DL_Question2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c99f3128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sat Oct  1 05:34:22 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 510.60.02    Driver Version: 510.60.02    CUDA Version: 11.6     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA RTX A4000    On   | 00000000:01:00.0 Off |                  Off |\n",
      "| 41%   29C    P8    15W / 140W |      5MiB / 16376MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   1  NVIDIA RTX A4000    On   | 00000000:25:00.0 Off |                  Off |\n",
      "| 41%   29C    P8    15W / 140W |      5MiB / 16376MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   2  NVIDIA RTX A4000    On   | 00000000:C1:00.0 Off |                  Off |\n",
      "| 41%   28C    P8    12W / 140W |      5MiB / 16376MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   3  NVIDIA RTX A4000    On   | 00000000:E1:00.0 Off |                  Off |\n",
      "| 41%   28C    P8    17W / 140W |      5MiB / 16376MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|    0   N/A  N/A      1783      G   /usr/lib/xorg/Xorg                  4MiB |\n",
      "|    1   N/A  N/A      1783      G   /usr/lib/xorg/Xorg                  4MiB |\n",
      "|    2   N/A  N/A      1783      G   /usr/lib/xorg/Xorg                  4MiB |\n",
      "|    3   N/A  N/A      1783      G   /usr/lib/xorg/Xorg                  4MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0189f842",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57f3a2ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose(\n",
    "        [transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "batch_size = 4\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root=\"./data\", train=True,\n",
    "                                       download=True, transform=transform)\n",
    "\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root=\"./data\", train = False,\n",
    "                                      download=True, transform=transform)\n",
    "\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
    "                                        shuffle=False, num_workers=2)\n",
    "\n",
    "classes = (\"plane\", \"car\", \"bird\", \"cat\",\n",
    "           \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbd509de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frog  truck truck deer \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# fucntion to show an image\n",
    "\n",
    "def imshow(img):\n",
    "    img = img/2 + 0.5\n",
    "    npimg = img.numpy()\n",
    "    plt.figure(figsize=(15,5))\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "# get some random training images\n",
    "\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "#show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "\n",
    "#print labels\n",
    "print(\" \".join(f\"{classes[labels[j]]:5s}\" for j in range(batch_size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6243e4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
    "\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1fe604d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroundTruth:  cat   ship  ship  plane\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print('GroundTruth: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdb26017",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e500b0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90853e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in relevant libraries, and alias where appropriate\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "# Define relevant variables for the ML task\n",
    "batch_size = 64\n",
    "num_classes = 10\n",
    "learning_rate = 0.001\n",
    "num_epochs = 50\n",
    "\n",
    "# Device will determine whether to run the training on GPU or CPU.\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84e4464f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# Use transforms.compose method to reformat images for modeling,\n",
    "# and save to variable all_transforms for later use\n",
    "all_transforms = transforms.Compose([transforms.Resize((32,32)),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],\n",
    "                                                          std=[0.2023, 0.1994, 0.2010])\n",
    "                                     ])\n",
    "# Create Training dataset\n",
    "train_dataset = torchvision.datasets.CIFAR10(root = './data',\n",
    "                                             train = True,\n",
    "                                             transform = all_transforms,\n",
    "                                             download = True)\n",
    "\n",
    "# Create Testing dataset\n",
    "test_dataset = torchvision.datasets.CIFAR10(root = './data',\n",
    "                                            train = False,\n",
    "                                            transform = all_transforms,\n",
    "                                            download=True)\n",
    "\n",
    "# Instantiate loader objects to facilitate processing\n",
    "train_loader = torch.utils.data.DataLoader(dataset = train_dataset,\n",
    "                                           batch_size = batch_size,\n",
    "                                           shuffle = True)\n",
    "\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset = test_dataset,\n",
    "                                           batch_size = batch_size,\n",
    "                                           shuffle = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f18d566f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee968a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_accuracy(model):\n",
    "\n",
    "    with torch.no_grad():\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        for images, labels in train_loader:\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "#         print('Accuracy of the network on the {} train images: {} %'.format(50000, 100 * correct / total))\n",
    "    return 100 * correct / total\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92682805",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e59aba82",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6860c79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_accuracy(model):\n",
    "    \n",
    "    with torch.no_grad():\n",
    "    \n",
    "        correct = 0\n",
    "        total = 0\n",
    "        for images, labels in test_loader:\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "#         print('Accuracy of the network on the {} test images: {} %'.format(10000, 100 * correct / total))\n",
    "    return 100 * correct / total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd21c6bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "35bd5958",
   "metadata": {},
   "source": [
    "## Training models with Configuration 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a6f6ed",
   "metadata": {},
   "source": [
    "## Model A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8318378c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN class\n",
    "class ConvNeuralNet_A(nn.Module):\n",
    "\t#  Determine what layers and their order in CNN object \n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNeuralNet_A, self).__init__()\n",
    "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=7)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.max_pool1 = nn.MaxPool2d(kernel_size = 2, stride = 1)\n",
    "        \n",
    "        self.conv_layer2 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=5)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=, out_channels=64, kernel_size=3)\n",
    "        self.max_pool2 = nn.MaxPool2d(kernel_size = 2, stride = 1)\n",
    "        self.conv_layer3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3)\n",
    "        self.max_pool3 = nn.MaxPool2d(kernel_size = 2, stride = 1)\n",
    "        self.fc1 = nn.Linear(17*17*24, num_classes)\n",
    "#         self.relu1 = nn.ReLU()\n",
    "#         self.fc2 = nn.Linear(128, num_classes)\n",
    "    \n",
    "    # Progresses data across layers    \n",
    "    def forward(self, x):\n",
    "        out = self.conv_layer1(x)\n",
    "        out = self.max_pool1(out)\n",
    "        \n",
    "        out = self.conv_layer2(out)\n",
    "        out = self.max_pool2(out)\n",
    "        \n",
    "        out = self.conv_layer3(out)\n",
    "        out = self.max_pool3(out)\n",
    "        \n",
    "#         out = self.conv_layer2(out)\n",
    "        \n",
    "        \n",
    "#         out = self.conv_layer3(out)\n",
    "#         out = self.conv_layer4(out)\n",
    "#         out = self.max_pool2(out)\n",
    "                \n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        \n",
    "        out = self.fc1(out)\n",
    "#         out = self.relu1(out)\n",
    "#         out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1568d16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_A = ConvNeuralNet_A(num_classes)\n",
    "model_A.to(device)\n",
    "# Set Loss function with criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set optimizer with optimizer\n",
    "optimizer = torch.optim.SGD(model_A.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
    "\n",
    "total_step = len(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "20f6a4e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 1.1448\n",
      "Epoch [2/50], Loss: 1.0176\n",
      "Epoch [3/50], Loss: 1.0958\n",
      "Epoch [4/50], Loss: 1.4455\n",
      "Epoch [5/50], Loss: 1.4039\n",
      "Epoch [6/50], Loss: 1.3580\n",
      "Epoch [7/50], Loss: 0.8527\n",
      "Epoch [8/50], Loss: 1.3694\n",
      "Epoch [9/50], Loss: 1.4761\n",
      "Epoch [10/50], Loss: 1.2155\n",
      "Epoch [11/50], Loss: 1.4571\n",
      "Epoch [12/50], Loss: 0.6576\n",
      "Epoch [13/50], Loss: 1.9499\n",
      "Epoch [14/50], Loss: 1.3246\n",
      "Epoch [15/50], Loss: 1.5172\n",
      "Epoch [16/50], Loss: 1.2378\n",
      "Epoch [17/50], Loss: 1.2541\n",
      "Epoch [18/50], Loss: 0.8955\n",
      "Epoch [19/50], Loss: 1.5671\n",
      "Epoch [20/50], Loss: 0.6944\n",
      "Epoch [21/50], Loss: 1.2069\n",
      "Epoch [22/50], Loss: 0.7898\n",
      "Epoch [23/50], Loss: 1.1067\n",
      "Epoch [24/50], Loss: 1.2406\n",
      "Epoch [25/50], Loss: 0.9595\n",
      "Epoch [26/50], Loss: 0.8956\n",
      "Epoch [27/50], Loss: 1.7134\n",
      "Epoch [28/50], Loss: 1.5611\n",
      "Epoch [29/50], Loss: 1.3486\n",
      "Epoch [30/50], Loss: 1.8616\n",
      "Epoch [31/50], Loss: 1.3745\n",
      "Epoch [32/50], Loss: 1.5243\n",
      "Epoch [33/50], Loss: 1.3056\n",
      "Epoch [34/50], Loss: 1.2032\n",
      "Epoch [35/50], Loss: 1.0216\n",
      "Epoch [36/50], Loss: 1.1136\n",
      "Epoch [37/50], Loss: 1.9965\n",
      "Epoch [38/50], Loss: 1.0111\n",
      "Epoch [39/50], Loss: 1.9023\n",
      "Epoch [40/50], Loss: 1.1818\n",
      "Epoch [41/50], Loss: 1.3363\n",
      "Epoch [42/50], Loss: 1.4568\n",
      "Epoch [43/50], Loss: 1.6542\n",
      "Epoch [44/50], Loss: 2.4560\n",
      "Epoch [45/50], Loss: 2.4710\n",
      "Epoch [46/50], Loss: 0.9294\n",
      "Epoch [47/50], Loss: 1.2590\n",
      "Epoch [48/50], Loss: 0.9864\n",
      "Epoch [49/50], Loss: 1.7690\n",
      "Epoch [50/50], Loss: 1.4347\n",
      "CPU times: user 17min 2s, sys: 1.77 s, total: 17min 4s\n",
      "Wall time: 16min 9s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "loss_train = []\n",
    "loss_test = []\n",
    "A_train = []\n",
    "A_test = []\n",
    "for epoch in range(num_epochs):\n",
    "\t#Load in the data in batches using the train_loader object\n",
    "    l_train = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(train_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_A(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_train += loss\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    l_train /=  samples\n",
    "    loss_train.append(float(l_train.cpu().detach().numpy()))\n",
    "    \n",
    "    l_test = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(test_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_A(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_test += loss\n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "    l_test /=  samples\n",
    "    loss_test.append(float(l_test.cpu().detach().numpy()))\n",
    "    \n",
    "    A_train.append(train_accuracy(model_A))\n",
    "    A_test.append(test_accuracy(model_A))\n",
    "    \n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "    \n",
    "plt.figure(figsize = (15,5))    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(loss_train, label = \"Training set\")\n",
    "plt.plot(loss_test, label = \"Test set\")\n",
    "plt.title(\"Loss Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(A_train, label = \"Training set\")\n",
    "plt.plot(A_test, label = \"Test set\")\n",
    "plt.title(\"Accuracy Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"model_A.jpg\")\n",
    "    \n",
    "#     for i, \n",
    "        \n",
    "\n",
    "    \n",
    "#     print(f\"Accuracy after {epoch  +1} :\")\n",
    "#     print(f\"Train set: {train_accuracy(model_A)}\")\n",
    "# print(test_accuracy(model_A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "597f6113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.modules of ConvNeuralNet_A(\n",
       "  (conv_layer1): Conv2d(3, 6, kernel_size=(7, 7), stride=(1, 1))\n",
