiN5lC9ndEeaFNp6qxv2P6fZt4McfpZASH28cUrp0MYQUtGvXeHWSy6WbYo4dK80g0g/MHTAAuP9+6Xn79o1XHyJzysyUVvD++GNpTSlAasG97z6UPvUUfi8qwvARIyBjgGwQkwNLfn6+UctHYmIiEhIS4OnpiTZt2mDevHlITk7GunXrAABLlixBaGgooqOjUVxcjFWrVuHPP//Etm3bDGW88MILiIuLQ3BwMFJSUvDGG29ALpdjwoQJZviIRJC6OZ5+WtpKSqT7G+kDzIkTwIED0rZggbSo0pAh0m0Dhg4FKoRrVwcFBkX4YlCENC0/X12Kw1dv4UDZTKQTN3KQnF2EH44l44djUguMv6tD2RowXrgjzBOh3k7mDzDZ2cBPP0khZds248F3nTuXhxRrhwKVSrqv1NSp0h2+P/8c2LxZ+qv06aeB11/nwFxqOo4elVpT1q+XBtICUmvt449LK3wHB0PUaKRZjtRgJgeWw4cPGy0EN3v2bADApEmTsHbtWqSmpiJJnzABlJSU4Pnnn0dycjJUKhU6d+6MP/74w6iMGzduYMKECcjKyoKPjw/69++P/fv3w8fHpyGfjahq9vbAoEHS9s47QHIy8PvvUniJj5e6UTZskDZAapXQt7707QtU+CvJWWmHgR18MbCDFGAKS0px5NptaSG7K1k4fiMbabnF+DEhBT8mpAAAfFyUUgtMqCdCvJ0Q6O6IVu6OcFCY2CGTk1MeUn7/3TikdOxYHlIiIhry3bIMf39pkbmZM6VbM/zyi9RFtG6dFGhmzOCKuWSbSkqklZ6XLwf+/rt8f7du0s/tv//Nn10LadCgW1uSm5sLNze3WgftmEqj0eDXX3/F8OHD2R9sAyx+PUpLpa4KfevL4cPGC6a5uAD33CO1vgwbZjzVtwpFJVocSyrvQkpIykaJVlflsd7O9obwUvFraw/pq4dKASEvT2qN+O47qX4lJeUFREeXh5RGvJuqWa7J9u3ACy9I604A0vdVv2JuFYOjqXr8P8tCUlKAzz6TNv3CbQqF9O9txgygd+8qB5HzetSurr+/uZwsUUV2dlIrSt++wJtvAjdvSl0sv/0mtWLcvCkNYt2yRTo+MrK89WXAgEp/WTnay9G3nTf6tpO6OYo1WhxLysaBxCwcv56NG7eLkJxdhMISLTLzS5CZX4ITN3KMynBSF+Key4dw34U9GHD5MOxLy1tS8kLaIee++2E3fhy8e3eDnbyJ/nK/5x7gyBHgq6+kFpZr14AHHyxfMZcDc8kaRFHqPl62TFpQUT8eLDBQ6vJ5/HGptZAaBQMLUU18fICHHpI2nU5agfK336T7Hv39N3D2rLR9+CHg6Ch1M+kDTLt2lf7iclDI0aetF/q09TLsE0UROUUa3LhdhJRsKcDcTM2E11/xiNrzO7qe3g+H0vKWlMuerfFzRH/8EtEfF7yDpXP8lA7Z/7bC39UBrTyMW2haeTiiddljJ6UN/5OXySoPzD10iANzqfEVFkrjUpYvl+52rHfnncD06dLPI1tLGp0N/+9FZGNkMqB7d2l75RVpNs727eXdR8nJ5fdAAqT7etx7r9R9NHAg4OxcZbGCIMBdZQ93UYOOe3dJ3T2//AIUFxuO0bULR8599yNx4HBc8guFNqcYnbOL4HW7CCk5UtDRaEWk5BQjJacYwO0qz+WuUiDQTQoxrcpCTcWA4+1sb/2plvqBuY89Jg3MXbmSA3OpcVy5AnzyiTTj53bZvyFHR+kPlunTpfWbyGoYWIjqy8NDuq/OmDFS0/Hp0+XhZdcu6T+/jz+WNnt76a8zfetLdLTUMlJYKAWc774Dfv4ZqLg0d7t20piUceMg69wZHoIADwDdqqiKTiciM1+NG9lFSK7QUpOSXWRoucktLkV2oQbZhRqcSc2t8iPZ28nKWmYcygKNSnpcFnAC3Bxhb9dI3U5+ftIvjxkzjAfmfvmlFGhmzuTgRmo4nQ744w/pZ+uXX8rHrIWGAtOmAY8+Ks0cJKtjYCEyB0EoX7juhReA/Hxgx47y7qPERKk1Zvt2YM4coFUroFMnKdgUFpaXExZmCCno0qXOK8HKZAJ8XR3g6+qAbm08qjwmr1hjCDHJt4uQnF1s9Dw9rxglpTokZhYgMbOg2o/p46w0aqHxdbHHxXQBxceS4WCvgJ1MBrlMgEIuwE4ug0ImQC4reywXYCeTvkrHyGBXts9OJsBOXrav7D2CIABRUVKYqzgwd+5cKQhyYC7VV06OFH5XrAAuXCjfP2yY1JoSG9siV521ZQwsRJbg7AzExUmbKAKXLpW3vuzYIXUfJUvrtCA0tHx2T7duFluu3sVBgQh/BSL8qx6Fr9HqkJZTbDSWRv81uSzUqEt1yMhTIyNPjWNJ2RXeLce3V06bvc6GECMrCzb3v4O4dtvx1LY18C0bmHv+pTex9v5pON++K+zk+uAjBSVDGKoqKFUIUfp9nk728HFWwsdF2rydlY3XokSN48wZaWzKunVAQVkwd3WVWlKeeYbjpGwYAwuRpQkCEB4ubTNmSN0+u3cDp05JA0q7d7eJe+oo5DIEeaoQ5Fn1zUNFUcStghKjrqbk7CKkZhfhRkoqvLx9oRWl4FOqE1Fq+CpCo9OhVFthn06UjtOK0Oqk16taYEF/bDHKp4KvaTsA6x/rhSmHf8Iz+zeiQ9I5LFo6A7+H34H/DnwUiZ6tzPp9cVcpjEKMj7MSvq76xw6G/e6OCshk1r+OVIXS0vIl8//8s3x/VJTUmvLww9KSBWTTGFiIGpujo7SC7tCh1q6JSQRBgJezEl7OSnRu7W7YL60zkYzhw7s1aJ0JrT7E6ERoK4Qcwz6dDhptxQA0CGcz5qLVknfhv/H/MOzifgxJPIyrox/G6cefQ6Gre9nx5SGpVCuVoQ9J+hCl0YnQlOpwq6AEN/PVuJknbaU60TDu52JG5RvYVWQnE+D9j2Dj41IWbirud1FCZW+m/3rz8qRZaqdPSy0Hp0/D7vx53AVAvmGDNFYqMlJaPLBdO2ksVUuSmQmsWiWNhaq4ZP7IkVJQGTTIJv5YoLphYCEimyCXCZDLTBwzEOoJfLMWeO1FYO5cyH7+GWEb1iJs6w8NHpir00nTzSsGmJt5asPzjLxiw77bhRqU6kSk5RYjLbe41rKd7OVGAcYQblwcjPZ7OdlLa+vk5UmBpCyUGL5WWFVcTwDgDkiDviuyswPatpUCTMWtQ4fm17pw5IjUmvLNN+VL5nt7ly+Z36aNdetH9cLAQkRNX1SU1OT/55/SwNxjx6SBuStWSANz//1vkwfmymQCPJzs4eFkj/Z+Nf9CLynVIavgH8HGKNyUh5xijQ4FJVoUZBXialahUTlO6kKEZ11HeOY1hGdeR/usJHTIuo6AnIxqz13q6wdERUPeMQpCx44obdsWR7ZvRw8nJ8gvXJBaYM6dkwaCnz8vbfqFD/Vat64cZCIiAF/fptMCUVICbNwoBZX9+8v3d+8udcWOH89ZZU0cAwsRNR933y3dTuHrr4GXX5ZaIB56qHzF3AEDLHJaezsZAtykad81EUURBSVaZKbcREHCSZSeOAX52TNQXb4Aj8SL8MhKq/a9GU4euODdBhfLtgvebXDRqw1yHKUwZS+Xwee2El7nFBDd+6Jjuzbwv0MFP1clfF3s0So/C74pV+F29RJk586VL3qYkQHcuCFt8fHGJ/XwqDrIhITYzsys5GRpufyVK42XzB83Tur2qWbJfHMqUJciI0+N9NxiaVB6ha9pOUXIzJLhl5wEeDop4aZSwN3RHu4qBdwcFXB3VEj7VPZwd1RAZS+3/lpINoqBhYiaF5kMeOQRYPRoYMkSqYXl8GHgrruksQvvvtt4M0Fyc426coTTp+F85gycr1+v/j0BAUB0NHSRkSho1wFZbdohJTAUqXKVocUmP08NRZ4a3nnFEPPUyC0uRYlWZ5jRBchw8tCNKouXy8Lh1ToaflEO8HVRIlhQo/3tGwi5mYSAlER4JF2G6vJFyJOuQbh9G9i3T9oqcnCQupL+GWTatweUSvN9/6ojisCePeVL5mu10v7AQGlxwccfl9bxaaB8dakUQnKl1jH913Sj52rkq0trKUmGS2eqbyWrSCEX4FYWaNwdFWXBxl4KNypFedApCzjuZQHIxcGu2Q/6ZmAhouZJpZJaWaZOlVbM/fxz4McfpcXBnnpKWjHXXHeEz8mpNPgVp09LrRbVKQsmiI6WurT0Xz2kdXRkAFzKtpBaTl+s0SKzLMyk3i7Ejv1H4BscjqwCjdFf/ln5amh1omFqejkXANGAXzTgB6An4FCqRteim4jJT0Vk9g2E3ryOgNREeCZfg7y4WFqyvuKy9YAUFsPCKgeZyEjAzc3Eb2oVCgrKl8w/caJ8/4ABUmvKqFG1LpkviiLy1KXVhBDpe3Wz7GthibbOVVPZy+HnKoVAX1cH+JUNuPZUKXDyeAJCO0QjT61DdpE0iDunqKTsq6ZsXwk0WhEarbQIZGa+uvaTViAIqNBiY294rA8+bhUDTlkI0ocfRRO5BxkDCxE1bxVXzJ07V1qETr8Ox8svA88+W/exDTk5VQ9+rSmYBAaWBxJ9KKkQTMzBQSFHaw8VWnuo0DHAGZqrIobf067SrK1SrQ5ZBSWVWwqMujGksTfFdkr87dIaf7u0BgJ6AmU3AJfrtGidk452WdelLfMGIrJvICzzOpyKC6Q1hy5dksYUVSAGBECoqnspIKD2Lhv9qtGrVwPZ2dI+R0dpOvL06UDnzhBFEbnFpbh5O8/wudJzpc+TnleMm2VfM3LVKNLUPYg4K+3g66qEr4uyPJC4OJTtc5C63Fwd4FzNfbo0Gg0UyccwvHebGmfRiaKIIo3WMCstu6gEOYUaQ8DJLipBrv5x2f6cwhJkF2lQWKKFKMLwGv4xNqo2TvZyuKv+2YpTuZXHXaVAlyB3OCiss6AeAwsRtQxVDcx96aXyFXMrDszVB5OKoeTMmdqDScXWEv2UYjMGk4ayk8vg5+oAP1cHANW3eGh10po7FVsgMir8ws/I88LZ3DDsyldDoy1bQEcU4VNwuzzIGLYb8Mu/BSE1FUhNNV4HBYDa2QWFoeEo7dABdlFRUMV0hLJTtDRO5s8/IS5bBvz6K4SyhXryW7XB8fsewu7+/8J10QHpe/OR8dsOpOcWQ12qQ125ONgZh5BKrSPS88a6YaggCFDZ20Flb4dA95rHQv2TulSLnCKNIeCUB50SqQWnqueFJchTl0IUIQ0CL9F3J9Zs30t3m1w/c2FgIaKWpbqBue+/L019PX26fBXiqrRqZdyFo//q7t5oH8HS5DLBMLU6OrD643Q6EdlFmioHm+7PU+PHsueFN2+hTUaSIcS0LfvaJjsdyvw8KE8eBU4eNSpbK8ggF3XQt738FdodX3b/F/4K6w5RkAEnqr7Bp6uDnRRCXKtoCanw1dG++Sy7r7STw9dFDl8X02ZBaXUi8oqrCzTlrTwVu63cVda7SzUDCxG1PFUNzD1q/AsTrVpVDiXNLJg0lEwmwNPJHp5O9ogMqP44UZTWtNF3OaXnFuO3PDWybuUCFy7C8fIFeFy7DL/kRIRmJiHsVjIcSkuQa6/Cpk6Dsa7bCGS3DoGvixL9agghvq5Kq3VXNEVyWdmd4lVNY0FBBhYiarkqDsxdv15aQE3flcNgYjaCUP6LsfKaNl0Nj/QDYm/cLkT2pUTIfLwxxNcTD7owiBADCxGRNDD3ueesXYsWTxAEuDoo4BrgBgR0sXZ1yMY0jblMRERE1