       "  (max_pool1): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer2): Conv2d(6, 12, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (max_pool2): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer3): Conv2d(12, 24, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool3): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=2904, out_features=10, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_A.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3f86a6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f610d970",
   "metadata": {},
   "source": [
    "### Model B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "400b0805",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN class\n",
    "class ConvNeuralNet_B(nn.Module):\n",
    "\t#  Determine what layers and their order in CNN object \n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNeuralNet_B, self).__init__()\n",
    "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)\n",
    "        self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.max_pool1 = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
    "        \n",
    "        self.conv_layer3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3)\n",
    "        self.conv_layer4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3)\n",
    "        self.max_pool2 = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
    "        \n",
    "        self.fc1 = nn.Linear(1600, 128)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(128, num_classes)\n",
    "    \n",
    "    # Progresses data across layers    \n",
    "    def forward(self, x):\n",
    "        out = self.conv_layer1(x)\n",
    "        out = self.conv_layer2(out)\n",
    "        out = self.max_pool1(out)\n",
    "        \n",
    "        out = self.conv_layer3(out)\n",
    "        out = self.conv_layer4(out)\n",
    "        out = self.max_pool2(out)\n",
    "                \n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        \n",
    "        out = self.fc1(out)\n",
    "        out = self.relu1(out)\n",
    "        out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "62679815",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_B = ConvNeuralNet_B(num_classes)\n",
    "model_B.to(device)\n",
    "# Set Loss function with criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set optimizer with optimizer\n",
    "optimizer = torch.optim.SGD(model_B.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
    "\n",
    "total_step = len(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b621dea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "109c24d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "# for epoch in range(num_epochs):\n",
    "# \t#Load in the data in batches using the train_loader object\n",
    "#     for i, (images, labels) in enumerate(train_loader):  \n",
    "#         # Move tensors to the configured device\n",
    "#         images = images.to(device)\n",
    "#         labels = labels.to(device)\n",
    "        \n",
    "#         # Forward pass\n",
    "#         outputs = model_B(images)\n",
    "#         loss = criterion(outputs, labels)\n",
    "        \n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "\n",
    "#     print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d8523e76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 1.7983\n",
      "Epoch [2/50], Loss: 2.0820\n",
      "Epoch [3/50], Loss: 1.1793\n",
      "Epoch [4/50], Loss: 1.2244\n",
      "Epoch [5/50], Loss: 1.5258\n",
      "Epoch [6/50], Loss: 0.8974\n",
      "Epoch [7/50], Loss: 0.7618\n",
      "Epoch [8/50], Loss: 0.6551\n",
      "Epoch [9/50], Loss: 1.4251\n",
      "Epoch [10/50], Loss: 0.9695\n",
      "Epoch [11/50], Loss: 1.2931\n",
      "Epoch [12/50], Loss: 0.8527\n",
      "Epoch [13/50], Loss: 0.6479\n",
      "Epoch [14/50], Loss: 0.8924\n",
      "Epoch [15/50], Loss: 0.7110\n",
      "Epoch [16/50], Loss: 0.8918\n",
      "Epoch [17/50], Loss: 0.3279\n",
      "Epoch [18/50], Loss: 0.9737\n",
      "Epoch [19/50], Loss: 1.1255\n",
      "Epoch [20/50], Loss: 0.8618\n",
      "Epoch [21/50], Loss: 0.8818\n",
      "Epoch [22/50], Loss: 0.2105\n",
      "Epoch [23/50], Loss: 0.8834\n",
      "Epoch [24/50], Loss: 0.7210\n",
      "Epoch [25/50], Loss: 1.3678\n",
      "Epoch [26/50], Loss: 0.9883\n",
      "Epoch [27/50], Loss: 0.6160\n",
      "Epoch [28/50], Loss: 1.7218\n",
      "Epoch [29/50], Loss: 1.0705\n",
      "Epoch [30/50], Loss: 1.0815\n",
      "Epoch [31/50], Loss: 2.0309\n",
      "Epoch [32/50], Loss: 1.5084\n",
      "Epoch [33/50], Loss: 0.6959\n",
      "Epoch [34/50], Loss: 1.4970\n",
      "Epoch [35/50], Loss: 0.9302\n",
      "Epoch [36/50], Loss: 0.7432\n",
      "Epoch [37/50], Loss: 1.8924\n",
      "Epoch [38/50], Loss: 1.0731\n",
      "Epoch [39/50], Loss: 0.4063\n",
      "Epoch [40/50], Loss: 0.8556\n",
      "Epoch [41/50], Loss: 2.5985\n",
      "Epoch [42/50], Loss: 1.9081\n",
      "Epoch [43/50], Loss: 1.3200\n",
      "Epoch [44/50], Loss: 1.6650\n",
      "Epoch [45/50], Loss: 0.4553\n",
      "Epoch [46/50], Loss: 1.3371\n",
      "Epoch [47/50], Loss: 3.0818\n",
      "Epoch [48/50], Loss: 2.7767\n",
      "Epoch [49/50], Loss: 1.0472\n",
      "Epoch [50/50], Loss: 0.8136\n",
      "CPU times: user 17min 12s, sys: 2.06 s, total: 17min 14s\n",
      "Wall time: 16min 20s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "loss_train = []\n",
    "loss_test = []\n",
    "A_train = []\n",
    "A_test = []\n",
    "for epoch in range(num_epochs):\n",
    "\t#Load in the data in batches using the train_loader object\n",
    "    l_train = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(train_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_B(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_train += loss\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    l_train /=  samples\n",
    "    loss_train.append(float(l_train.cpu().detach().numpy()))\n",
    "    \n",
    "    l_test = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(test_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_B(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_test += loss\n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "    l_test /=  samples\n",
    "    loss_test.append(float(l_test.cpu().detach().numpy()))\n",
    "    \n",
    "    A_train.append(train_accuracy(model_B))\n",
    "    A_test.append(test_accuracy(model_B))\n",
    "    \n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "    \n",
    "plt.figure(figsize = (15,5))    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(loss_train, label = \"Training set\")\n",
    "plt.plot(loss_test, label = \"Test set\")\n",
    "plt.title(\"Loss Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(A_train, label = \"Training set\")\n",
    "plt.plot(A_test, label = \"Test set\")\n",
    "plt.title(\"Accuracy Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"model_B.jpg\")\n",
    "    \n",
    "#     for i, \n",
    "        \n",
    "\n",
    "    \n",
    "#     print(f\"Accuracy after {epoch  +1} :\")\n",
    "#     print(f\"Train set: {train_accuracy(model_A)}\")\n",
    "# print(test_accuracy(model_A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fe040493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.modules of ConvNeuralNet_B(\n",
       "  (conv_layer1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (conv_layer2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (conv_layer4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=1600, out_features=128, bias=True)\n",
       "  (relu1): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_B.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9642f0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20d7c010",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0bc5715",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "676dafc2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12cb1c01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1ec10e57",
   "metadata": {},
   "source": [
    "### Model C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ccc71733",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN class\n",
    "class ConvNeuralNet_C(nn.Module):\n",
    "\t#  Determine what layers and their order in CNN object \n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNeuralNet_C, self).__init__()\n",
    "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=7)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.max_pool1 = nn.MaxPool2d(kernel_size = 2, stride = 1)\n",
    "        \n",
    "        self.conv_layer2 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=5)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=, out_channels=64, kernel_size=3)\n",
    "        self.max_pool2 = nn.MaxPool2d(kernel_size = 2, stride = 1)\n",
    "        self.conv_layer3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3)\n",
    "        self.max_pool3 = nn.MaxPool2d(kernel_size = 2, stride = 1)\n",
    "        self.fc1 = nn.Linear(17*17*24, 128)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(128, num_classes)\n",
    "    \n",
    "    # Progresses data across layers    \n",
    "    def forward(self, x):\n",
    "        out = self.conv_layer1(x)\n",
    "        out = self.max_pool1(out)\n",
    "        \n",
    "        out = self.conv_layer2(out)\n",
    "        out = self.max_pool2(out)\n",
    "        \n",
    "        out = self.conv_layer3(out)\n",
    "        out = self.max_pool3(out)\n",
    "        \n",
    "#         out = self.conv_layer2(out)\n",
    "        \n",
    "        \n",
    "#         out = self.conv_layer3(out)\n",
    "#         out = self.conv_layer4(out)\n",
    "#         out = self.max_pool2(out)\n",
    "                \n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        \n",
    "        out = self.fc1(out)\n",
    "        out = self.relu1(out)\n",
    "        out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e91a5512",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_C = ConvNeuralNet_C(num_classes)\n",
    "model_C.to(device)\n",
    "# Set Loss function with criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set optimizer with optimizer\n",
    "optimizer = torch.optim.SGD(model_C.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
    "\n",
    "total_step = len(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "902d48f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 1.6737\n",
      "Epoch [2/50], Loss: 1.3655\n",
      "Epoch [3/50], Loss: 1.2144\n",
      "Epoch [4/50], Loss: 1.7027\n",
      "Epoch [5/50], Loss: 1.3546\n",
      "Epoch [6/50], Loss: 1.0872\n",
      "Epoch [7/50], Loss: 1.3264\n",
      "Epoch [8/50], Loss: 0.8065\n",
      "Epoch [9/50], Loss: 1.1487\n",
      "Epoch [10/50], Loss: 1.2030\n",
      "Epoch [11/50], Loss: 0.5346\n",
      "Epoch [12/50], Loss: 1.4901\n",
      "Epoch [13/50], Loss: 1.0289\n",
      "Epoch [14/50], Loss: 1.1648\n",
      "Epoch [15/50], Loss: 0.8375\n",
      "Epoch [16/50], Loss: 0.6406\n",
      "Epoch [17/50], Loss: 1.3915\n",
      "Epoch [18/50], Loss: 1.4511\n",
      "Epoch [19/50], Loss: 0.7517\n",
      "Epoch [20/50], Loss: 0.5016\n",
      "Epoch [21/50], Loss: 1.6189\n",
      "Epoch [22/50], Loss: 0.8010\n",
      "Epoch [23/50], Loss: 2.4606\n",
      "Epoch [24/50], Loss: 1.6286\n",
      "Epoch [25/50], Loss: 1.9067\n",
      "Epoch [26/50], Loss: 1.3961\n",
      "Epoch [27/50], Loss: 2.0816\n",
      "Epoch [28/50], Loss: 0.7161\n",
      "Epoch [29/50], Loss: 2.8109\n",
      "Epoch [30/50], Loss: 0.7235\n",
      "Epoch [31/50], Loss: 0.9724\n",
      "Epoch [32/50], Loss: 1.3268\n",
      "Epoch [33/50], Loss: 2.6305\n",
      "Epoch [34/50], Loss: 2.8708\n",
      "Epoch [35/50], Loss: 1.1654\n",
      "Epoch [36/50], Loss: 2.1185\n",
      "Epoch [37/50], Loss: 1.1264\n",
      "Epoch [38/50], Loss: 2.1752\n",
      "Epoch [39/50], Loss: 2.8946\n",
      "Epoch [40/50], Loss: 1.5906\n",
      "Epoch [41/50], Loss: 1.4894\n",
      "Epoch [42/50], Loss: 2.6580\n",
      "Epoch [43/50], Loss: 3.6168\n",
      "Epoch [44/50], Loss: 1.6175\n",
      "Epoch [45/50], Loss: 2.4019\n",
      "Epoch [46/50], Loss: 3.1427\n",
      "Epoch [47/50], Loss: 2.0905\n",
      "Epoch [48/50], Loss: 0.8544\n",
      "Epoch [49/50], Loss: 1.2303\n",
      "Epoch [50/50], Loss: 2.5162\n",
      "CPU times: user 16min 54s, sys: 1.77 s, total: 16min 56s\n",
      "Wall time: 16min 1s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "loss_train = []\n",
    "loss_test = []\n",
    "A_train = []\n",
    "A_test = []\n",
    "for epoch in range(num_epochs):\n",
    "\t#Load in the data in batches using the train_loader object\n",
    "    l_train = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(train_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_C(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_train += loss\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    l_train /=  samples\n",
    "    loss_train.append(float(l_train.cpu().detach().numpy()))\n",
    "    \n",
    "    l_test = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(test_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_C(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_test += loss\n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "    l_test /=  samples\n",
    "    loss_test.append(float(l_test.cpu().detach().numpy()))\n",
    "    \n",
    "    A_train.append(train_accuracy(model_C))\n",
    "    A_test.append(test_accuracy(model_C))\n",
    "    \n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "    \n",
    "plt.figure(figsize = (15,5))    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(loss_train, label = \"Training set\")\n",
    "plt.plot(loss_test, label = \"Test set\")\n",
    "plt.title(\"Loss Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(A_train, label = \"Training set\")\n",
    "plt.plot(A_test, label = \"Test set\")\n",
    "plt.title(\"Accuracy Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"model_C.jpg\")\n",
    "    \n",
    "#     for i, \n",
    "        \n",
    "\n",
    "    \n",
    "#     print(f\"Accuracy after {epoch  +1} :\")\n",
    "#     print(f\"Train set: {train_accuracy(model_A)}\")\n",
    "# print(test_accuracy(model_A))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d473e79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "f8430025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.modules of ConvNeuralNet_C(\n",
       "  (conv_layer1): Conv2d(3, 6, kernel_size=(7, 7), stride=(1, 1))\n",
       "  (max_pool1): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer2): Conv2d(6, 12, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (max_pool2): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer3): Conv2d(12, 24, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool3): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=6936, out_features=128, bias=True)\n",
       "  (relu1): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_C.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0af4a8fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "282469ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d414671e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c250f180",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e058766e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71e4aaf6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4909fe86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "592f7263",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3db9eb0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "68c3f7ac",
   "metadata": {},
   "source": [
    "# Training MOdels with Configuration 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8411afb",
   "metadata": {},
   "source": [
    "## Model A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ff3e5ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN class\n",
    "class ConvNeuralNet_A(nn.Module):\n",
    "\t#  Determine what layers and their order in CNN object \n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNeuralNet_A, self).__init__()\n",
    "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=7)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.max_pool1 = nn.MaxPool2d(kernel_size = 3, stride = 1)\n",
    "        \n",
    "        self.conv_layer2 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=5)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=, out_channels=64, kernel_size=3)\n",
    "        self.max_pool2 = nn.MaxPool2d(kernel_size = 3, stride = 1)\n",
    "        self.conv_layer3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3)\n",
    "        self.max_pool3 = nn.MaxPool2d(kernel_size = 3, stride = 1)\n",
    "        self.fc1 = nn.Linear(14*14*24, num_classes)\n",
    "#         self.relu1 = nn.ReLU()\n",
    "#         self.fc2 = nn.Linear(128, num_classes)\n",
    "    \n",
    "    # Progresses data across layers    \n",
    "    def forward(self, x):\n",
    "        out = self.conv_layer1(x)\n",
    "        out = self.max_pool1(out)\n",
    "        \n",
    "        out = self.conv_layer2(out)\n",
    "        out = self.max_pool2(out)\n",
    "        \n",
    "        out = self.conv_layer3(out)\n",
    "        out = self.max_pool3(out)\n",
    "        \n",
    "#         out = self.conv_layer2(out)\n",
    "        \n",
    "        \n",
    "#         out = self.conv_layer3(out)\n",
    "#         out = self.conv_layer4(out)\n",
    "#         out = self.max_pool2(out)\n",
    "                \n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        \n",
    "        out = self.fc1(out)\n",
    "#         out = self.relu1(out)\n",
    "#         out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a0dab36",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_A = ConvNeuralNet_A(num_classes)\n",
    "model_A.to(device)\n",
    "# Set Loss function with criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set optimizer with optimizer\n",
    "optimizer = torch.optim.SGD(model_A.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
    "\n",
    "total_step = len(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "28e76502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 1.4306\n",
      "Epoch [2/50], Loss: 1.5088\n",
      "Epoch [3/50], Loss: 1.9421\n",
      "Epoch [4/50], Loss: 0.8221\n",
      "Epoch [5/50], Loss: 1.3556\n",
      "Epoch [6/50], Loss: 1.6585\n",
      "Epoch [7/50], Loss: 1.2773\n",
      "Epoch [8/50], Loss: 1.3795\n",
      "Epoch [9/50], Loss: 1.1067\n",
      "Epoch [10/50], Loss: 1.2511\n",
      "Epoch [11/50], Loss: 1.2936\n",
      "Epoch [12/50], Loss: 0.8167\n",
      "Epoch [13/50], Loss: 1.5472\n",
      "Epoch [14/50], Loss: 1.2183\n",
      "Epoch [15/50], Loss: 1.1743\n",
      "Epoch [16/50], Loss: 1.8342\n",
      "Epoch [17/50], Loss: 1.2800\n",
      "Epoch [18/50], Loss: 0.7205\n",
      "Epoch [19/50], Loss: 0.6479\n",
      "Epoch [20/50], Loss: 0.9663\n",
      "Epoch [21/50], Loss: 1.1998\n",
      "Epoch [22/50], Loss: 1.2911\n",
      "Epoch [23/50], Loss: 0.8246\n",
      "Epoch [24/50], Loss: 1.2561\n",
      "Epoch [25/50], Loss: 0.8116\n",
      "Epoch [26/50], Loss: 0.8385\n",
      "Epoch [27/50], Loss: 1.2628\n",
      "Epoch [28/50], Loss: 1.2483\n",
      "Epoch [29/50], Loss: 0.8242\n",
      "Epoch [30/50], Loss: 1.0680\n",
      "Epoch [31/50], Loss: 1.1355\n",
      "Epoch [32/50], Loss: 1.5517\n",
      "Epoch [33/50], Loss: 0.8668\n",
      "Epoch [34/50], Loss: 0.7979\n",
      "Epoch [35/50], Loss: 0.9515\n",
      "Epoch [36/50], Loss: 1.3050\n",
      "Epoch [37/50], Loss: 1.0788\n",
      "Epoch [38/50], Loss: 0.8712\n",
      "Epoch [39/50], Loss: 1.1139\n",
      "Epoch [40/50], Loss: 1.1339\n",
      "Epoch [41/50], Loss: 1.0929\n",
      "Epoch [42/50], Loss: 1.6530\n",
      "Epoch [43/50], Loss: 1.5001\n",
      "Epoch [44/50], Loss: 1.8339\n",
      "Epoch [45/50], Loss: 1.6347\n",
      "Epoch [46/50], Loss: 0.9101\n",
      "Epoch [47/50], Loss: 0.9984\n",
      "Epoch [48/50], Loss: 1.0064\n",
      "Epoch [49/50], Loss: 0.8288\n",
      "Epoch [50/50], Loss: 1.0493\n",
      "CPU times: user 16min 12s, sys: 985 ms, total: 16min 13s\n",
      "Wall time: 15min 18s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "loss_train = []\n",
    "loss_test = []\n",
    "A_train = []\n",
    "A_test = []\n",
    "for epoch in range(num_epochs):\n",
    "\t#Load in the data in batches using the train_loader object\n",
    "    l_train = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(train_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_A(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_train += loss\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    l_train /=  samples\n",
    "    loss_train.append(float(l_train.cpu().detach().numpy()))\n",
    "    \n",
    "    l_test = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(test_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_A(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_test += loss\n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "    l_test /=  samples\n",
    "    loss_test.append(float(l_test.cpu().detach().numpy()))\n",
    "    \n",
    "    A_train.append(train_accuracy(model_A))\n",
    "    A_test.append(test_accuracy(model_A))\n",
    "    \n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "    \n",
    "plt.figure(figsize = (15,5))    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(loss_train, label = \"Training set\")\n",
    "plt.plot(loss_test, label = \"Test set\")\n",
    "plt.title(\"Loss Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(A_train, label = \"Training set\")\n",
    "plt.plot(A_test, label = \"Test set\")\n",
    "plt.title(\"Accuracy Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"config2_model_A.jpg\")\n",
    "    \n",
    "#     for i, \n",
    "        \n",
    "\n",
    "    \n",
    "#     print(f\"Accuracy after {epoch  +1} :\")\n",
    "#     print(f\"Train set: {train_accuracy(model_A)}\")\n",
    "# print(test_accuracy(model_A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a98aacb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.modules of ConvNeuralNet_A(\n",
       "  (conv_layer1): Conv2d(3, 6, kernel_size=(7, 7), stride=(1, 1))\n",
       "  (max_pool1): MaxPool2d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer2): Conv2d(6, 12, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (max_pool2): MaxPool2d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer3): Conv2d(12, 24, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool3): MaxPool2d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=4704, out_features=10, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_A.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13f61a93",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46dbd10d",
   "metadata": {},
   "source": [
    "### Model B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9dfdacbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN class\n",
    "class ConvNeuralNet_B(nn.Module):\n",
    "\t#  Determine what layers and their order in CNN object \n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNeuralNet_B, self).__init__()\n",
    "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)\n",
    "        self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.max_pool1 = nn.MaxPool2d(kernel_size = 3, stride = 1)\n",
    "        \n",
    "        self.conv_layer3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3)\n",
    "        self.conv_layer4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3)\n",
    "        self.max_pool2 = nn.MaxPool2d(kernel_size = 3, stride = 1)\n",
    "        \n",
    "        self.fc1 = nn.Linear(20*20*64, 128)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(128, num_classes)\n",
    "    \n",
    "    # Progresses data across layers    \n",
    "    def forward(self, x):\n",
    "        out = self.conv_layer1(x)\n",
    "        out = self.conv_layer2(out)\n",
    "        out = self.max_pool1(out)\n",
    "        \n",
    "        out = self.conv_layer3(out)\n",
    "        out = self.conv_layer4(out)\n",
    "        out = self.max_pool2(out)\n",
    "                \n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        \n",
    "        out = self.fc1(out)\n",
    "        out = self.relu1(out)\n",
    "        out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa64a5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_B = ConvNeuralNet_B(num_classes)\n",
    "model_B.to(device)\n",
    "# Set Loss function with criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set optimizer with optimizer\n",
    "optimizer = torch.optim.SGD(model_B.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
    "\n",
    "total_step = len(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5f9ad20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e764e2c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "# for epoch in range(num_epochs):\n",
    "# \t#Load in the data in batches using the train_loader object\n",
    "#     for i, (images, labels) in enumerate(train_loader):  \n",
    "#         # Move tensors to the configured device\n",
    "#         images = images.to(device)\n",
    "#         labels = labels.to(device)\n",
    "        \n",
    "#         # Forward pass\n",
    "#         outputs = model_B(images)\n",
    "#         loss = criterion(outputs, labels)\n",
    "        \n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "\n",
    "#     print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6dbdf5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 1.4714\n",
      "Epoch [2/50], Loss: 0.8857\n",
      "Epoch [3/50], Loss: 1.1961\n",
      "Epoch [4/50], Loss: 0.8879\n",
      "Epoch [5/50], Loss: 0.8457\n",
      "Epoch [6/50], Loss: 0.6881\n",
      "Epoch [7/50], Loss: 0.6236\n",
      "Epoch [8/50], Loss: 0.3237\n",
      "Epoch [9/50], Loss: 0.9342\n",
      "Epoch [10/50], Loss: 1.0907\n",
      "Epoch [11/50], Loss: 0.3305\n",
      "Epoch [12/50], Loss: 0.9047\n",
      "Epoch [13/50], Loss: 0.8608\n",
      "Epoch [14/50], Loss: 0.5704\n",
      "Epoch [15/50], Loss: 1.1895\n",
      "Epoch [16/50], Loss: 0.6586\n",
      "Epoch [17/50], Loss: 0.7711\n",
      "Epoch [18/50], Loss: 0.5295\n",
      "Epoch [19/50], Loss: 1.2597\n",
      "Epoch [20/50], Loss: 0.4516\n",
      "Epoch [21/50], Loss: 0.8443\n",
      "Epoch [22/50], Loss: 0.8532\n",
      "Epoch [23/50], Loss: 1.0561\n",
      "Epoch [24/50], Loss: 0.9394\n",
      "Epoch [25/50], Loss: 2.8659\n",
      "Epoch [26/50], Loss: 2.0623\n",
      "Epoch [27/50], Loss: 1.3715\n",
      "Epoch [28/50], Loss: 0.8971\n",
      "Epoch [29/50], Loss: 0.9510\n",
      "Epoch [30/50], Loss: 1.3810\n",
      "Epoch [31/50], Loss: 1.5190\n",
      "Epoch [32/50], Loss: 1.0109\n",
      "Epoch [33/50], Loss: 1.3505\n",
      "Epoch [34/50], Loss: 0.6971\n",
      "Epoch [35/50], Loss: 1.6407\n",
      "Epoch [36/50], Loss: 2.5057\n",
      "Epoch [37/50], Loss: 0.7058\n",
      "Epoch [38/50], Loss: 3.2214\n",
      "Epoch [39/50], Loss: 1.9345\n",
      "Epoch [40/50], Loss: 0.8641\n",
      "Epoch [41/50], Loss: 0.6431\n",
      "Epoch [42/50], Loss: 1.1335\n",
      "Epoch [43/50], Loss: 0.3576\n",
      "Epoch [44/50], Loss: 2.0055\n",
      "Epoch [45/50], Loss: 1.1468\n",
      "Epoch [46/50], Loss: 2.8245\n",
      "Epoch [47/50], Loss: 0.6186\n",
      "Epoch [48/50], Loss: 0.3093\n",
      "Epoch [49/50], Loss: 0.8886\n",
      "Epoch [50/50], Loss: 0.3433\n",
      "CPU times: user 17min 10s, sys: 1.04 s, total: 17min 11s\n",