KIxsBAREZHNY2AhIiIim8fAQkRERDaPgYWIiIhsHgMLERER2TwGFiIiIrJ5DCxERERk8xhYiIiIyOYxsBAREZHNY2AhIiIim8fAQkRERDaPgYWIiIhsXrO5W7MoigCA3Nxcs5ar0WhQWFiI3NxcKBQKs5ZNpuP1sD28JraF18O28HrUTv97W/97vDrNJrDk5eUBAIKCgqxcEyIiIjJVXl4e3Nzcqn1dEGuLNE2ETqdDSkoKXFxcIAiC2crNzc1FUFAQrl+/DldXV7OVS/XD62F7eE1sC6+HbeH1qJ0oisjLy0NgYCBksupHqjSbFhaZTIbWrVtbrHxXV1f+sNkQXg/bw2tiW3g9bAuvR81qalnR46BbIiIisnkMLERERGTzGFhqoVQq8cYbb0CpVFq7KgReD1vEa2JbeD1sC6+H+TSbQbdERETUfLGFhYiIiGweAwsRERHZPAYWIiIisnkMLERERGTzGFiIiIjI5jGw1GLFihUICQmBg4MDevfujYMHD1q7Si3SokWL0LNnT7i4uMDX1xejRo3C+fPnrV0tKvPf//4XgiBg1qxZ1q5Ki5WcnIyHH34YXl5ecHR0RKdOnXD48GFrV6vF0mq1eO211xAaGgpHR0e0bdsWCxcurPUGf1Q9BpYabNiwAbNnz8Ybb7yBo0ePIiYmBsOGDUNGRoa1q9bi7Ny5E9OmTcP+/fsRHx8PjUaDoUOHoqCgwNpVa/EOHTqEzz77DJ07d7Z2VVqs27dvo1+/flAoFNi6dSvOnDmD999/Hx4eHtauWov1zjvv4JNPPsHy5ctx9uxZvPPOO3j33XexbNkya1etyeI6LDXo3bs3evbsieXLlwOQbrAYFBSEGTNm4KWXXrJy7Vq2mzdvwtfXFzt37sSAAQOsXZ0WKz8/H926dcPHH3+M//znP+jSpQuWLFli7Wq1OC+99BL27t2L3bt3W7sqVOZf//oX/Pz8sHr1asO+0aNHw9HREf/3f/9nxZo1XWxhqUZJSQmOHDmCwYMHG/bJZDIMHjwYf//9txVrRgCQk5MDAPD09LRyTVq2adOmYcSIEUb/Tqjx/fTTT+jRowfGjh0LX19fdO3aFZ9//rm1q9Wi9e3bF9u3b8eFCxcAAMePH8eePXsQGxtr5Zo1Xc3mbs3mlpmZCa1WCz8/P6P9fn5+OHfunJVqRYDU0jVr1iz069cPHTt2tHZ1Wqxvv/0WR48exaFDh6xdlRbvypUr+OSTTzB79my8/PLLOHToEGbOnAl7e3tMmjTJ2tVrkV566SXk5uYiIiICcrkcWq0Wb731Fh566CFrV63JYmChJmfatGk4deoU9uzZY+2qtFjXr1/Hs88+i/j4eDg4OFi7Oi2eTqdDjx498PbbbwMAunbtilOnTuHTTz9lYLGS7777Dl9//TXWr1+P6OhoJCQkYNasWQgMDOQ1qScGlmp4e3tDLpcjPT3daH96ejr8/f2tVCuaPn06fv75Z+zatQutW7e2dnVarCNHjiAjIwPdunUz7NNqtdi1axeWL18OtVoNuVxuxRq2LAEBAYiKijLaFxkZie+//95KNaI5c+bgpZdewr///W8AQKdOnXDt2jUsWrSIgaWeOIalGvb29ujevTu2b99u2KfT6bB9+3b06dPHijVrmURRxPTp07F582b8+eefCA0NtXaVWrR77rkHJ0+eREJCgmHr0aMHHnroISQkJDCsNLJ+/fpVmuZ/4cIFBAcHW6lGVFhYCJnM+FesXC6HTqezUo2aPraw1GD27NmYNGkSevTogV69emHJkiUoKCjAo48+au2qtTjTpk3D+vXr8eOPP8LFxQVpaWkAADc3Nzg6Olq5di2Pi4tLpfFDTk5O8PLy4rgiK3juuefQt29fvP322xg3bhwOHjyIlStXYuXKldauWosVFxeHt956C23