      "Wall time: 16min 16s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "loss_train = []\n",
    "loss_test = []\n",
    "A_train = []\n",
    "A_test = []\n",
    "for epoch in range(num_epochs):\n",
    "\t#Load in the data in batches using the train_loader object\n",
    "    l_train = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(train_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_B(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_train += loss\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    l_train /=  samples\n",
    "    loss_train.append(float(l_train.cpu().detach().numpy()))\n",
    "    \n",
    "    l_test = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(test_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_B(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_test += loss\n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "    l_test /=  samples\n",
    "    loss_test.append(float(l_test.cpu().detach().numpy()))\n",
    "    \n",
    "    A_train.append(train_accuracy(model_B))\n",
    "    A_test.append(test_accuracy(model_B))\n",
    "    \n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "    \n",
    "plt.figure(figsize = (15,5))    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(loss_train, label = \"Training set\")\n",
    "plt.plot(loss_test, label = \"Test set\")\n",
    "plt.title(\"Loss Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(A_train, label = \"Training set\")\n",
    "plt.plot(A_test, label = \"Test set\")\n",
    "plt.title(\"Accuracy Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"config2_model_B.jpg\")\n",
    "    \n",
    "#     for i, \n",
    "        \n",
    "\n",
    "    \n",
    "#     print(f\"Accuracy after {epoch  +1} :\")\n",
    "#     print(f\"Train set: {train_accuracy(model_A)}\")\n",
    "# print(test_accuracy(model_A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e217a937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.modules of ConvNeuralNet_B(\n",
       "  (conv_layer1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (conv_layer2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool1): MaxPool2d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (conv_layer4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool2): MaxPool2d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=25600, out_features=128, bias=True)\n",
       "  (relu1): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_B.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "317bd56b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9860d4d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58d8ce89",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea95d861",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0a88a923",
   "metadata": {},
   "source": [
    "### Model C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df57c690",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN class\n",
    "class ConvNeuralNet_C(nn.Module):\n",
    "\t#  Determine what layers and their order in CNN object \n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNeuralNet_C, self).__init__()\n",
    "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=7)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.max_pool1 = nn.MaxPool2d(kernel_size = 3, stride = 1)\n",
    "        \n",
    "        self.conv_layer2 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=5)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=, out_channels=64, kernel_size=3)\n",
    "        self.max_pool2 = nn.MaxPool2d(kernel_size = 3, stride = 1)\n",
    "        self.conv_layer3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3)\n",
    "        self.max_pool3 = nn.MaxPool2d(kernel_size = 3, stride = 1)\n",
    "        self.fc1 = nn.Linear(14*14*24, 128)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(128, num_classes)\n",
    "    \n",
    "    # Progresses data across layers    \n",
    "    def forward(self, x):\n",
    "        out = self.conv_layer1(x)\n",
    "        out = self.max_pool1(out)\n",
    "        \n",
    "        out = self.conv_layer2(out)\n",
    "        out = self.max_pool2(out)\n",
    "        \n",
    "        out = self.conv_layer3(out)\n",
    "        out = self.max_pool3(out)\n",
    "        \n",
    "#         out = self.conv_layer2(out)\n",
    "        \n",
    "        \n",
    "#         out = self.conv_layer3(out)\n",
    "#         out = self.conv_layer4(out)\n",
    "#         out = self.max_pool2(out)\n",
    "                \n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        \n",
    "        out = self.fc1(out)\n",
    "        out = self.relu1(out)\n",
    "        out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fafad746",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_C = ConvNeuralNet_C(num_classes)\n",
    "model_C.to(device)\n",
    "# Set Loss function with criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set optimizer with optimizer\n",
    "optimizer = torch.optim.SGD(model_C.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
    "\n",
    "total_step = len(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5683013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 1.6969\n",
      "Epoch [2/50], Loss: 1.7747\n",
      "Epoch [3/50], Loss: 1.0057\n",
      "Epoch [4/50], Loss: 1.1025\n",
      "Epoch [5/50], Loss: 1.0696\n",
      "Epoch [6/50], Loss: 1.3004\n",
      "Epoch [7/50], Loss: 1.4717\n",
      "Epoch [8/50], Loss: 1.0869\n",
      "Epoch [9/50], Loss: 1.0174\n",
      "Epoch [10/50], Loss: 0.8995\n",
      "Epoch [11/50], Loss: 0.6239\n",
      "Epoch [12/50], Loss: 1.2359\n",
      "Epoch [13/50], Loss: 1.1453\n",
      "Epoch [14/50], Loss: 0.7219\n",
      "Epoch [15/50], Loss: 0.4429\n",
      "Epoch [16/50], Loss: 1.9169\n",
      "Epoch [17/50], Loss: 1.2149\n",
      "Epoch [18/50], Loss: 0.9133\n",
      "Epoch [19/50], Loss: 0.8480\n",
      "Epoch [20/50], Loss: 1.2226\n",
      "Epoch [21/50], Loss: 0.5876\n",
      "Epoch [22/50], Loss: 1.4706\n",
      "Epoch [23/50], Loss: 1.1127\n",
      "Epoch [24/50], Loss: 0.7125\n",
      "Epoch [25/50], Loss: 1.1989\n",
      "Epoch [26/50], Loss: 0.7093\n",
      "Epoch [27/50], Loss: 0.4921\n",
      "Epoch [28/50], Loss: 0.4333\n",
      "Epoch [29/50], Loss: 0.8987\n",
      "Epoch [30/50], Loss: 1.0452\n",
      "Epoch [31/50], Loss: 1.0195\n",
      "Epoch [32/50], Loss: 1.6102\n",
      "Epoch [33/50], Loss: 0.6298\n",
      "Epoch [34/50], Loss: 0.9045\n",
      "Epoch [35/50], Loss: 0.7397\n",
      "Epoch [36/50], Loss: 1.1417\n",
      "Epoch [37/50], Loss: 1.6223\n",
      "Epoch [38/50], Loss: 1.0700\n",
      "Epoch [39/50], Loss: 0.8336\n",
      "Epoch [40/50], Loss: 0.8372\n",
      "Epoch [41/50], Loss: 0.9383\n",
      "Epoch [42/50], Loss: 0.8529\n",
      "Epoch [43/50], Loss: 0.8893\n",
      "Epoch [44/50], Loss: 1.3578\n",
      "Epoch [45/50], Loss: 0.9619\n",
      "Epoch [46/50], Loss: 2.3668\n",
      "Epoch [47/50], Loss: 1.3759\n",
      "Epoch [48/50], Loss: 0.5770\n",
      "Epoch [49/50], Loss: 0.9265\n",
      "Epoch [50/50], Loss: 1.1808\n",
      "CPU times: user 16min 20s, sys: 1.15 s, total: 16min 21s\n",
      "Wall time: 15min 27s\n"
     ]
    },
    {
     "data": {
      "image/png": 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VLEjN4PlLk4kMCQDg50Vb+Mvn8wgO8GPUjT3o0jC6PJ/aCfFmm4Rrga+sswpYC7QsbkNr7RvW2i7W2i5xcXEn/cD1o4M1RVNEqrS//vWvPPjgg/Tu3ZuCgrIvAD+e4OBgXnnlFYYMGUKfPn1ISEggIiLiqO0+++wz2rZtS3JyMsuWLeOqq66idevW/Otf/+K0006jffv2DB48mM2bNwMwYsQI2rdvr6IUchi9n0XkRM3bsItPZ6ZwepsEMrLzuPLtGaRnlb2kx/ItmVz97gyiQwP46IbuRNcKOHBfkL8vT17Qnv+c144pq3dw9kuTWLQxg2d+XcHIj2bTNCGM72/vUymSO/Bwm4SiNXjfW2vbFnPfq8BWa+2jxpgEYA7QwVp7zLIy5dEm4YHRCxi3dBuzHhp0UscRkepJ5c2dPXv2EBoairWWW2+9lWbNmnH33Xd77PHUJuHkVfY2Cd5UGd7PIlK+Cgotw16exLbd+/jtL6eyaONurn53Bq3rhPPxDd2pFVi6yYrrdmRx0etT8THwxU29SIoJKXHbuSnp3PzRHLbszgHgos6JPHZuW4+vqzuSt9okjAKmAi2MManGmOuNMSONMSOLNnkM6GWMWQj8Btx/vOSuvCRGBbNjz74TKnsqIlJTvPnmmyQnJ9OmTRsyMjK46aabvB2SyAnT+1mk+vlk+noWbdzNQ0NbExbkT88mMbx4WUcWpO5i5Eezyc0/fnmPzRnZDH9rOvkFhXx0ffdjJncAHZOi+P6OPgxLrsu/zm3Lfy9sX+HJ3fF4sopm8TWID96/CTjNU49/LPsraaam76VpfJg3QhARqfTuvvtuj45wiFQkvZ9Fqpftmfv47y/L6d00hrPb1zlw++ltavPE+e356+gF3PP5PJ6/tONRRVEAsvbl88OCzbw8fhW7s/P45MYeNEsoXV4QGxrI85d2LLfnUt68WWTFaxKjilol7MxWgiciIiIiUkEKCi2rtu2heUIorlvaiXn8p6Xk5BXwz2FtjzrOxV3rk743l8d/WkZUSAD/HNYGYwzWWuanZvDZzBS+nbeJrNwCmsaH8u61XWmXePS63KqqRiZ49aNcZR21ShARERERqRjWWv765QJGz0mlQ2IEtw1oxqBW8WVO9KavSeOrORu5pV8TmpTQFuGmU5uwMyuX1yesITTIj4SwQD6duYFlWzIJ9vdlaPs6XNqtPp2Sok4q0ayMamSCFxcWSKCfDxvS1SpBRERERKQiPD12BaPnpDIsuS5zU3Zx4wezaFUnnNsHNGVIm9r4FDOV8kh5BYU8PGYR9SKDuX1As2Nu+8AZLdmZlcur41cD0K5eBP8+ry3ndKhLWFD1bW9SIxM8YwyJUcFs2KkRPBERERERT/to2npe+mMVl3Wrz3/Oa0dBoWXMvE28/Mcqbvl4Ds3iQ7ltQFOGtq9b7Jq5/d6bvI4VW/fwxpWdCQ44dnETYwyPn9+Ozg2iaFsvgrb1qs80zGOpkQkeuEIr6oUnIpVRWloaAwcOBGDLli34+vqyvwfojBkzCAgIONbujB8/noCAAHr16nVScezatYtPPvmEW2655aSOIzWb3s8iMnbxFh4Zs4gBLeN5rGjNnJ+v4YLOiZzbsR4/LNzMS7+v5M5P5/H02BUMaBlPj8bRdGsUc1g/ui0ZOTw3zt0/uHVCqR7bz9eHS7sleeqpVUo1NsFLjApm3oZd3g5DROQoMTExzJs3D4BHH32U0NBQ7r333lLvP378eEJDQ8vlA/Err7yiD8RyUvR+FqnZ5qSkc8enc2lXL4KXLu+In+/hXdp8fQzndKjL0HZ1GLtkCx9NS+HTmSm8N2UdAC0SwujeOJoejWMYM28j+YWWR89uU+3WzZUnj/XBq+zqR4Wwa28emTl53g5FROS4Zs+ezamnnkrnzp05/fTT2bx5MwAvvPACrVu3pn379lx66aWsW7eO1157jWeffZbk5GQmTpx42HH+/PNPkpOTSU5OpmPHjmRmZgLw1FNP0bVrV9q3b88//vEPAB544AFWr15NcnIy9913X8U+YanW9H4WqRnWbN/D9e/NJCE8iLev6UpIQMljSz4+hiFt6/DRDd1Z8I/TGX1zT+47vQXx4YF8MSuVWz6ewy+Lt3Jr/6bH7VVX09XYEbz9vfA27Mymdd3qu8hSRKo+ay233347Y8aMIS4ujs8++4y///3vvPPOOzzxxBOsXbuWwMBAdu3aRWRkJCNHjixxlOR///sfL7/8Mr1792bPnj0EBQUxduxYVq5cyYwZM7DWcs455zBhwgSeeOIJFi1adGD0RaQ86P0sUj28PWkto2en0rJOGJ2SouiUFEWL2mEH1s9ty8zh6ndn4GMM71/bjdjQwFIfO8DPh84NouncIJpb+zclr6CQhRszWL1tD8OS63nqKVUbNTbBSzykVULruuFejkZEKq2fHoAtC8v3mLXbwRlPlHrzffv2sWjRIgYPHgxAQUEBdeq4pq7t27dn+PDhnHvuuZx77rnHPVbv3r255557GD58OOeffz6JiYmMHTuWsWPH0rGja9q6Z88eVq5cSVJSzVqzUCPo/SwiJ6mw0PL4T0t5c+JaWtUJZ8KK7Xw1ZyMAoYF+dKgfQaekKP5Yvo0dmbmMGtGDhrG1Tuox/X19DiSRcnw1NsGrv7/ZuVoliEglZ62lTZs2TJ069aj7fvjhByZMmMC3337LY489xuLFi495rAceeICzzjqLH3/8kR49ejBu3DistTz44IPcdNNNh227bt268nwaIoDezyLesCUjh4zsPFrUDjup4+QVFPLXLxfw9dyNXNOrIY8MbY0xkLJzL3NS0pm9Pp0563fx8h+rMMbw5lWdSa4fWT5PQkqtxiZ4kSH+hAb6qVWCiBxbGUYmPCUwMJDt27czdepUevbsSV5eHitWrKBVq1Zs2LCB/v3706dPHz755BP27NlDWFgYu3fvLvZYq1evpl27drRr146pU6eybNkyTj/9dB5++GGGDx9OaGgoGzduxN/fn7CwsANrmqSa0Pu5gp+tiPftzsnjotensGlXDo+e3ZorezY8oePszc3n5o/m8OeK7dx3egtu6dfkQKGTBjG1aBBTi/M6JgKQtS+fffmFh1XAlIpTY4us7O+Fl6pWCSJSyfn4+PDll19y//3306FDB5KTk5kyZQoFBQVcccUVtGvXjo4dO3L33XcTGRnJ2Wefzddff11sUYrnnnuOtm3b0qFDB4KDgznjjDM47bTTuPzyy+nZsyft2rXjwgsvJDMzk5iYGHr37k3btm1VlELKjd7PIhXHWsvD3yxi064cOjeI4uExi/n71wvJKygs03F2ZuVy2ZvTmbhyO0+c345b+zc9ZhXLWoF+Su68yFhrvR1DmXTp0sXOmjWrXI51w/uzSE3fy8939S2X44lI9bB06VJatWrl7TBqnOJed2PMbGttFy+FVOUUd47U+9k79LpLZTB6dip/+WI+9wxuzq39m/LUL8t57c/V9GgczavDOxNViiQsNX0vV70zg43p2bx4WUdOa1O7AiKX4znW+bHGjuAB1I8OZsPOvVS1JFdERERE5FjW7sji4TGL6NbIVaL09TE8cEZLnr2kA3NSdjHs5cms2FrytOXMnDx+XrSZC1+dyvbMfXx4fXcld1VEjV2DB67QSlZuAel78zSMLCIiIiLVQm5+IXeMmou/rw/PXZJ8oHUBwHkdE2kYU4sRH87m/Fem8PylyQxslUBBoWXRxgwmrNjOxJU7mJOSTn6hpU5EEF+M7EnL2qo6X1XU6ATv0FYJSvBEREREpDr439jlLNyYwWtXdKZuZPBR93dMiuLb23pz4wezuOGDWfRtFsf81F3s2psHQNt64Yzo25i+zePolBRFgF+NnvRX5dToBO/QZuftEyO9G4yIVCrW2mMuIJfypanynqX3c8XS+1m8acKK7bwxYQ3DuycxpG3JUyrrRATzxU29eOibRUxbk8bAlgn0bR5Ln6axxJShKblUPjU6wds/grdBlTRF5BBBQUGkpaURExOjD8UVwFpLWloaQUFB3g6lWtL7uWLp/SzetGPPPu75fD7N4kN56KzWx90+OMCXpy/uUAGRSUWq0QleWJA/kSH+6oUnIodJTEwkNTWV7du3ezuUGiMoKIjExERvh1Et6f1c8fR+Fm8oLLT85fP57M7J46MbuhEc4OvtkMRLanSCB67QSmp6trfDEJFKxN/fn0aNGnk7DJFyofezSPVWUGiZt2EXo+ek8ueK7fxzWBsVRKnhlOBFB7NsS8klYkVEREREKpPNGdlMWLGdCSt2MGnVDjKy8zAGLuqcyJU9Gng7PPEyJXhRIYxbuo3CQouPj9YmiIiIiEjlY63l7Ulr+XzWBlZs3QNAQnggp7VOoG/zOPo0jS1V43Kp/mp8gpcYFUxufiE79uwjPlwLokVERESkcsnOLeDeL+fzw4LNdG0Yxd/ObEnf5nG0SAhT8SQ5ihK8/a0S0vcqwRMRERGRSmXTrmxGfDiLxZt288AZLbmpb2MldXJMNT7Bqx91sBdeZ01ZFhEREZFKYvb6dG76cDY5eQW8fXUXBrRM8HZIUgXU+ATvQC88tUoQEREvMcbcDdwAWGAhcC0QAnwGNATWARdba9O9FKKIlEF+QSGfzEhhe+Y+woP8CQvyIzy46HvR73UjgwnyL7mVwZezU/nbVwupExnEqBu70ywhrAKfgVRlNT7BC/L3JS4sUK0SRETEK4wx9YA7gNbW2mxjzOfApUBr4Ddr7RPGmAeAB4D7vRiqiJTCxl3Z3DlqLrPWp2MMWFv8dj4GGseF0qpOOC1rh9G6Tjit6oQTFxbIkz8v440Ja+jVJIaXL++k4ilSJjU+wQOoHxXMhnSN4ImIiNf4AcHGmDzcyN0m4EGgX9H97wPjUYInUqn9sngLf/1yAQWFlucvTebs9nXJys0nMyef3Tl57M7OJzMnj4zsPNbtyGLJ5kzmrE/nu/mbDhwj2N+X7LwCrurZgIeHtsbf18eLz0iqIo8leMaYd4ChwDZrbdsStukHPAf4Azustad6Kp5jSYwKYe4GzXoREZGKZ63daIz5H5ACZANjrbVjjTEJ1trNRdtsNsbEezVQESnRvvwCHv9xGe9NWUe7ehG8eFlHGsbWAiAsyJ+wIH/qElzi/rtz8li2OZNlW3azfEsmnRtEcX6nxIoKX6oZT47gvQe8BHxQ3J3GmEjgFWCItTbFmyeu+tHB/LhwM/kFhfjpKomIiFQgY0wUMAxoBOwCvjDGXFGG/UcAIwCSkpI8EaKIHMOa7Xu4fdRcFm/azXW9G3H/GS0I9Ct5bV1xwoP86dYomm6Noj0UpdQkHstmrLUTgJ3H2ORy4CtrbUrR9ts8Fcvx1I8KIb/QsmV3jrdCEBGRmmsQsNZau91amwd8BfQCthpj6gAUfS/2PGmtfcNa28Va2yUuLq7CghYRGDNvI2e/OImNu7J566ouPHJ26zIndyLlzZvDVc2BKGPMeGPMbGPMVSVtaIwZYYyZZYyZtX379pN71J1r4fd/Q9aOAzfVjz7YKkFERKSCpQA9jDEhxjW3GggsBb4Fri7a5mpgjJfiE5FiTFy5nTs/nUfruuH8eMcpDGqtFgZSOXgzwfMDOgNnAacDDxtjmhe3YblenczaDhP+CylTD9y0v1VCqgqtiIhIBbPWTge+BObgWiT4AG8ATwCDjTErgcFFv4tIOcrYm8czY5fT/T/j+GpOaqn3S9uzj3s+n0+z+FA+uK47dSNLXl8nUtG8WUUzFVdYJQvIMsZMADoAKzz6qHU6gG8gpEyDVmcDUDcyGB8DG9QqQUREvMBa+w/gH0fcvA83mici5Sxjbx5vT1rDu5PXkbkvnzoRQdw/egH1o0Po2vDY6+Cstdz35QIysvP44LpuBAdoSqZULt4cwRsDnGKM8TPGhADdcVNSPMsvEOp1dgleEX9fH+pEBJOqZuciIiIi1db+Ebs+T/7OC7+v4pTmsfx81yn8fGdf6keFcNOHs0lJO/bnwQ+mruf3Zdt48IyWtKoTXkGRi5SeJ9skjML174k1xqTirkz6A1hrX7PWLjXG/AwsAAqBt6y1izwVz2GSusOUFyF3LwS49Xf11AtPREREpFoqLLS8+Psq3pq4hsx9+ZzZrjZ3DGxGy9oHE7S3r+nKuS9P5vr3Z/LVLb0IC/I/6jhLN+/m3z8uZUDLeK7p1bACn4FI6XmyiuZl1to61lp/a22itfbtosTutUO2ecpa29pa29Za+5ynYjlKUk8ozIeNsw/cVD8qhFRN0RQRERGpdsYt3cqz41bQo0kMP991Cq8M73xYcgfQKLYWrw7vxNodWdw+ai75BYWH3Z+dW8Ado+YSEezPUxe2x9VEEql8ambTt/rd3PcNB6dp1o8OZsvuHPblF3gpKBERERHxhK/nbiQ2NIBXh3c6KrE7VK+msfzfsDaMX76d//y47LD7/v3jElZu28PTF3UgJjTQ0yGLnLCameAFR0Fcq8PW4dWPCsFa2LRLvfBEREREqouMvXn8tnQbZ3eoi5/v8T/6Du/egGt7N+SdyWv5ZHoKAL8s3sJH01IY0bcxfZur36RUbjUzwQNI6gEbZkChG7FTqwQRERGRym3ehl1c8dZ0tmSU/oL8j4s2k1tQyPkdE0u9z9/PbEW/FnE8MmYRX81J5f7RC2hbL5x7T2txImGLVKianeDt2w3bXOFONTsXERERqbz27Mvn9lFzmLRqB+9MXlvq/b6es5EmcbVoW6/0FS/9fH144bKONIqtxT2fzyc3v5AXLu1IgF/N/egsVUfNfZcm9XDfixqeJ4QH4e9rVElTREREpBJ69NvFbEzPpm29cEbNSCFrX/5x99mwcy8z1u3k/E6JZS6KEh7kzzvXdKVN3XAeP78djeNCTzR0kQpVcxO8yAYQVgc2TAfA18dQNzKYDeqFJyIiIlKp/LhwM1/OTuW2/k3557C2ZObk88WsDcfdb8y8jQCc06HuCT1u/egQfrjjFIYl1zuh/UW8oeYmeMZA/e5HFVpRqwQRERGRymNLRg5/+3ohHRIjuH1gMzolRdEpKZJ3Jq+joNCWuJ+1lq/mbqRbo+gDS3FEaoKam+CB64eXsQEyUgHXKkFFVkREREQqh8JCy71fzGdfXiHPXpKMf1EVzOv7NCZl517GLd1a4r4LN2awZnsW53fU6JvULDU8wdu/Ds+N4iVGhbBjTy57c48/p1tEREREPOvdKeuYtGoHj5zd+rA1cKe3SaBeZDBvTyy52MpXczYS4OfDGe3qVESoIpVGzU7wEtqCf61DEjzXKmGjpmmKiIiIeNWyLbt58udlDGqVwKVd6x92n5+vD9f2bsiMdTtZmJpx1L55BYV8N38Tg1rFExHsX1Ehi1QKNTvB8/WD+l0PJHhJRfOz1+zI8mZUIiIiIjVaTl4Bd306j/Agf568oF2xFTAv7lqfWgG+vD1pzVH3TVq5g7SsXM4rQ+87keqiZid4APV7wLbFkJNBqzrhBPj5MGvdTm9HJSIiIlJj/e+X5SzbkslTF7YnJjSw2G3Cg/y5pGsS3y/YfFTj86/mbiQqxJ9Tm8dVRLgilYoSvKQeYAshdSZB/r4k149k+loleCIiIiLeMGPtTt6atJarejagf8v4Y257be+GFFrL+1PXHbgtMyePsYu3MLR9XTUmlxpJ7/rELmB8IcX1w+vRKJpFGzPIzMnzcmAiIiIiNc/TY5eTEB7Ig2e0Ou629aNDOK11bT6ZnnKgSN7Pi7awL7+Q8zqpeqbUTErwAsOgdltImQpA98YxFFqYtT7dy4GJiIiIVF3fL9jENe/OIDu3oNT7TF+TxvS1O7mpbxOCA3xLtc8NpzQiIzuP0bNd26uv526kYUwIHetHnkjYIlWeEjxw/fBSZ0FBHp2SovD3NUxfo2maIiIiIiciNX0v93+5gPHLt/PmxKOLoJTkxd9XERsayGXdkkq9T+cGUXRIjOCdyevYuCubqWvSOLdjvWILs4jUBErwAOp3h/xs2LKA4ABfOiRGMm1NmrejEhEREalyrLXcP3oBAL2axPDq+NVHFUEpzuz1O5m0agc39W1c6tE7AGMM1/VpxNodWdz7+XyshXOTNT1Tai4leHBUw/PujaNZuDGDrH1qeC4iIiJSFh9PT2HyqjT+flZrnrygPQWFlv/+suy4+73w2yqiawUwvEfpR+/2O7NdHepEBDF1TRqdkiJpGFvrREIXqRaU4AGE14XIBgcTvEYxFBRaZmsdnoiIiEipbdi5l//8uJRTmsVyWbf61I8O4fpTGvHVnI0sSN1V4n7zN+zizxXbueGURoQE+JX5cf19fbi6V0MAzuuk3ndSsynB2y+ph0vwrKVzgyh8fQzT12qapoiIiEhpFBZa/vrlAnyM4YkL2h9YA3dLvybEhgbwz++WYK0tdt8Xf19JZIg/V/VseMKPf1XPBjxwRksuVIInNZwSvP2SekDWNti5hlqBfrSrF6FCKyIiIiKl9PH09Uxdk8ZDZ7WiXmTwgdvDgvy597QWzFqfzo8Ltxy136KNGYxbuo3rejciNLDso3f7hQT4MfLU0lffFKmulODtV79oHd6Gon54jWOYn7qrTKV9RURERGqilLS9/OfHZfRtHsclXesfdf9FXerTqk44j/+0lJy8wz9bvfT7KsKC/A5MsRSRk6MEb7+4lhAUcUg/vGjyCixzUrQOT0RERKQkhYWW+76cj5+P4ckL2hXbnsDXx/Dw0FakpmfzzuS1B25ftmU3Py/ewrW9GhIR7F+RYYtUW0rw9vPxcaN4RYVWujSIwse4hpsiIiIiUrwPpq5j+tqdPHx2a+pEBJe4Xa8msQxuncDLv69iW6Zrm/DS76uoFeDLdX0aVVS4ItWeErxDJXWHHSsgK42wIH/a1otg2lqtwxMREREpztodWTz583L6t4jjos7HL27ytzNbkVtQyNO/rGDVtkx+WLiZq3s1JDIkoAKiFakZlOAdKqmX+75uAgDdG0UzL2XXUXPFRURERGq6qavTuOi1Kfj7Gh4/v32xUzOP1Ci2Ftf0asjnszdw/+iFBPn5cr1G70TKlRK8QyV2hVrxsOgrwBVayS0oZG7KLu/GJSIiIlIK63ZkkZq+16OPUVhoeWX8Koa/NY2IYH9G39yL2hFBpd7/tgHNiAoJYPb6dK7s2YCY0EAPRitS83gswTPGvGOM2WaMWXSc7boaYwqMMRd6KpZS8/WDtufDil8gJ4MuDaMxBvXDExERkUovJ6+AS9+YxjXvzqSwsPh+cycrY28eIz6czX9/Xs6Z7eow5rY+NEsIK9MxIoL9efCMlsSGBnLDKRq9EylvnhzBew8YcqwNjDG+wJPALx6Mo2zaXQQF+2Dp90QE+9O6Trj64YmIiEil99G09WzZncOqbXv4c8X2cj/+oo0ZDH1pIuOXb+PRs1vz4mUdT7hv3UVd6jProUHEh5V+5E9ESsdjCZ61dgJwvMzodmA0sM1TcZRZvc4Q1RAWfgFA90YxzElJZ1++1uGJiIhI5ZSZk8fLf6yid9MYaocH8ebENeV6/M9mpnD+q1PIy7d8dlNPrundqFRr7kSk4p3YZZdyYIypB5wHDAC6eiuOoxjjRvEmPg2ZW+neOJp3Jq9l/oYMujWK9nZ0IiIiIkd5Z9I60vfm8cCQVkxevYMnflrG4k0ZtKkbUar935q4hncnr8NaS6EFS9F3C4XWsjMrlz5NY3n+0mStmROp5LyW4AHPAfdbawuOdwXIGDMCGAGQlJTk+cjaXQQTnoLFX9Ot3XWA64enBE9EREQqm/SsXN6cuIYz2tamXWIESTEhvPjbSt6auJZnL0k+7v6rtu3hiZ+W0aZeBM3iQ/ExYDD4+IAxBgM0iQvl6l4N8fXRqJ1IZefNBK8L8GlRchcLnGmMybfWfnPkhtbaN4A3ALp06eKZVcOHimsBtdvBwi+I6jGSlrXDmL52J7d7/IFFREREyubVP1ezNzefewY3B1wRk4u71ufDqev565AWx2w+DvCvH5YQ7O/L21d3IVajcyJVntfaJFhrG1lrG1prGwJfArcUl9x5TbuLYOMs2LmGHo1jmL0+nbyCQm9HJSIiInLAlowc3p+yjvM6Jh5WzfK63o0otJb3pqw75v5/LNvG+OXbuXNQMyV3ItWEJ9skjAKmAi2MManGmOuNMSONMSM99Zjlqs357vvC0XRvFE12XgELUjO8G5OISEWa+zG80R/2qpKwSGX14u8rKbSWuwY1O+z2+tEhnNG2Dp9MT2HPvvxi983NL+Sx75fQOLYWV/VsWAHRikhF8GQVzcustXWstf7W2kRr7dvW2testa8Vs+011tovPRXLCYmsD0m9YOHndGsYBcC0NeqHJyI1xL498OvDsGkOfHu7q7QgIpXK+rQsPpu5gcu6JVE/OuSo+284pRGZOfl8PnNDsft/MHUda3Zk8dDQVgT4eW1Sl4iUM/1vPpZ2F8KOFcTsWUGz+FCmr9VVbBGpIWa8AXvToP2lsOx7mPWOtyMSkSM8N24lfr6G2/o3Lfb+jklRdGkQxTuT15J/xDKTtD37eP63lZzaPI7+LeIrIlwRqSBK8I6l9bng4wcLv3Dr8NbtPOoPpIhItZOzG6a8AM1Og3NfhSYD4Ze/wdYl3o5MRIos35LJN/M2ck2vRsSHl9ws/IZTGpOans0vi7cedvvTv65gb24BDw9tpX52ItWMErxjqRXjPtgsGk33RpFk5RawcKPW4YlINTf9dchOh34Pgo8PnPcaBIbB6OshL/v4+2duhfFPaFqniAc9PXY5oQF+jDy18TG3G9w6gQYxIbw5cQ226P/k4k0ZjJqRwlU9G9A0PuyY+4tI1aME73jaXQS7N3JKwCp8fQw/L97i7YhERDwnexdMfRFanAn1OrnbQuPh3Ndg2xIY+/Cx91/9B7zWByY9B9uWejpakRpp3oZdjF2ylRF9GxMZEnDMbX19DNf3acS8DbuYvT4day3//G4JkcH+3DWweQVFLCIVyZt98KqGFmeAfwgRq75hQMsrGD07lXtPa4G/r3JjEamGpr8GORnQ74HDb282CHrcCtNehiYDoOWZh99fkA9/PgET/ud6iV79LcS3qri4RaqZnVm5rN2xh62797F1dw5bd+9jW2YO23bvY9mWTGJqBXBtn0alOtaFnRN5euwK3py4hu2Z9Zi+diePnduWiBB/Dz8LEfEGJXjHExjqrmQv+YbLzvoLvy7Zym9LtzGkbW1vRyYiUr6y02Hqy9ByKNTpcPT9g/4B6ybCmFuh7mQIr+tu370JRt8A6ydD8hVw5n8hoFbFxi5SDWzOyObnRVv4adEWZq7bedgsZ39fQ3xYEPHhgXRuEMlVPRsSGli6j3EhAX5c0SOJV8avZm7KLlrWDuOyrvU99CxExNuU4JVGu4tg0Zec6ruQhPBAPpuZogRPRKqfqS/Dvt1u7V1x/ALhwnfg9b7w9U1w5Tew6jf3c/4+OO8N6HBJhYYsUtVt2LmXnxdt4cdFm5mbsguAFglh3DGgGclJkdQODyIhPIjIYH98fE68GMrVPRvy5oS1bMvcx3OXJOOnmUgi1ZYSvNJoMgCCo/BdPJqLOv+VV8avYtOubOpGBns7MhGR8rF3J0x71VUPrt225O1im8EZ/4Vvb4P3hkLKFEhoCxe95+4TkRLlFRSyfEsmC1IzWJC6i3kbdrFsSyYAbeqGc9/pLRjStjZN4kLL/bHjw4O4pX8Tdu3No1fT2HI/vohUHkrwSsMvwH3oWfAZl1z3BC/9AV/OTuWOgfowIyLVxJQXITfr6LV3xel4Baz+DRZ/DV2ug9P/A/664HWijDEtgM8Ouakx8AjwQdHtDYF1wMXW2vSKjk9OzqSVO/h1yRbmp2awZPNucvNdu6WIYH/aJ0ZwXsd6nNG2DkkxRzcqL293DVJRFZGaQAleabW7CGa/S/1NP9K7aQs+m7mB2/o3PanpEiIilULWDtcaoe35pSuMYgyc9zqc8heo3c7z8VVz1trlQDKAMcYX2Ah8DTwA/GatfcIY80DR7/d7K04pu9Xb93DF29MJCfClbd0IrurRgPb1I+mQGEFSdIj6z4mIRyjBK60GvaBuR5jwFJed+j23fb6Yyat3cEqzOG9HJiJyciY/D/nZcGopRu/28wtUcucZA4HV1tr1xphhQL+i298HxqMEr0p5d/JaAnx9+PO+/sSFBXo7HBGpIbTCtrSMgYGPQMYGTs/+gcgQfz6dscHbUYmIHN++TNgwEzbNc73p0lbDrg2wZxvsXAMz3nSzFOI0fasSuBQYVfRzgrV2M0DR93ivRSVltmtvLqNnb2RYcl0ldyJSoTSCVxaN+0PDU/Cf/AyXtB/FOzO3kLZnHzGh+sMtIuUgZTr8+gjENIFzXgKfcrgGt2QM/Hgf7Nla8jbGB/r+9eQfS06KMSYAOAcooYxpifuNAEYAJCUleSAyORGjZmwgO6+Aa3uXrlediEh5UYJXFsbAwH/A24O40f8XXi/oxNdzN3LDKY29HZmIVGW7N8Gv/4CFn0NQBGyYBmF1YODDJ37MzC3w472w9Duo3R7O/B/4+EJBLuTnQsE+19qgIA+iG0Ns0/J7PnKizgDmWGv3Z+NbjTF1rLWbjTF1gG3F7WStfQN4A6BLly62uG2kYuUVFPLB1HX0ahJD67rh3g5HRGoYJXhlVb8rtDiL2AVv0DfxDT6duYHr+zTSQmkRKbu8HJj6Ikx8BgoL4JR7oc/d8MvfYOL/XNuBDpeW7ZjWwtwPYexDLoEb9Cj0vB189ee+CriMg9MzAb4FrgaeKPo+xhtBSdn9tGgLmzNyeGzYMVqOiIh4iNbgnYgBD8G+TP4W/jOrtu1hToqqVotIGVjrRtZe7ga//8v12rx1uhuxCwyFs56GhqfAt7fD+qmlP+7ONfDBOW6/hLYwcrJLGJXcVXrGmBBgMPDVITc/AQw2xqwsuu8Jb8QmZffOpLU0jAlhQEstmxSRiqcE70QktIb2l9Ai5VMaBWSo2IqIlJ618OW18NkV4B8CV42BSz+G6EPW6fj6w8UfQER9+Gw47Fx77GPm74NJz8ErvWDjXBj6LFz9vaZdViHW2r3W2hhrbcYht6VZawdaa5sVfd/pzRildGavT2fehl1c27uRWimJiFcowTtR/R/EFBbwROxPfL9gM5k5ed6OSESqggWfuwbhp9wLIydB437FbxcSDZd/7qZujroUcjKO3sZaWP4zvNIDxv0DmvR3I4FdriufAi0iUmbvTF5LWJAfF3ZO9HYoIlJD6RPAiYpqCJ2voVv6DyTkp/Ld/M3ejkhEKrvsdLe+rl5n6P+340+djG0Kl3wIaavgi2uhIP/gfdtXwEcXwKhLwPjC8NFw2SiIqOfZ5yAiJdq4K5ufF23hsm5J1ArU1GgR8Q4leCej773gG8A/Qsfw2cwUb0cjIpXduP+D7J0w9DlX0bI0GvWFs56B1b/BLw+6kbyf/wav9oTUmXD6f+CWqdBskEdDF5Hj+2DKOgCu7tXQq3GISM2mBO9khNXG9BhJ/7wJ5G5cwJJNu70dkYhUVhtmwOx3ofvNUKd92fbtfDX0vA1mvAHPtoVpr0DycLh9DvS81a3ZE5GTUlBoGb98G7n5hSe0f9a+fD6ZkcKQNrWpFxlcztGJiJSeEryT1ftOCgMjuD/gC578eRnWqgWRiByhIA++uwvC60H/MvWwPmjwP6HdxVA3GUaMh3NegNC4cgxSpOay1vLwmEVc8+5MXv5j1QkdY/ScVDJz8rmujxqbi4h3KcE7WcFR+PS5k35mDtGrvuL7+Zu8HZGIVDbTXoVti+GMJyEw7MSO4eMLF7wJV3/nkjypdIwxQ40xOq9WQc//tpJPpqcQGxrAWxPXkLZnX5n2Lyy0vDt5HR3qR9IpKdIzQYqIlJJOROWh+0hs3Y48G/AqUWOuYPfmE7v6JyLV0K4NMP5xaH4GtBzq7WjEsy4FVhpj/muMaeXtYKR0Ppq2nufGreSizomMurEH2XkFvDJ+dZmO8cfybazdkcX1fRphjFojiIh3KcErDwG1MNePY3OPR+hYuJigN3rBxGfctCwRqdl+ut99P/O/oA9+1Zq19gqgI7AaeNcYM9UYM8IYc4LDtuJpPy3czMNjFjGwZTyPn9+OZglhXNApkQ+nrWfTruxSH+ftSWupExHEGW1rezBaEZHSUYJXXnz9qDPkL7zV4TN+z28Pv/0fvHYKpEzzdmQi4i3LfoDlP8Cp90NkkrejkQpgrd0NjAY+BeoA5wFzjDG3ezUwOcrU1Wnc+ek8OtaP5KXLO+Hn6z4S3TW4OVh4ftzKUh3nx4WbmbI6jat7NcTfVx+rRMT71KSlnN1w1ikMXv4gU3zn8n+572LeOR06XQWn/QuCIrwdnoiUJHcv7NkKWdvd9z3b3FfWdijYB4WFYPd/FRR9t65wSnwrSGgNca0gIMQdb98e+PGvEN/aVbqUas8YczZwHdAE+BDoZq3dZowJAZYCL3ozPjloyabdjPhgFkkxIbxzTVeCAw62LakXGczwHkm8P2UdI05tTJO40BKPs3ZHFn/9cgHJ9SO5rreKq4hI5aAEr5zVCvTjn8PacsMHOdQb9Ck32S9gykuQsxsuft/b4YnIofJyYNbbMPkF2LOlmA0MhESDXzAYHzfF0vi4gifGxyV4K36B/OyD20c1hIQ2kJcNu1PhwrFqY1BzXAQ8a62dcOiN1tq9xpjrvBSTHGHDzr1c/e4MQoP8+OC6bkSGBBy1za39m/LZzA08M3YFLw/vVOxxcvIKuOXjOfj5Gl4e3okAP43eiUjl4LEEzxjzDjAU2GatbVvM/cOBosUp7AFuttbO91Q8FWlQ6wSGtKnNM+NTGXL3/TQIDIPf/wUrxkLz07wdnogU5MP8T2D8ky4Ja9wPut8EoQlFX3Hue0gs+B7nz2RhAaSvg62LYdtSVy1z6xLYuRq63gBJ3SviGUnl8A9g8/5fjDHBQIK1dp219jfvhSX7pWflctU7M8jNL+STkT2pW0K/utjQQG7o04gXfl/FyNQM2iUePQPn/75bzNLNu3nnmi7qeycilYonR/DeA14CPijh/rXAqdbadGPMGcAbQLX5JPToOW2Y9MwOHvpmER9cfQdmwRfww1+g4TQIqOXt8ERqJmthyRh3wSVtJdTrDOe+Ao1PPfFj+vhCTBP31fqcg7cX5IGPJknUMF8AvQ75vaDotq7eCUcOVVho+csX89mYns2oEd1plnDs2jc39G3Mh9PW899flvHh9Yd/PPlqTiqjZmzgln5NGNAywZNhi4iUmcfmExRNUdl5jPunWGvTi36dBiR6KhZvqB0RxH2nt2Diyh18u2gHnP0cZKTA+Ce8HZpIzbRmPLzZH7642iVll3wMN/x2csndsfj6q2pmzeNnrc3d/0vRz0fP/xOveHPiGn5fto2Hhraic4Po424fHuTPLf2aMnHlDqauTjtw+4qtmfz960V0bxTNPYObezJkEZETUlkmjF8P/FTSnUVlpmcZY2Zt3769AsM6OVf0aECH+pE89v0SdsV1ccVWpr4MWxZ6OzSRmsNaN2L3wTDISoNzX4Wbp0CroUrApLxtN8YcGMY1xgwDdngxHikye/1O/vvLcs5sV5srezQo9X5X9mxA7fAg/vvLMqy1ZO3L55aP51Ar0JcXL+t4oPKmiEhl4vW/TMaY/rgE7/6StrHWvmGt7WKt7RIXF1dxwZ0kXx/D4+e1I31vHn//ZhF20P9BcBR8d5dbtyMinpW/D74aAROego5Xwu2zIPlyN4InUv5GAn8zxqQYYzbgzms3eTmmGi89K5fbPplLvchgnrigfZkakQf5+3LnoGbMTdnFuKXb+NvXC1mzfQ8vXNqR+PAgD0YtInLivJrgGWPaA28Bw6y1acfbvipqXTecv5zWnB8WbOaj+bthyOOwcRbMesfboYlUTdaWbrvsdPjoAlj4OQx4GM55EfwCPRub1GjW2tXW2h5Aa6C1tbaXtXaVt+Oqyfavu0vbk8vLl3ciPKjsFW0v6pxIo9ha3PP5PMbM28Tdg5rTq2msB6IVESkfXkvwjDFJwFfAldbaFd6KoyKM7NuE/i3ieOz7pSyMOs1V7Pvtn7B783H3FZEihQXu/81jcfDhebD855JHwtPXwdunwYbpcP5b0PdeTceUCmGMOQu4BbjbGPOIMeYRb8dUkx267q64Spil4efrw19Oa05mTj59m8dxa/+m5RyliEj58liCZ4wZBUwFWhhjUo0x1xtjRhpjRhZt8ggQA7xijJlnjJnlqVi8zcfH8MzFycSGBnDLqDlkDvqvmzr28wPeDk2kati7Ez6+ECY+DU0GuHYEoy6BFzu5PpPZ6Qe3TZ0Nbw1yTcqv/BraX+S9uKVGMca8BlwC3A4YXF+80i/4knJ1ouvuinNWuzq8fmVnXrq8Iz4+ulgkIpWbsaWY7mSMuRN4F8jETansCDxgrR3r2fCO1qVLFztrVtXMBeekpHPxa1MZ2Cqe15L+wPzxL7j8c2h+urdDE6m8Ns2Dz6+EzC1w5v+g89WuBcHS72DGG5AyFfxDoP3FUKcD/Pw318du+GiIU4W7qs4YM9ta28XbcZSGMWaBtbb9Id9Dga+stRXWALUqnyPLU3pWLme+MBF/Xx++v6PPCU3NFBGpzI51fiztCN511trdwGlAHHAtoHr/ZdQpKYoHzmjJL4u38p4ZBrEt4Id7ITfL26GJVE7zRsE7p7upmNf+7JI7cC0I2p4P1/0MN01wP8//FL6/GxJau/YHSu6k4uUUfd9rjKkL5AGNvBhPjVQe6+5ERKqy0nbh3T8f4UzgXWvtfFOWMlRywPV9GjFj7U7+/fMqep37GC1+vBhGXeaaLUdUq1aAIkfLyYDVf8CqX900y5hmULsd1G4LCe2gVozbLj8XfvkbzHwTGp4CF77rRuWKU6cDDHsZBj8Gq3+HFmdCQEjFPSeRg74zxkQCTwFzAAu86dWIaqDnf1vJ78u28c9hbU543Z2ISFVW2ima7wL1cFciOwC+wHhrbWfPhne06jD9JCM7j6EvTqSgwPJrv3XU+v0h8PFzFTaTh6sYhFQf1rq+j6t+hZXjXNETWwBBES6h27kaMg8pNhRW1yV7Wdth01zodTsMfBR8S3stSqqbqjJF0xjjA/Sw1k4p+j0QCLLWZlRkHNXhHHkyvp6byt2fzefCzok8dWHZWiKIiFQlxzo/lvZT0/VAMrDGWrvXGBONm6YpJyAi2J+XL+/Eha9O5bZl7Xh75BR8vr0VxtwKS8bA2c9DeF1vhymVVc5uN0XRP7j8jrn6d5eI9bytbD3i5n7s1sHZomqWR14vytoGe7a6n2u3hz53QdPBkNj1YNKWtcM99tZFsGWR+743DS58B9pecLLPTKRCWGsLjTFPAz2Lft8H7PNuVDXLjLU7uf/LhfRsHMN/zmun5E5EaqzSJng9gXnW2ixjzBVAJ+B5z4VV/bVPjOShoa14ZMxiXpwbxZ1Xf+8+KI97FF7pAWf8F9pfotE8OVz+PnijnysqcuNv5dPXbdtS+HQ45O2F1Jlw/pvHTx6thQn/gz/+5aZYRtQ/5M5D3rMJraHRqdB0EIQlFH+sWrHQpL/7EqnaxhpjLsAVVillw0YpD2t3ZDHiw1kkRgfz2hWdCfDzaptf8TRr9flI5BhKm+C9CnQwxnQA/gq8DXwAnOqpwGqCK3s0YN6GXTw7bgV1I4O4qMdIaDYYvrkFvr7JjeYNfRbCans71KojKw0m/g9631k9X7fpr7tpjQB/PgkDT7LF1r5M+OxKCAh1r9n4J+CDc+GyURASXfw+hQXw0/1ufVz7S2HYS25EUUTuAWoB+caYHNzVDmutDfduWNVbelYu1703Ex9jePearkSE6O9RtTbzLfj933Dpx9CgV+n3y8t259CwOlA3GWKalm3GikgVUtoEL99aa40xw4DnrbVvG2Ou9mRgNYExhifOb8/2zH088NVC4sIC6deiCVz7I0x/zTV1frl70WjexZ6/WvXrI7BxjlsLWLudZx/LU6a9DNNegc0L4Kox3l+7teIXiGsJUeXQCisrzY2aNR0MoQkw6VlXUCTxBJcnWQvf3u4Sxqu+hUanuFi/GuGahF/xJUQ1PHyf/H3u/iXfuPVxg/4JPrpSLgJgrQ3zdgw1zb78Am76aDYb07P55MbuNIip5e2QxJNmvg0//AWML3xxLYycCKHxx9/PWvjuLljw6cHbAkLd0oG6yVAnGep1hlg1sZfqobSfzDKNMQ8CVwI/GGN8AV0iKwcBfj68ekVnWiSEccvHc1iYmuGuKPW8FUZOhrgW8PUIV2lz9+bjH/BErRkPk593PcVeP9Ule7l7Pfd4npCXA7Pfc9MF10+CP73cyWPdJPjkYnj3TNi96eSP9+cTkLsHTvsXDPmPK0jy9U0n/u80/TVY/LUbBWx0irutzbkuMc7aDm8NdoVO9svZ7ZqNL/nGVaw87V9K7kQOYYzpW9yXt+Oqrqy1PDh6ITPW7uSpi9rTpWEJsw7Ee/Zsh3WTy+dYs96FH+6BZqfDDeNcVebR17tZJccz7VWX3J16P9w8BYa9AsmXQ2G+O+7XI+ClzvDL310yKFLFlfbT2SW4xeLXWWu34CpqPuWxqGqY0EA/3ru2K1EhAVz73gxS0oo+sMc2hWt/gtMfdwnYK91h3ifl/8cnNwu+vQOim8Bdi9wfvcnPu7WAq34r38fypCXfuOIc57wIHa9wo13eij8/F76/B8LrQc4u+PgidzI6UdtXuCuXna+B+JauCuWwlyBtFfz+WNmPlzIdxj4ELc6C3ncdfl+DnnD9WPALgnfPgpW/QuZWeO8sWD8Fznsdet9x4s9FpPq675Cvh4HvgEe9GVB19uLvq/hq7kbuGdycYcn1vB2OHGnVOHi1J7x3Jiz97uSONft9+P4uaHYaXPIh1OsEZz0NayfA+MePve+a8e5813IonPoAJLSBjsPhzKfghl/hwVS4eSp0vhamvgQ/P1C2z1lbFkL6+pN5diLlrlQJXlFS9zEQYYwZCuRYaz/waGQ1THx4EO9f1438QsvV785gZ1auu8PHF3reAjdPhvjW8M3NblSoPEaE9vv937BrvUuMwuu4xOGaH9y6qo/Oh9E3uqtwld2MN1xftcb94IynDk439OTIZ0mmvgg7lrs1lJd8CNuXubVu+bkndrxfH3GFVfo9ePC2Jv2h641uSuraiaU/1p7t8MU1bqTz3FeKn/ob18Kd+GKawCeXuMIuaavgsk+hw6Un9hxEqjlr7dmHfA0G2gJbvR1XdfTrkq088+sKzu9Uj9sHaFpdpVKQ585ZH10AIbFuGuQ3t8DONSd2vDkfwnd3uGJdF394sLhYx+FFF3Ofcm14ipO+zp3vYpvDea8VP+vE188VBBv6rKskPf01Nw20sPDYceXuhe/uhNf6wPPt4aVubgRwzZ8nfq73FGvd2sU//lP5YhOPKG0fvItxI3bjcYvGTwHus9Z+6dHoilHde/zMWreT4W9Np1WdcEbd2IPggEMWABcWuiTmt/9zffMGPepGdE5mkfCGmfD2YOhyHQx95vD78nJg0jMw8RkIDIUz/wftLjzxx/Kk1Nnw1gCX2HUf4W7bvtwlJnU7Vex6vPR18HIPaDrQLQIHmDcKvhkJ7S6G898o23rKNX/CB+e4f+8+dx9+X24WvNrbtSm4eQoEHmcJUGEBfHie60d3/a9Qp/2xt9+X6U6Om+bC5Z+f+Ho/kRNUVfrgFce4Ov0LrLUVtqi5up8jwRVVGfzsBOLDAvn61l4E+qlQxgH5+9xShfC60Orsin/89HXw5fWwcZYbERvyOOzZBq/3hcj67rxTlhY/cz+CMbe5C5qXjgL/oMPvz8uGtwa5i94jJ0JE4sH7crPcevKMDXDjH+6C5fFYC+P+4WYxdb4Gznq2+KRw6xL48jrYvtQlheH1YOVYWD8ZCnLd+r7G/VxSGtME/Gu55+0f7C7WBoSAX7BbdrFnK2Ruca/Tni0Hf45t5hLYk22blbPbteFa+q37vX53uOh9d0FfqrRjnR9Lm+DNBwZba7cV/R4HjLPWdijXSEuhJpy8fl60hZs/ns3AlvG8dkVn/HyP+OOyc42bUrluIiS0hdP/A41PoKBp/j73R3ffHrhlKgSVUOht2zJXjCN1piu80XRQ2R/L0766CZZ9D/csPfx57E+s+t4HAx7yfBzWwqhL3YjabTMOP9lMeAp+/5dL0gY9WrrjFRbAG6dCdgbcNvPokxtAyjR4Zwh0vtr1UDyW3x5zVUaHvexOHKV9TgW55dOSQaSMqlKCZ4x5kYPdIH1w/WPXWWtL+Z/t5NWEc+Rdn87l+wWb+fa2PrSuqwKlgPs7vewHNxUxfS34Brikpnbbioth8TfuswkWznkB2px38L4Vv7jZR52udveVxrxP3Mhf436usnNJiWHaalc7IL4lXPMj+AW41+OLa1xSM/yLsn1usdYtfZj4tDtPnv3CwQvp1sKst91IXWC4GxVsOvDgvvv2uM9mK8e65Q0ZG0r/uPv5BUGtOMhIBeMDLc5wF+Eb9y/7uveti93sofR17nNHeF33bxQYChe9V7YqpGVhLaz4GZZ+74rgRCRCZJKbORRZHwJUDKk8lEejc5/9yV2RNEq/fk/KaEjb2vzfOW14ZMxi/v71Ip644IiGrdGN4erv3JqzsY+40Z2WQ+G0x9x9pTXxaTd18PIvSk7uwP3RvOobdyXsy+thxHiIbnSCz84D9myHxV+5E8eRzyP5MldwZcL/IKnn4X+IPWHZD+6P2mn/Ojy5AzjlXsjY6KpfhteDbjce/3jzR7n5/Re8XXxyB5DUw1W0nPICtDwbmh1xIrPWXRRY8bNL7jpeWfrkDtxoo5I7kdI4NLPKB0ZZa8upwoSAm5r5zbxN3DWoWdVI7gry3Afq/Bw3khPdxJ2nY5pASEz5VMfeuhh+fhDW/umWJlz0Hvz4V/jqRpfklXTuKC+5e2Hs32HWO64S5YXvHF2Bufnp7uLmpGddUnGsqf4F+W67P/7tLl4fK7kD91oOewm+uNqNvg153O2/5BsY/M+yX5Q2BgY87GZK/fmku9A67GW3jv7b293F5CYDXXJ3ZAXPwFCXkLU4w517d6x0I3R52a7X7IGvbPe6BdRyLZ1C4yG0tusXGxjuYti51o3Gzv3QPWZUI+hyLSRfAbVijv885n3iagEERcA13x9M5hLauN637w11n1V63Fy+Vdq3r3DrGFf/5h47N8sVszlUcJRbUtP+Ytfz+VifQb0pbTVMedHVpqjfzdvRlElpR/CeAtoDo4puugQ37eR+D8ZWrJpwdXK///2ynJf+WMWNpzTib2e2OjzJ2y8vxy0KnvgMFOZB96LRquP9Z9myyI0Mtb3ATRksjZ1r3JTHiCRXhCMgpMzPySMm/M9dbbt1JsQ1P/r+3L3w5gDI2gYjJx0+3SF7F2xd5Noq7FjuTrhRDYu+GrltSzsFdt8eeLkbBEXCTX8W3xuuIB8+u8IlW5d8BK2Glny83Cx4odPBaS3H+gOcl+P+PXMy3HPcleJG9lKmuu9ZRddnEru6iwNlmSIj4kVVbASvFm6NekHR775AoLW2wkoSV+dz5K69bmpmbGggY27tXTWame+fuRGZ5C7w2UMqPgZGuIulfkEuAczfV/Q95+Dv+3u21Uku+t7BfWgG1zrnj3/D7HddUtD/726kx9fPjR59fCH0uNVVXfaEPdtdP9SZb7kCZ73ucFWZS+qLWpAPHwyDTXPgxt8hvtXR22xfDl+PdNu0vdDVBijtZ42f7nfr57qPdP3u2l4AF7x1csnLn/91r3HzM2DLAjd1ctA/3OtaUVWk8/e5IjUz34aUKW50tnE/SOwGiZ3dMpTgyIPb5+XAT3+FOe9Dw1PcBeKwhMOPmZPhRkeXfe9ep3NeLH5ULS/bJan5Oe69d6yLvTkZ7vWa/trBmgHdbnSjkJlb3GhmRqr7fJKxAVJnudfUv5Zb/tP1evcYxSksdAMSKVPc9NiYJu7/RJ32x1+aciL27nTPZeabLjkNq+OWwZTUH9hLTnqKZtFBLgB649bgTbDWfl1+IZZedT55Hclay6PfLub9qev5y+Dm3D6wWckbZ25xffPmfeyG9nvfCe0uKr7Zd0E+vD0Idm1w0/7K8oZd+aurCNnuorKvJfOEgny3uDm2mVtnV5L96/Fqt3NX8zbPdyNjuw6pfBUc5eaqH3oC9g1wJ+aoRu6KY5vzS/6jPvYhd6Xnul/cqFpJcvfC+2e7xPKM/7qqYMXNhf/jcdca4bqxkNT9mC8D4NbJvTkQsGCLFodHNnAjl0k93PfY5mptIFVKFUvwpgGDrLV7in4PBcZaaz00D+po1fkcec/n8/h23ibG3NabNnUjvB3O8W1f7gpwtBwKF73rilvsSnG9R9NWu+8717gPkH5B7sOzX9DBL98Ad47aPP/wqX7Rjd3yjLV/uguLXa93H6aPPJf/cK/7gHrVGJcQlOfzmvoSzP8MCvZB8yGuGnODnsffN3MLvHaKS0hu/MONeIH7AD/tFfc5JqCWqwlw6BTP0sjPhXfPcOv/ardz587yuBA98WkXV3RjlyzV63TyxzxR25a6Ub3Vf7iL0vvFNncXcOt2dCN+m+dDn3tc0l9S/YHCQpj8rLsAEdfSjXZmbnb/vjtWuO+7Ujgw69wvyD1Gwz7QoLf72T/IHWfex64+RNYO6HQlDHgEQuOO/3w2znFTXheOhvxsNwLc5TpodY5LLNdPLrpQPRWy090+AaFu7SIAxjWsP3ARpKOrFXCis47y98GMN2HCf10Ngk5XuZlRoy6Flme50XFvf+49RLkkeJVFdT55Faew0HLvF/P5au5GHj27Ndf0Ps7UyI1zXKKxfrK7atLoVDf83Wrowasck1+AXx+GC9+FtueXPag/n4I//gVDnoQeI8u+f2kVFh4/GVkyBj6/Ci79xP3nO5b5n7q+cRh39ad2e3cS2P89LMEljLtT3dSI9HVFX2vdCF/6WndSHfCwm25y6H/yrYvdSSv5cjdV5HiydrjpEduXut/jWroTcON+7g9n7h43etdiiPuDUlrzPoFN81xCWL8HRKh0uFRtVSzBm2etTT7ebZ5UXc+Rvy3dyvXvz+KOgc24Z3AxMzUqm8ICtzY6baWbXVKaD7vHkrXD/W3fPNd937IAYlu4pRnFjYSBu5j4xqkuCbx58vEv5ubnuvOfj98RX77ue+pMmPISrPrVfdjvcBn0uKX4mTPHsnaiW1rS5nw3wpa+zhUBWT8ZWpzp1pKXpnl5cTJS3chL33vdxdnykjrbVZfen5BWBtm73Ehn6myX1KbOgr073Ajvea+7aaKlsfp3t/wme6f73S/ITZ+Ma+7eY7HN3Kjs+qlufeGWhYAF30CXTOXucQll/e5wxpMuyTqR5zL/UzfN99DEFdy05gY93WejpJ5uhlXW9qL/D/Pc901zIbOourx/iBu5bDrILcspbXGdpd+66q/p69wU3NP+5aqrgpvyO+5R97pWokriJ5zgGWMyObhg/LC7AGutrfBJs9X15HUs+QWF3PLxHMYu2crTF3Xggs6Jx99p+wpY+Dks+MxdgfELhpZnujf893e775d8dGJXIgoL4bPhbhHxVd9Cw95lP0ZJrHUNwic96yo9nvsKtB5W8vbvDXX9Z+6cV7qplGmrITSh7H+kCwth0Wg3VSN9rZsasb9BeGEhvDvEXW26fXbpR0QLC2HrQlclc81412MuPxuMrxuFzd7pRliPXMsgUoNUsQRvMnC7tXZO0e+dgZestaUY2igf1fEcmbE3j9Oe+5OokAC+va1P1ZiaOf11N03O2x8IN82DtwYWjSK+V/I5f/UfruT/obNailMrzrXn6Xo91Io98bj2L61od7Fbu+7j65KDDpdVqhGSKsVa9+8XFOFmJJVF5laXuMU0cYnxsT5PZae7pR/rJrmvfZnQ7wE3s+tk/+2sdZ+F1vzh1gom9Sx+Jlpx9mxzSe7q310PxvS17vaohu4zb6NT3fPKTndTMLPT3ees7HRIW+M+j8W1gtP/dfS6zcIC93lzy0J3sSSqwck9z3KiEbxqICevgOvfn8nU1Wm8MrwzQ9qW8g1vLWyY4RK9xV+5N3JgBNw6/eRK5OZkuHVtORlw04STL+NbWAjLf3CJ3cbZ7iQSmuCmMQ76Pzfl9Mg/HFuXuCaqg/4P+tx1co9fWgV5rmzzn/91V4sa93dXqyY9U7bKlMXJ3+f+rdaMd1fJWg5VQ3Gp8apYgtcV+BTY36i0DnCJtXZ2RcVQHc+R934xn6/nbmTMrb1pW68KTM1MXw+v9HSjDsO/9H7CMvEZN32uuGQzO93N+pn7kZuC2OsON1pTkOemjhYWFH0vWofUelj5FG0pLIRRl7gLxY37ufPnkYXJRE5G2uqDyd7aCa64zaF8/FwiHBzt6i+0v9gVoStpSmv6ejflOqGN6xV9Mi3KyokSvGoia18+V7w9ncUbd/P2NV04pVkZp3zk57qrIrVi3Tznk7VtmbsyGN/Kvdn9At3i3m2Li4bO57uvrO1ujnR8KzcVMa6lq8wZHOWSmgWfu54zaSvdlZZed7ipjuAWAS/+ys2DPuuZwxdvf3eXqzJ5z9KKX/ial+0WPE982l0BSurpyjNrfZtIuapKCR6AMcYfaIGb6bLMWptXkY9f3c6RfyzbxrXvzeT2AU35y2ktvB3O8VkLH53vLtbdMrV8pwmeqJJGH5Z+5xp6Z+1wlZj7PVCxBbj27YHUGe5CqbeTYKne8ve5pTa+/kVJXZRbtlTW9938z+DrEW4G1yl/8UysZaAErxrJ2JvHJW9MZX3aXt6/rhvdGnm5os/+NXCJXV3Ss23pwSIlwVGuIlJogpu+uH055GUd3De0tisGkrXNrYHrcze0Gnb41ZPCQrfeb+LT7irfRe+7xdnZu+CZVm4O/7kvV+ATPsK+TDc62nyIrj6KeEBVSvCMMbcCH1trdxX9HgVcZq19paJiqE7nyIzsPE5/dgLhwX58d3sfzzU0t9YtZdi2xE3zanPeiZdtn/cJfHMznPm/0rXCqSiHjj5c+C78fL87f9duB+e85IpUiMixWesa3C/9Fm4Yd+z1hmmr3cWV6EYlV5Y9SUrwqpltmTlc+vo0NqTv5YEzWnFd74bFt1CoKH887srxJrR1CV2dDu5kEVH/8KsjhYWuEtj2ZS4R3L7cLc7tfA00GXDsKylzP3LrA2KawuWfwbIf4ZcHYcSfOjGJVGNVLMErrsjKXGvtCVQdODHV6Rz5f98t5v0p6/jm1t60T4wsn4PuX7awZYFbArB1iTsf5WYe3KbRqW5qpV9A2Y6dudW1yolrCdf+VPlmdOwfffANAAz0u//glEwRKZ3sdHill6v2etOEwyu17tkOi750s8s2z3e3+fi76c/7i9bEFRWuiW1+0g3fleBVQxl787j3y/n8umQrp7VO4KkLOxARUs3/SK+d4HrI+Qa4r/B6cMOv3o5KRDyoiiV4C4AOtujEWtQHb4G1tk1FxVBdzpHr07IY9MyfXNg5kcfPb18+B1070VXJ2zTH/R4U6Ua04lu7annxbdwo3vd3QfJwty6sLBdPP78Klv/spkHGHqOtkbdYCz/c46pEn/Hfsle/FBFnzXjX07HrDXDav2HFT64K6Mpf3Sy2uh2h/aWu4M2O5a7w4Y7l7v/e/lluLc6Cyz45qTCOdX4sYSWhVHYRIf68cWVn3p60lid+WsZZL07k5cs70aF+pLdD85xGfeH6cfDJRa6M7eB/ejsiEZFD/QJ8box5DVeBeiTwk3dDqpr++/Ny/H19uHtQOSQhW5e4Eucrf3EXBs950VXJC6tzdAKX1N31avvzCbcm/NS/lu4xln7npjwOeLhyJnfgnuvQZ70dhUjV17gf9LzN9YNc8Dns2w1hdd1a1g6Xlty6JH+f6325fXnZK52WkRK8KswYww2nNKZTgyhu/2QuF742hb+f2Yqre3l5yqYnxTWHG35zlbfK2gRVRMSz7gdGADfjiqzMxVXSlDKYk5LODws3c+fAZsSHn0TFxoyN8Md/YP4nEBDmKi53v+n4hUT6PeAuIv7xb4hsAB0uOfb2aye49kMJ7VzFZxGp/gY87EbkAmpB8mUH2zAci1+gS/5KSgDLkRK8aqBTUhQ/3NGHe7+Yz6PfLWHamp08eWF7IoKr6ZTNWrEHq2yKiFQS1tpCY8w0oDFwCRANjPZuVFWLtZb//LCUuLBARvRtfGIHycuGP5+Eaa+6Ql49bnEV70pbbdkYN8q3e6NrwB1e1/U8PVJ2Oox9GOZ+CFGN4II3tZ5NpKbwDzrpKZaeVMlWAMuJigwJ4M2ruvD3M1sxbulWznt5MuvTso6/o4iInBRjTHNjzCPGmKXAS8AGAGttf2vtS96Nrmr5ZfFWZq1P5+5BzakVeALXoLN2wPtnu56qrYfBbbPg9H+XvZWOXwBc8pFr/PzZcDelaj9rYfE38FI3VzWz912uJUIFXJUXESkNJXjViDGGG/s25pMbe7Bzby7nvTKF2et3ejssEZHqbhkwEDjbWtvHWvsiUODlmKqcvIJCnvx5GU3jQ7m4ywm0ndmxCt4a5Pq9XfIRnP/GwZ5vJyI4Ei7/HHwD4aMLXZXM3Zvg0+HwxdUQXgdG/AGD/69i+8eJiByHxxI8Y8w7xphtxphFJdxvjDEvGGNWGWMWGGM6eSqWmqZbo2i+vqU34UF+XPbmdH5YsNnbIYmIVGcXAFuAP4wxbxpjBuLW4EkZjJqRwtodWTx4Rkv8fMv48SRlGrw9yPUmveYHaHV2+QQV1cC15tm7A94fCi93h9W/w+DH4IbfXVsgEZFKxpMjeO8BQ45x/xlAs6KvEcCrHoylxmkUW4uvbulN+3oR3PrJHF4dv5qq1hJDRKQqsNZ+ba29BGgJjAfuBhKMMa8aY04rzTGMMZHGmC+NMcuMMUuNMT2NMdHGmF+NMSuLvnu27JoXZebk8fy4lfRoHM2AlvFl23nx1/D+ORAS41rnJJZzV416neDCd1zj4rod4ZYp0PsO8FUZAxGpnDyW4FlrJwDHmh84DPjAOtOASGOMqo2Vo+haAXx0Q3fO7lCXJ39ext++XkheQaG3wxIRqZastVnW2o+ttUOBRGAe8EApd38e+Nla2xLoACwt2vc3a20z4LcyHKvKee3P1aRl5fL3M1uXvgq0tTD5efjiGpeEXf+rayjsCS3OgPtWwVVjPPcYIiLlxJtr8OpRtBC9SGrRbVKOgvx9ef6SZG7t34RRMzZw3XszyczJ83ZYIiLVmrV2p7X2dWvtgONta4wJB/oCbxftm2ut3YW7EPp+0WbvA+d6Jlrv2pyRzVsT13Jucl3aJUaUbqeCfPjxXte4vM15cOU3ZS+kUlYh0WVrfC4i4iXeTPCK+ytZ7BxCY8wIY8wsY8ys7du3ezis6sfHx3Df6S357wXtmbo6jXNemsz0NWneDktERJzGwHbgXWPMXGPMW8aYWkCCtXYzQNH3YucuVvVz5NNjV2CBe09vUbod0lbDu2fAzLdc37kL3nEly0VEBPBugpcK1D/k90RgU3EbWmvfsNZ2sdZ2iYuLq5DgqqOLu9bnoxu6k19YyCVvTOOhbxZqNE9ExPv8gE7Aq9bajkAWZZiOWZXPkYs3ZTB6TirX9mpIYlTIsTe2Fma/B6+dAjuWwwVvw+B/go8KgouIHMqbfxW/Ba4qqqbZA8jYf6VSPKdH4xh+uasvN/RpxCfTUzjt2Qn8vmyrt8MSEanJUoFUa+30ot+/xCV8W/evTS/6vs1L8XnE1t05jPhgNjG1Arilf9Njb7xnG4y6FL670xVRuXkqtLuwYgIVEaliPNkmYRQwFWhhjEk1xlxvjBlpjBlZtMmPwBpgFfAmcIunYpHDhQT48dDQ1oy+uRdhQX5c994s7vx0Lml79nk7NBGRGsdauwXYYIzZP0dxILAEdyH06qLbrgbGeCE8j8jYm8dVb89g195c3r2mGxHB/iVvvPR7eKUHrBkPQ5506+0itGRfRKQkHqvxa6297Dj3W+BWTz2+HF/HpCi+v/0UXhm/ipf/WMXElTt4ZGhrhiXXLX0VMxERKQ+3Ax8bYwJwFz+vxV2E/dwYcz2QAlzkxfjKTXZuAde/P5O1O7J479quJRdWyc2Cn/4Kcz+C2u3h/DchvmXFBisiUgWpiUsNF+Dnw12DmnNmuzr89csF3PXZPD6ZkcI/h7WhZe1wb4cnIlIjWGvnAcU1cBtYwaF4VF5BIbd+MofZKem8fHknejWNLXnjcf8Hcz+GU/4Cpz4AfgEVF6iISBWmlckCQPOEMEbf3IvHz2/Hyq2ZnPXCJP7vu8VkZKsIi4iInLzCQsv9Xy7g92XbeGxYW85sd4zWt2mrYdbb0PkaGPiIkjsRkTJQgicH+PoYLuuWxB/39uOybvV5b8o6Bj49ni9mbaCwsNgOFiIiIsdlreU/Py7lq7kbuWdwc67o0eDYO4x7FHwDod+DFRKfiEh1ogRPjhIZEsC/zm3Hd7f1ISk6hPu+XMAFr01h0cYMb4cmIiJV0OsT1vDWpLVc3bMBtw84TsXMlOmw9FvX4y4soWICFBGpRpTgSYna1ovgy5G9ePqiDmzYmc25L0/m5T9WUaDRPBERKaWfFm7miZ+WcXaHuvzj7DbHLuJlLYx9CEJrQ6/bKi5IEZFqRAmeHJOPj+GCzon89pdTGdK2Nk/9spzhb01jc0a2t0MTEZEq4M2Ja2gaH8rTF3XAx+c4FZqXfgupM6D/3yCgVsUEKCJSzSjBk1KJCPbnxcs68r+LOrAgNYMhz03k50XqSy8iIiVbn5bFnJRdXNApkQC/43zkyM91a+/iWkHHKyokPhGR6kgJnpSaMYYLOyfywx2n0CAmhJEfzeHBrxayNzff26GJiEgl9M3cTQAMS657/I1nvws718Dgf4KPr4cjExGpvpTgSZk1iq3FlyN7cXO/Jnw6M4WhL05SARYRETmMtZYx8zbSo3E0dSODj71xTgaMfwIa9YVmgysmQBGRakoJnpyQAD8f7h/Sko9v6M7efQWc/dIkbvl4thI9EREBYEFqBmt2ZHFucr3jbzzpWcjeCYMfg2MVYRERkeNSgicnpVeTWH6+6xRu6deEiSt2MPTFSVzz7gxmrdvp7dBERMSLvpm3kQBfH844VkNzgIxUmPYqtL8E6iZXSGwiItWZEjw5aZEhAdx3eksmPziA+05vwYLUDC58bSoXvz6VCSu2Y63aKoiI1CT5BYV8N38TA1vFExHsf+yNf/+Xa48w4KGKCU5EpJpTgiflJjzIn1v7N2Xy/QN4ZGhrUtL2ctU7Mzj35clK9EREapBJq3awY08uw443PXPTXJj/KfQYCZFJFROciEg1pwRPyl1wgC/X9WnEn3/txxPnt2PHnlyuemcGl785nTkp6d4OT0REPGzMvE2EB/nRv2VcyRttXQwfXwShCdDnnooLTkSkmlOCJx4T6OfLpd2S+P3eU3n07Nas3JbJ+a9M4cYPZrFia6a3wxMREQ/Ym5vPL4u3cFb7OgT6ldDuYNM8eO8s8PGHa76H4MiKDFFEpFpTgiceF+jnyzW9G/Hnff2597TmTFudxunPTeCez+axYedeb4cnIiLl6NclW9mbW1By9czUWfD+ORAQBtf+CLHNKjZAEZFqzs/bAUjNUSvQj9sGNGN49wa89udq3puyjm/nb+LsDnW58ZTGtK4b7u0QRUTkJH09dyP1IoPp2jD66DvXT3HTMmvFwdXfQWT9ig9QRKSa0wieVLioWgE8eGYr/ryvP1f1bMgvi7dw5gsTufLt6UxauUPFWEREqqgde/YxceUOzkmui4/PEf3s1oyHjy6A8Lpw7U9K7kREPEQJnnhN7YggHjm7NVMfGMhfh7Rg2ZZMrnh7Ome9MIlv5m4kr6DQ2yGKiEgZfD9/EwWFlvM6HjE9c+Wv8PHFENUIrvkBwo/TG09ERE6YEjzxuogQf27p15RJ9/fnvxe0J7egkLs+m0e/p8bz2cwU8pXoiYhUCV/P20TrOuE0Twg7eOPqP2DUZRDf0hVUCY33XoAiIjWAEjypNAL9fLm4a33G3tWXd67pQlxYIPePXsjgZyfw7fxNFBZq6qaISGW1dkcW8zfs4tyOdQ+/4/fHICIRrvoWQopZlyciIuVKCZ5UOj4+hgEtE/j6ll68eVUXAnx9uGPUXM58YSK/Ld2qNXoiIpXQN3M3Ygyc0+GQ6Zkb58DG2dB9pFohiIhUECV4UmkZYxjcOoGf7jyF5y9NJjuvgOvfn8X5r05hyioVYxERqSystXwzbyM9G8dQOyLo4B0z3wL/WpB8mfeCExGpYZTgSaXn42MYllyPcfecyn/Oa8fmXTlc/pYrxvLZzBSycwu8HaKISI02b8Mu1qft5dxDi6vs3QmLRkOHSyAownvBiYjUMErwpMrw9/Xh8u5JjL+vH/8+ry0FhZb7Ry+kx+O/8e8flpCSpqbpIiLe8N38zQT4+TCkbe2DN879EPJzoOuN3gtMRKQGUqNzqXKC/H0Z3r0Bl3dLYsbanXwwdT3vTF7HW5PW0r9FPFf2aECvpjEE+vl6O1QRkRphyuoddGsYTXiQv7uhsABmvg0NekNCa+8GJyJSwyjBkyrLGEP3xjF0bxzDlowcPpmRwifTU7j2vZkE+fvQtWE0vZvG0qtJDG3qRuB7ZNNdERE5aelZuSzbkslfBh/S227VONi1HgY96rW4RERqKo8meMaYIcDzgC/wlrX2iSPujwA+ApKKYvmftfZdT8Yk1VPtiCDuGdyc2/o35c8V25m8ageTV+3giZ+WARAR7E+PxtH0aRbHucl1Cdt/lVlERE7KjHU7AejeOOaQG9+E0NrQ6mwvRSUiUnN5LMEzxvgCLwODgVRgpjHmW2vtkkM2uxVYYq092xgTByw3xnxsrc31VFxSvQX4+TC4dQKDWycAsC0zh6mr04oSvjR+WbyVp8cu56a+Tbi6VwNCAjSILSJyMqav2Umgnw8d6hcVUtm5xo3gnXo/+OpimohIRfPkp9tuwCpr7RoAY8ynwDDg0ATPAmHGGAOEAjuBfA/GJDVMfFgQw5LrMSy5HtZaFm7M4NlfV/Dkz8t4e9Iabu7XlOHdkwjy13o9EZETMW1NGp2Sog6ue575Nvj4QudrvBqXiEhN5ckqmvWADYf8nlp026FeAloBm4CFwJ3W2kIPxiQ1mDGG9omRvHttN0bf3JPmCWE89v0S+j01no+mrSc3X289EZGyyNibx9Itu+neONrdkLvXVc9sORTC6xx7ZxER8QhPJnjFVbQ4sjP16cA8oC6QDLxkjAk/6kDGjDDGzDLGzNq+fXt5xyk1UOcG0XxyYw8+ubE79aKCeeibRQx4ejwfTltPTp766omIlMbMdTuxFro3Klp/t+hLyMmAbmqNICLiLZ5M8FKB+of8nogbqTvUtcBX1lkFrAVaHnkga+0b1tou1toucXFxHgtYap5eTWL5cmRP3ru2KzGhgTz8zSJ6PfE7z49byc4sLQUVETmW6WvTCPD1oWNSJFjriqvEtXLtEURExCs8meDNBJoZYxoZYwKAS4Fvj9gmBRgIYIxJAFoAazwYk8hRjDH0axHPN7f04tMRPUiuH8mz41bQ64nfeGTMIjVQFxEpwfS1O0muH+nWMafOhC0LoNsNYNSWRkTEWzxWZMVam2+MuQ34Bdcm4R1r7WJjzMii+18DHgPeM8YsxE3pvN9au8NTMYkcizGGHo1j6NE4hhVbM3ljwhpGzUjho2nrOaNdHS7uUp+ejWMI8PPkdRERkaohMyePRRszuLV/U3fDjDchIAzaX+LdwEREajiP1oi31v4I/HjEba8d8vMm4DRPxiByIponhPG/izpw72kteHfKWj6ZnsIPCzYTHuTHoFYJDGlbm77N41R9U0RqrFnr0yncv/5uz3ZY8o2rnBkY5u3QRERqNDUBEzmG2hFBPHhGK+4e1JxJK3fw06ItjFu6la/mbiQkwJf+LeMZ0qY2A1vFq6eeiNQo09fsxM/H0KlBJEx/HgpyoesN3g5LRKTG0ydSkVII8vdlUOsEBrVOIK+gkKmr0/h58RbGLt7CDws2UyvAl7Pa1+GCTol0axSN0foTEanmpq9No31iBCH+vjD3Y1dYJa6Ft8MSEanxlOCJlJG/rw99m8fRt3kcjw1ry4y1O/l6bio/LNjM57NSqR8dzAWdErmgUyL1o0O8Ha6ISLnbm5vPwtQMbuzbGDbMgJ2r4ZR7vB2WiIigBE/kpPj6GHo2iaFnkxgePacNvyzewpezU3n+t5U8N24l3RtFc1b7OvRvEa9kT0Sqjdnr08kvtHRvFA3zXgH/EGg9zNthiYgISvBEyk1IgB/ndUzkvI6JbNyVzTdzNzJ6TiqPjFkMLKZZfCgDWsbTv2U8nRtE4e+rapwiUjVNX7MTXx9Dl3rB8NXX0OocFVcREakklOCJeEC9yGBu7d+UW/s3Zc32Pfy+bBt/LN/GO5PX8vqENYQF+dG3eRxntq3DwFbxqsYpIlXK9LVptK0XQejaX2Dfbki+zNshiYhIESV4Ih7WOC6UxnGh3HBKYzJz8pi8age/L9vG78u288OCzUQE+3NOh7pc2DmR9okRKtAiIpVaTl4B8zdkcG3vhjD/GQhPhIZ9vR2WiIgUUYInUoHCgvwZ0rYOQ9rWoaDQMnnVDr6cncrnszbw4bT1NIsP5cLOiZzXsR7x4UHeDldE5ChzUtLJLSjklDp5MPN36HMP+GjKuYhIZaEET8RLfH3MgWqcu3Py+GHBZr6cncrjPy3jyZ+XMahVAiP6NqZLw2hvhyoicsC0NTvxMdAl41ewhdBB0zNFRCoTJXgilUB4kD+XdUvism5JrN6+hy9npzJqRgpjl2ylU1IkI/o2ZnDr2vj6aPqmiHjX9DVptK4TRtDizyGxG8Q29XZIIiJyCM2pEKlkmsSFcv+Qlkx5YAD/HNaGHXtyGfnRHAY8PZ4Pp64jO7fA2yGKSA2Vk1fA3A27ODdhO2xfBsmXezskERE5gkbwRCqpkAA/rurZkOHdG/DL4i28PmEND49ZzDO/ruC8jokMbBVP14bRBPjpOo2IVIz5G3aRm1/Iabm/gW8gtDnP2yGJiMgRlOCJVHK+PoYz29XhjLa1mbU+nTcnrOGjaet5Z/JaagX40rtpLANaxtOvRTy1I1SYRaQqMsasAzKBAiDfWtvFGBMNfAY0BNYBF1tr070VI8D0tTsJNHkkbvwRWp4FwZHeDEdERIqhBE+kijDG0LVhNF0bRrM3N58pq9L4Y/k2/li2jbFLtgLQqk44fZvF0r1xNF0aRhMe5O/lqEWkDPpba3cc8vsDwG/W2ieMMQ8U/X6/d0Jzpq9N44qoZfjsTdf0TBGRSkoJnkgVFBLgx6DWCQxqnYC1lhVb9xxI9vY3U/cx0LpuON0bxdCtUTTdGkYTVSvA26GLSOkNA/oV/fw+MB4vJni5+YXMXp/Ow9ETIbQ2NO7vrVBEROQYlOCJVHHGGFrUDqNF7TBGntqE7NwC5qakM33tTqavTeOjaet5e9JaADokRnB+p0TO7lCXaCV7IpWJBcYaYyzwurX2DSDBWrsZwFq72RgT780AF27cRa28dJrvngY9bwFffYQQEamM9NdZpJoJDvClV9NYejWNBWBffgHzN2QwfU0aPy3awj++Xcy/flhC/xbxnN8pkQEt41WoRcT7eltrNxUlcb8aY5aVdkdjzAhgBEBSUpKn4mPamp0M852Cj83X9EwRkUpMCZ5INRfo5+umaDaK5vaBzVi6eTdfzUnl67mbGLtkK1Eh/pzdoS5D2tamZe1wjeyJeIG1dlPR923GmK+BbsBWY0ydotG7OsC2EvZ9A3gDoEuXLtZTMc5NSef+wElQuyPEt/LUw4iIyElSgidSw7SqE87fz2rN/UNaMnHlDkbPSeXTmRv4YOp6AGJDA2gWH0bzhFCaJYTRLD6UVnXDVbBFxEOMMbUAH2ttZtHPpwH/BL4FrgaeKPo+xntRQt7GBTQrXAsdnvJmGCIichxK8ERqKD9fH/q3jKd/y3gysvOYm5LOyq17WLktkxVb9zB6zkb27MsHICTAl1v7N+X6Po0I8vf1cuQi1U4C8LUxBtx5+RNr7c/GmJnA58aY64EU4CJvBbg7J4/ee3+jwN8P33YXeisMEREpBSV4IkJEsD/9WrheevtZa9mUkcOKrZl8OiOFp35ZzqczU/j7ma04vU1tij6MishJstauAToUc3saMLDiIzra8i2ZdPVZTmZsRyJDor0djoiIHIMqK4hIsYwx1IsMpn+LeF6/sgsf39CdEH8/Rn40h8vfnM7Szbu9HaKIVJDlm9JpaVLwT0z2digiInIcSvBEpFR6N43lhzv68NiwNizdspuzXpjI379eyM6sXG+HJiIetn39UoJNLiFJHb0dioiIHIemaIpIqfn5+nBlz4ac3aEuz41byYfT1vPVnI0MaVub8zvVo1eTWHx9NHVTpLop3LwAAFOnvZcjERGR41GCJyJlFhkSwKPntGF49yTembyO7xds4uu5G0kID2RYcj3O71SPlrXDvR2miJSDwkJLRMYyCnz88I1t4e1wRETkOJTgicgJa5YQxuPnt+MfZ7fm92Xb+GrORt6ZtJY3JqyhVZ1wzmpXm4axtagbGUy9yGDiQgPx0QifSJWSmp5Ns8K17I5oSpSf+mSKiFR2SvBE5KQF+ftyZrs6nNmuDml79vH9gs18NXcj/xu74rDt/H0NtSOCqBsRTKPYWpzepjZ9msXi76vlwCKV1dItu+nks57ChNO8HYqIiJSCEjwRKVcxoYFc3ashV/dqyO6cPDbtymbTrmw27so58POmXdn8uHAzn87cQFSIP2e2q8Ow5Hp0aRClET6RSiZl/VpONxnkNlCBFRGRqsCjCZ4xZgjwPOALvGWtfaKYbfoBzwH+wA5r7amejElEKk54kD/htf2LXY+Xm1/IhBXbGTN/E1/N2cjH01OoGxHE2R3qck5yXdrUjfBCxCJypH0b5gIQkHhUqz4REamEPJbgGWN8gZeBwUAqMNMY8621dskh20QCrwBDrLUpxpj4Yg8mItVOgJ8Pg1onMKh1Aln78hm3dCtj5m3i7UlreX3CGtrVi+Cybkmck1yX0EBNNhDxlsC0otN27XbeDURERErFk5+augGrrLVrAIwxnwLDgCWHbHM58JW1NgXAWrvNg/GISCVVK9CPYcn1GJZcj51ZuXw3fxOjZqTwt68X8u8flnBOcj0u75ZEu0SN6olUpL25+dTJXklGSD0igvT/T0SkKvBkglcP2HDI76lA9yO2aQ74G2PGA2HA89baDzwYk4hUctG1Ari6V0Ou6tmAuRt2MWp6Cl/PTWXUjBTa1Yvg4i6JtK4bTmJUiKpyinjYiq17aGXWkxPbFqV3IiJVgycTvOI+ddliHr8zMBAIBqYaY6ZZaw8rvWeMGQGMAEhKSvJAqCJS2Rhj6JQURaekKB4a2pox8zbyyfQUHh6z+MA2Ab4+1ItyLRgSo4KpHx1CryYxdEiMVOInUg5WbdjC+WYLuxOv8HYoIiJSSp5M8FKB+of8nghsKmabHdbaLCDLGDMB6AAcluBZa98A3gDo0qXLkUmiiFRzEcH+XNWzIVf2aMDaHVms37mX1PRsUtPd943p2Yxbuo0de/YBEBsayICWcQxslUCfprHU0ho+kROya908fIwlvKEqaIqIVBWe/NQzE2hmjGkEbAQuxa25O9QY4CVjjB8QgJvC+awHYxKRKswYQ+O4UBrHhRZ7/669ufy5Yjvjlm7jp0Vb+HxWKgF+PvRsHMOg1gmc06EuEcH+FRy1SNVltiwAwKdOey9HIiIipeWxBM9am2+MuQ34Bdcm4R1r7WJjzMii+1+z1i41xvwMLAAKca0UFnkqJhGp3iJDAg4Ua8krKGTmup38tnQbvy3dysPfLOK/Py3jql4NuK53I2JCA70drkilZq0lYvcy9vqGExKR6O1wRESklIy1VWvGY5cuXeysWbO8HYaIVDGLNmbw6vjV/LhoM0F+vlzWLYkRfRtTOyLI26HJMRhjZltru3g7jqqiPM+RmzOy2fp0LxJiY6lzx6/lckwRESkfxzo/amGKiNQIbetF8PLwTqzalskr41fz/tR1fDRtPRd0TuTmU5uQFBPi7RBFKpXlG9PpYTawq05/b4ciIiJloARPRGqUpvFhPHNxMncPas5rf67mi1mpfDYzhbiwQKJrBRJTK4Dooq+YWgFEhwaQXD+S1nXCMUaVOaXm2Lx2EUEmj4hGnb0dioiIlIESPBGpkepHh/Dv89px+4BmfDZzA6npe0nfm0taVi4b0veyc08umfvyD2wfHxZIvxZx9G8RT+9msYQHqViLVG+5qfMBCK6f7N1ARESkTJTgiUiNVjsiiDsHNSv2vtz8QrZl5jBtzU7+WH6wMqefj6Fzgyj6t4ynb7M4WtYOU989qXaC0xaThz/+sc29HYqIiJSBEjwRkRIE+PmQGBXChZ1DuLBzIvkFhcxJ2cUfy7fxx7JtPPHTMp74aRkxtQLo1TSWPk1j6N00lsQoreeTqm1ffgF1claRFtaE2r4arRYRqUqU4ImIlJKfrw/dGkXTrVE09w9pyZaMHCav2sHkVTuYtGoH383fBEDDmBB6N42lW6NokutHkhQdovV7UqWs2ppJa7OOrNjTvB2KiIiUkRI8EZETVDsiiAs6J3JB50SstazatodJRQnfmHmb+Hh6CgDRtQLokBhBh/qRJBd9RYYEeDl6kZKtW7uaNiaTwqSO3g5FRETKSAmeiEg5MMbQLCGMZglhXNu7EfkFhazYuod5G3Yxb0M68zdkMH7FSva3Hm1XL4JhyXU5u0NdEsLVi08ql8x1cwCIatzJy5GIiEhZKcETEfEAP18fWtcNp3XdcC7vngTAnn35LEjdxdyUXfyyeAv/+mEp//5xKT0bx3Bucj1Ob1ubiGCtdxLv89m2CAC/Ou28HImIiJSVEjwRkQoSGuhHryax9GoSy639m7J6+x6+nbeJMfM28tfRC3hozCIGtIhnUOsEOiZF0iimlqpzildEZy5jR0A9YoPCvR2KiIiUkRI8EREvaRIXyt2Dm3PXoGYsSM3gm3kb+W7+Zn5evAWA8CA/kpOiSK4fScekSJITI4mqpbV74lnbM/fRpGAtmXHtiPV2MCIiUmZK8EREvMwYQ4f6kXSoH8lDZ7Vm9fY9zE1JZ94GN53zpd9XUli0dq9pfCi9msTQq0ksPRvHEBGiKZ1SvlZu2EQvn62srzPc26GIiMgJUIInIlKJ+PoYmieE0TwhjEu6Hr12b8banXwxK5UPpq7Hx0DbehH0ahJL76YxdG4QRUiA/qzLydmxai4AUY07ezkSERE5EfokICJSyR2+dg9y8wuZt2EXk1ftYMrqHbw1cQ2v/bkaHwON40JpXccVd2lTN5zWdcKJCQ309lOQKiR/0zwAwhupgqaISFWkBE9EpIoJ8DvYcP3uwc3J2pfPjHU7mZuyiyWbdjN7fTrfFjVdB6gdHkTbeuF0ahBF56Qo2idGEhzg68VnIJVZrZ1L2e0TQXhYHW+HIiIiJ0AJnohIFVcr0I/+LeLp3yL+wG3pWbks3bybJZt3s3jTbuan7mLc0m0A+PkY2tQtSvgaRNGtYTTx6sUnQF5BIXVzVpEW2ZxwowquIiJVkRI8EZFqKKpWAL2axtKr6cE6iDuzcpmbks7s9e5r1IwU3p28DmOgW8Nozkmuy5lt66hSZw22dusumpsNpMSf6u1QRETkBCnBExGpIaJrBTCwVQIDWyUAbrRmyabdjF++nTHzN/L3rxfxjzGL6ds8jnM61GVw6wRqBeo0UZOkrlpAc5NHSFJHb4ciIiInSGduEZEayt/X50B7hjsGNmXxpt18N38T383fxO/LthHk70PPxjHEhQUSFRJAVK0AokL8iQwJICokgJjQAOpFBhPkr/V81UXWeldBM65ZVy9HIiIiJ0oJnoiIYIyhbb0I2taL4P4hLZm1Pp1v529k1rp0lmzeTfrePHLzC4vdNzY0gHpRISRGBlMvKpjEqGDqRQbTs0mM2jZUMf7bF7GPAALjm3s7FBEROUE684qIyGF8fMyBKp37WWvJzitgZ1Yuu/bmkb43lx179rExPZuNu7JJTc9m6ebd/Lp064FEcOqDA5TgVTE9a21mn18LAn317yYiUlXpL7iIiByXMYaQAD9CAvxIjCp5u8JCy46sfaSmZxMfpsqcVU3EZW9D9k5vhyEiIidBCZ6IiJQbHx9DfFiQkruqKryO+xIRkSrLx9sBiIiIiIiISPlQgiciIiIiIlJNKMETERERERGpJpTgiYiIiIiIVBMeTfCMMUOMMcuNMauMMQ8cY7uuxpgCY8yFnoxHRERERESkOvNYgmeM8QVeBs4AWgOXGWNal7Ddk8AvnopFRERERESkJvDkCF43YJW1do21Nhf4FBhWzHa3A6OBbR6MRUREREREpNrzZIJXD9hwyO+pRbcdYIypB5wHvObBOERERERERGoETyZ4ppjb7BG/Pwfcb60tOOaBjBlhjJlljJm1ffv28opPRERERESkWvHz4LFTgfqH/J4IbDpimy7Ap8YYgFjgTGNMvrX2m0M3sta+AbwB0KVLlyOTRBEREREREQGMtZ7Jl4wxfsAKYCCwEZgJXG6tXVzC9u8B31trvzzOcbcD608yvFhgx0keo7rSa1MyvTbF0+tSMr02xSvL69LAWhvnyWCqE50jPU6vTfH0upRMr03x9LqUrLSvTYnnR4+N4Flr840xt+GqY/oC71hrFxtjRhbdf0Lr7srjRG+MmWWt7XKyx6mO9NqUTK9N8fS6lEyvTfH0uniOzpGepdemeHpdSqbXpnh6XUpWHq+NJ6doYq39EfjxiNuKTeystdd4MhYREREREZHqzqONzkVERERERKTi1NQE7w1vB1CJ6bUpmV6b4ul1KZlem+Lpdanc9O9TMr02xdPrUjK9NsXT61Kyk35tPFZkRURERERERCpWTR3BExERERERqXZqXIJnzP+3d38xcpVlHMe/P1dUpERAqTEtWkEuqAYWTQyxmlQ0pioRLiD+gzTeeMMFJBoFoyGScKnhhkQSJdRQ/6BSJVxRq1S50EJrsWhrJIRo04a9UMGaiFoeL85bd7rd2U3TnT3jzPeTbOacd2dP3vPkzPzynDlnJ1uS/CHJM0lu63s+fUpyX5K5JE8PjF2QZGeSP7bH8/ucYx+SXJTk50kOJvldklvauLVJXpNkT5KnWm2+2sanvjYASWaS/CbJI23dugBJnktyIMn+JE+2MWszZszHeebjcGbk4szHpZmPixtVPk5Vg5dkBrgH+DCwEfhkko39zqpX9wNbFozdBuyqqkuBXW192vwH+FxVXQZcBdzcjhNrAy8BV1fVFcAssCXJVVibE24BDg6sW5d576+q2YF//Wxtxoj5eIr7MR+HMSMXZz4uzXwcbsXzcaoaPODdwDNV9WxV/Qv4HnBtz3PqTVX9AvjLguFrgW1teRtw3WrOaRxU1dGq2teW/073hrQOa0N1jrXVs9pPYW1Ish74KPDNgeGpr8sSrM14MR8HmI/DmZGLMx+HMx9P2xnXZtoavHXAnwfWD7cxzXtjVR2F7k0cWNvzfHqVZANwJfBrrA3wv8ss9gNzwM6qsjadu4EvAC8PjFmXTgGPJtmb5LNtzNqMF/NxeR6zC5iRJzMfh7ob83GYkeTjSL/ofAxlkTH/jagWlWQN8CPg1qp6MVns8Jk+VXUcmE1yHrAjyTt6nlLvklwDzFXV3iSbe57OONpUVUeSrAV2JjnU94R0CvNRp8WMPJX5eCrzcVkjycdp+wTvMHDRwPp64EhPcxlXzyd5E0B7nOt5Pr1IchZdcG2vqofasLUZUFV/Ax6ju09l2muzCfhYkufoLm27OskDWBcAqupIe5wDdtBdDmhtxov5uDyP2caMXJr5eBLzcQmjysdpa/CeAC5N8tYkrwI+ATzc85zGzcPA1ra8FfhJj3PpRbrTkN8CDlbV1wd+ZW2SC9uZSZKcDXwQOMSU16aqbq+q9VW1ge595WdVdSNTXheAJOckOffEMvAh4GmszbgxH5fnMYsZOYz5uDjzcbhR5uPUfdF5ko/QXQs8A9xXVXf1O6P+JPkusBl4A/A8cAfwY+BB4M3An4AbqmrhjeYTLcl7gV8CB5i/XvxLdPcYTHttLqe74XeG7gTRg1V1Z5LXM+W1OaFdgvL5qrrGukCSi+nOSkJ3W8B3quouazN+zMd55uNwZuTizMflmY8nG2U+Tl2DJ0mSJEmTatou0ZQkSZKkiWWDJ0mSJEkTwgZPkiRJkiaEDZ4kSZIkTQgbPEmSJEmaEDZ40oRIsjnJI33PQ5KkcWI+atrY4EmSJEnShLDBk1ZZkhuT7EmyP8m9SWaSHEvytST7kuxKcmF77mySXyX5bZIdSc5v429L8tMkT7W/uaRtfk2SHyY5lGR7kvS2o5IknQbzUVoZNnjSKkpyGfBxYFNVzQLHgU8D5wD7quqdwG7gjvYn3wa+WFWXAwcGxrcD91TVFcB7gKNt/ErgVmAjcDGwacS7JEnSGTMfpZXzyr4nIE2ZDwDvAp5oJw/PBuaAl4Hvt+c8ADyU5HXAeVW1u41vA36Q5FxgXVXtAKiqfwK07e2pqsNtfT+wAXh85HslSdKZMR+lFWKDJ62uANuq6vaTBpOvLHheLbONYV4aWD6Or3FJ0v8H81FaIV6iKa2uXcD1SdYCJLkgyVvoXovXt+d8Cni8ql4A/prkfW38JmB3Vb0IHE5yXdvGq5O8djV3QpKkFWY+SivEsxfSKqqq3yf5MvBoklcA/wZuBv4BvD3JXuAFuvsQALYC32gB9SzwmTZ+E3BvkjvbNm5Yxd2QJGlFmY/SyknVUp90S1oNSY5V1Zq+5yFJ0jgxH6XT5yWakiRJkjQh/ARPkiRJkiaEn+BJkiRJ0oSwwZMkSZKkCWGDJ0mSJEkTwgZPkiRJkiaEDZ4kSZIkTQgbPEmSJEmaEP8FXDwdJzI7yjMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "loss_train = []\n",
    "loss_test = []\n",
    "A_train = []\n",
    "A_test = []\n",
    "for epoch in range(num_epochs):\n",
    "\t#Load in the data in batches using the train_loader object\n",
    "    l_train = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(train_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_C(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_train += loss\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    l_train /=  samples\n",
    "    loss_train.append(float(l_train.cpu().detach().numpy()))\n",
    "    \n",
    "    l_test = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(test_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_C(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_test += loss\n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "    l_test /=  samples\n",
    "    loss_test.append(float(l_test.cpu().detach().numpy()))\n",
    "    \n",
    "    A_train.append(train_accuracy(model_C))\n",
    "    A_test.append(test_accuracy(model_C))\n",
    "    \n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "    \n",
    "plt.figure(figsize = (15,5))    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(loss_train, label = \"Training set\")\n",
    "plt.plot(loss_test, label = \"Test set\")\n",
    "plt.title(\"Loss Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(A_train, label = \"Training set\")\n",
    "plt.plot(A_test, label = \"Test set\")\n",
    "plt.title(\"Accuracy Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"config2_model_C.jpg\")\n",
    "    \n",
    "#     for i, \n",
    "        \n",
    "\n",
    "    \n",
    "#     print(f\"Accuracy after {epoch  +1} :\")\n",
    "#     print(f\"Train set: {train_accuracy(model_A)}\")\n",
    "# print(test_accuracy(model_A))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27c89481",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "00584523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.modules of ConvNeuralNet_C(\n",
       "  (conv_layer1): Conv2d(3, 6, kernel_size=(7, 7), stride=(1, 1))\n",
       "  (max_pool1): MaxPool2d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer2): Conv2d(6, 12, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (max_pool2): MaxPool2d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer3): Conv2d(12, 24, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool3): MaxPool2d(kernel_size=3, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=4704, out_features=128, bias=True)\n",
       "  (relu1): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_C.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fee7c090",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07ca239d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5435b8fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b185ad4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16ff44a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "166b8347",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e7379ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbd33043",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66452ee2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8fe162a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24b448af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "49251053",
   "metadata": {},
   "source": [
    "# Training Models with configuration 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138d0d4a",
   "metadata": {},
   "source": [
    "## Model A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "acb6d26a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN class\n",
    "class ConvNeuralNet_A(nn.Module):\n",
    "\t#  Determine what layers and their order in CNN object \n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNeuralNet_A, self).__init__()\n",
    "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=7)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.max_pool1 = nn.MaxPool2d(kernel_size = 4, stride = 1)\n",
    "        \n",
    "        self.conv_layer2 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=5)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=, out_channels=64, kernel_size=3)\n",
    "        self.max_pool2 = nn.MaxPool2d(kernel_size = 4, stride = 1)\n",
    "        self.conv_layer3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3)\n",
    "        self.max_pool3 = nn.MaxPool2d(kernel_size = 4, stride = 1)\n",
    "        self.fc1 = nn.Linear(11*11*24, num_classes)\n",
    "#         self.relu1 = nn.ReLU()\n",
    "#         self.fc2 = nn.Linear(128, num_classes)\n",
    "    \n",
    "    # Progresses data across layers    \n",
    "    def forward(self, x):\n",
    "        out = self.conv_layer1(x)\n",
    "        out = self.max_pool1(out)\n",
    "        \n",
    "        out = self.conv_layer2(out)\n",
    "        out = self.max_pool2(out)\n",
    "        \n",
    "        out = self.conv_layer3(out)\n",
    "        out = self.max_pool3(out)\n",
    "        \n",
    "#         out = self.conv_layer2(out)\n",
    "        \n",
    "        \n",
    "#         out = self.conv_layer3(out)\n",
    "#         out = self.conv_layer4(out)\n",
    "#         out = self.max_pool2(out)\n",
    "                \n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        \n",
    "        out = self.fc1(out)\n",
    "#         out = self.relu1(out)\n",
    "#         out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "837c494b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_A = ConvNeuralNet_A(num_classes)\n",
    "model_A.to(device)\n",
    "# Set Loss function with criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set optimizer with optimizer\n",
    "optimizer = torch.optim.SGD(model_A.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
    "\n",
    "total_step = len(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "12ed0600",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 1.7162\n",
      "Epoch [2/50], Loss: 1.6299\n",
      "Epoch [3/50], Loss: 1.0360\n",
      "Epoch [4/50], Loss: 1.4320\n",
      "Epoch [5/50], Loss: 1.1204\n",
      "Epoch [6/50], Loss: 1.6931\n",
      "Epoch [7/50], Loss: 1.0628\n",
      "Epoch [8/50], Loss: 1.1342\n",
      "Epoch [9/50], Loss: 1.6844\n",
      "Epoch [10/50], Loss: 1.1584\n",
      "Epoch [11/50], Loss: 1.0178\n",
      "Epoch [12/50], Loss: 1.2707\n",
      "Epoch [13/50], Loss: 1.1078\n",
      "Epoch [14/50], Loss: 1.3423\n",
      "Epoch [15/50], Loss: 0.9498\n",
      "Epoch [16/50], Loss: 1.1027\n",
      "Epoch [17/50], Loss: 1.4558\n",
      "Epoch [18/50], Loss: 1.1807\n",
      "Epoch [19/50], Loss: 1.5387\n",
      "Epoch [20/50], Loss: 1.4819\n",
      "Epoch [21/50], Loss: 0.8524\n",
      "Epoch [22/50], Loss: 2.0073\n",
      "Epoch [23/50], Loss: 1.2768\n",
      "Epoch [24/50], Loss: 1.0232\n",
      "Epoch [25/50], Loss: 1.2992\n",