atEF0dDSOHTuGDz74AFOmTLF21ZoukWq0bNkysU2bNqK9vb3Yq1cvcf/+/dauUosEoMrtiy++sHbVqMxdd90lPvvss9auRov1v//9T+zYsaOoVCrFiIgIceXKldauUouWm5srPvvss2KbNm1EBwcHMSwsTHzllVdEtVpt7ao1WVyHhYiIiGwex7AQERGRzWNgISIiIpvHwEJEREQ2j4GFiIiIbB4DCxEREdk8BhYiIiKyeQwsREREZPMYWIiIiMjmMbAQERGRzWNgISIiIpvHwEJEREQ27/8BjY8VqLn4becAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss_list, label='Train Loss')\n",
    "plt.plot(val_loss_list, c='r', label='Validation Loss')\n",
    "plt.title('Epoch versus Loss')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "96a24a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(val_accuracy_list)\n",
    "plt.title('Epoch versus Validation Accuracy')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9a8d612d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the best model and evaluate it on the test set\n",
    "model.load_state_dict(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "890aeb4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 1.553978, Test Accuracy: 0.907500\n"
     ]
    }
   ],
   "source": [
    "# Test the model on the test set\n",
    "test_loss = 0\n",
    "correct = 0\n",
    "\n",
    "model.eval()\n",
    "for data, target in test_loader:    \n",
    "    data, target = data.to(device), target.to(device)\n",
    "    with torch.no_grad():        \n",
    "        outputs = model(data)\n",
    "        loss = criterion(outputs, target)\n",
    "    test_loss += loss.item() * len(data)\n",
    "    pred = outputs.argmax(dim=1, keepdim=True)\n",
    "    correct += pred.eq(target.view_as(pred)).sum().item()  #(predicted == labels).sum().item()\n",
    "\n",
    "test_loss /= len(test_loader.dataset)\n",
    "test_acc = correct / len(test_loader.dataset)\n",
    "print(f'Test Loss: {test_loss:.6f}, Test Accuracy: {test_acc:.6f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d844e6a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}