      "Epoch [26/50], Loss: 1.1216\n",
      "Epoch [27/50], Loss: 1.0485\n",
      "Epoch [28/50], Loss: 1.0146\n",
      "Epoch [29/50], Loss: 0.7060\n",
      "Epoch [30/50], Loss: 1.3827\n",
      "Epoch [31/50], Loss: 1.2253\n",
      "Epoch [32/50], Loss: 0.8902\n",
      "Epoch [33/50], Loss: 1.1872\n",
      "Epoch [34/50], Loss: 0.6910\n",
      "Epoch [35/50], Loss: 1.3589\n",
      "Epoch [36/50], Loss: 1.3785\n",
      "Epoch [37/50], Loss: 1.7441\n",
      "Epoch [38/50], Loss: 0.7016\n",
      "Epoch [39/50], Loss: 1.4261\n",
      "Epoch [40/50], Loss: 1.4074\n",
      "Epoch [41/50], Loss: 0.9460\n",
      "Epoch [42/50], Loss: 0.9465\n",
      "Epoch [43/50], Loss: 1.4860\n",
      "Epoch [44/50], Loss: 0.7808\n",
      "Epoch [45/50], Loss: 1.3661\n",
      "Epoch [46/50], Loss: 0.5706\n",
      "Epoch [47/50], Loss: 1.0145\n",
      "Epoch [48/50], Loss: 1.0007\n",
      "Epoch [49/50], Loss: 0.8414\n",
      "Epoch [50/50], Loss: 1.3315\n",
      "CPU times: user 16min 7s, sys: 1.14 s, total: 16min 8s\n",
      "Wall time: 15min 13s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "loss_train = []\n",
    "loss_test = []\n",
    "A_train = []\n",
    "A_test = []\n",
    "for epoch in range(num_epochs):\n",
    "\t#Load in the data in batches using the train_loader object\n",
    "    l_train = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(train_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_A(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_train += loss\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    l_train /=  samples\n",
    "    loss_train.append(float(l_train.cpu().detach().numpy()))\n",
    "    \n",
    "    l_test = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(test_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_A(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_test += loss\n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "    l_test /=  samples\n",
    "    loss_test.append(float(l_test.cpu().detach().numpy()))\n",
    "    \n",
    "    A_train.append(train_accuracy(model_A))\n",
    "    A_test.append(test_accuracy(model_A))\n",
    "    \n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "    \n",
    "plt.figure(figsize = (15,5))    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(loss_train, label = \"Training set\")\n",
    "plt.plot(loss_test, label = \"Test set\")\n",
    "plt.title(\"Loss Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(A_train, label = \"Training set\")\n",
    "plt.plot(A_test, label = \"Test set\")\n",
    "plt.title(\"Accuracy Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"config3_model_A.jpg\")\n",
    "    \n",
    "#     for i, \n",
    "        \n",
    "\n",
    "    \n",
    "#     print(f\"Accuracy after {epoch  +1} :\")\n",
    "#     print(f\"Train set: {train_accuracy(model_A)}\")\n",
    "# print(test_accuracy(model_A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "80bfa67d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.modules of ConvNeuralNet_A(\n",
       "  (conv_layer1): Conv2d(3, 6, kernel_size=(7, 7), stride=(1, 1))\n",
       "  (max_pool1): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer2): Conv2d(6, 12, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (max_pool2): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer3): Conv2d(12, 24, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool3): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=2904, out_features=10, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_A.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38f5ba2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea43ed3c",
   "metadata": {},
   "source": [
    "### Model B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e6cb248c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN class\n",
    "class ConvNeuralNet_B(nn.Module):\n",
    "\t#  Determine what layers and their order in CNN object \n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNeuralNet_B, self).__init__()\n",
    "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)\n",
    "        self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.max_pool1 = nn.MaxPool2d(kernel_size = 4, stride = 1)\n",
    "        \n",
    "        self.conv_layer3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3)\n",
    "        self.conv_layer4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3)\n",
    "        self.max_pool2 = nn.MaxPool2d(kernel_size = 4, stride = 1)\n",
    "        \n",
    "        self.fc1 = nn.Linear(18*18*64, 128)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(128, num_classes)\n",
    "    \n",
    "    # Progresses data across layers    \n",
    "    def forward(self, x):\n",
    "        out = self.conv_layer1(x)\n",
    "        out = self.conv_layer2(out)\n",
    "        out = self.max_pool1(out)\n",
    "        \n",
    "        out = self.conv_layer3(out)\n",
    "        out = self.conv_layer4(out)\n",
    "        out = self.max_pool2(out)\n",
    "                \n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        \n",
    "        out = self.fc1(out)\n",
    "        out = self.relu1(out)\n",
    "        out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2b63089",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_B = ConvNeuralNet_B(num_classes)\n",
    "model_B.to(device)\n",
    "# Set Loss function with criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set optimizer with optimizer\n",
    "optimizer = torch.optim.SGD(model_B.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
    "\n",
    "total_step = len(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df7b9fbd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b9b219f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "# for epoch in range(num_epochs):\n",
    "# \t#Load in the data in batches using the train_loader object\n",
    "#     for i, (images, labels) in enumerate(train_loader):  \n",
    "#         # Move tensors to the configured device\n",
    "#         images = images.to(device)\n",
    "#         labels = labels.to(device)\n",
    "        \n",
    "#         # Forward pass\n",
    "#         outputs = model_B(images)\n",
    "#         loss = criterion(outputs, labels)\n",
    "        \n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "\n",
    "#     print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a62c222a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 1.5641\n",
      "Epoch [2/50], Loss: 1.1632\n",
      "Epoch [3/50], Loss: 1.0125\n",
      "Epoch [4/50], Loss: 0.6414\n",
      "Epoch [5/50], Loss: 0.6497\n",
      "Epoch [6/50], Loss: 1.1194\n",
      "Epoch [7/50], Loss: 1.0660\n",
      "Epoch [8/50], Loss: 0.4246\n",
      "Epoch [9/50], Loss: 0.9191\n",
      "Epoch [10/50], Loss: 0.9212\n",
      "Epoch [11/50], Loss: 1.4056\n",
      "Epoch [12/50], Loss: 0.7481\n",
      "Epoch [13/50], Loss: 1.2862\n",
      "Epoch [14/50], Loss: 0.5925\n",
      "Epoch [15/50], Loss: 0.8993\n",
      "Epoch [16/50], Loss: 1.0140\n",
      "Epoch [17/50], Loss: 0.3161\n",
      "Epoch [18/50], Loss: 0.8250\n",
      "Epoch [19/50], Loss: 0.6075\n",
      "Epoch [20/50], Loss: 0.2198\n",
      "Epoch [21/50], Loss: 0.4968\n",
      "Epoch [22/50], Loss: 0.6105\n",
      "Epoch [23/50], Loss: 0.9106\n",
      "Epoch [24/50], Loss: 1.6980\n",
      "Epoch [25/50], Loss: 0.2998\n",
      "Epoch [26/50], Loss: 0.4839\n",
      "Epoch [27/50], Loss: 0.8305\n",
      "Epoch [28/50], Loss: 1.1097\n",
      "Epoch [29/50], Loss: 0.9774\n",
      "Epoch [30/50], Loss: 0.8650\n",
      "Epoch [31/50], Loss: 1.1733\n",
      "Epoch [32/50], Loss: 1.3060\n",
      "Epoch [33/50], Loss: 0.9791\n",
      "Epoch [34/50], Loss: 1.0698\n",
      "Epoch [35/50], Loss: 0.7855\n",
      "Epoch [36/50], Loss: 1.9256\n",
      "Epoch [37/50], Loss: 1.4338\n",
      "Epoch [38/50], Loss: 1.5421\n",
      "Epoch [39/50], Loss: 0.9039\n",
      "Epoch [40/50], Loss: 0.9372\n",
      "Epoch [41/50], Loss: 0.4436\n",
      "Epoch [42/50], Loss: 0.4447\n",
      "Epoch [43/50], Loss: 0.5699\n",
      "Epoch [44/50], Loss: 0.8826\n",
      "Epoch [45/50], Loss: 1.4347\n",
      "Epoch [46/50], Loss: 0.4239\n",
      "Epoch [47/50], Loss: 2.1242\n",
      "Epoch [48/50], Loss: 1.3706\n",
      "Epoch [49/50], Loss: 1.0315\n",
      "Epoch [50/50], Loss: 1.5216\n",
      "CPU times: user 16min 55s, sys: 1.24 s, total: 16min 56s\n",
      "Wall time: 16min 1s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "loss_train = []\n",
    "loss_test = []\n",
    "A_train = []\n",
    "A_test = []\n",
    "for epoch in range(num_epochs):\n",
    "\t#Load in the data in batches using the train_loader object\n",
    "    l_train = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(train_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_B(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_train += loss\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    l_train /=  samples\n",
    "    loss_train.append(float(l_train.cpu().detach().numpy()))\n",
    "    \n",
    "    l_test = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(test_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_B(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_test += loss\n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "    l_test /=  samples\n",
    "    loss_test.append(float(l_test.cpu().detach().numpy()))\n",
    "    \n",
    "    A_train.append(train_accuracy(model_B))\n",
    "    A_test.append(test_accuracy(model_B))\n",
    "    \n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "    \n",
    "plt.figure(figsize = (15,5))    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(loss_train, label = \"Training set\")\n",
    "plt.plot(loss_test, label = \"Test set\")\n",
    "plt.title(\"Loss Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(A_train, label = \"Training set\")\n",
    "plt.plot(A_test, label = \"Test set\")\n",
    "plt.title(\"Accuracy Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"config3_model_B.jpg\")\n",
    "    \n",
    "#     for i, \n",
    "        \n",
    "\n",
    "    \n",
    "#     print(f\"Accuracy after {epoch  +1} :\")\n",
    "#     print(f\"Train set: {train_accuracy(model_A)}\")\n",
    "# print(test_accuracy(model_A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "892bf9a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.modules of ConvNeuralNet_B(\n",
       "  (conv_layer1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (conv_layer2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool1): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (conv_layer4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool2): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=20736, out_features=128, bias=True)\n",
       "  (relu1): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_B.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a08d8f33",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebf1643d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0ff427f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd7186bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "83c63610",
   "metadata": {},
   "source": [
    "### Model C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "33962492",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a CNN class\n",
    "class ConvNeuralNet_C(nn.Module):\n",
    "\t#  Determine what layers and their order in CNN object \n",
    "    def __init__(self, num_classes):\n",
    "        super(ConvNeuralNet_C, self).__init__()\n",
    "        self.conv_layer1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=7)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.max_pool1 = nn.MaxPool2d(kernel_size = 4, stride = 1)\n",
    "        \n",
    "        self.conv_layer2 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=5)\n",
    "#         self.conv_layer2 = nn.Conv2d(in_channels=, out_channels=64, kernel_size=3)\n",
    "        self.max_pool2 = nn.MaxPool2d(kernel_size = 4, stride = 1)\n",
    "        self.conv_layer3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3)\n",
    "        self.max_pool3 = nn.MaxPool2d(kernel_size = 4, stride = 1)\n",
    "        self.fc1 = nn.Linear(11*11*24, 128)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(128, num_classes)\n",
    "    \n",
    "    # Progresses data across layers    \n",
    "    def forward(self, x):\n",
    "        out = self.conv_layer1(x)\n",
    "        out = self.max_pool1(out)\n",
    "        \n",
    "        out = self.conv_layer2(out)\n",
    "        out = self.max_pool2(out)\n",
    "        \n",
    "        out = self.conv_layer3(out)\n",
    "        out = self.max_pool3(out)\n",
    "        \n",
    "#         out = self.conv_layer2(out)\n",
    "        \n",
    "        \n",
    "#         out = self.conv_layer3(out)\n",
    "#         out = self.conv_layer4(out)\n",
    "#         out = self.max_pool2(out)\n",
    "                \n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        \n",
    "        out = self.fc1(out)\n",
    "        out = self.relu1(out)\n",
    "        out = self.fc2(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ec38c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_C = ConvNeuralNet_C(num_classes)\n",
    "model_C.to(device)\n",
    "# Set Loss function with criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Set optimizer with optimizer\n",
    "optimizer = torch.optim.SGD(model_C.parameters(), lr=learning_rate, weight_decay = 0.005, momentum = 0.9)  \n",
    "\n",
    "total_step = len(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cdd3cd12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Loss: 1.4274\n",
      "Epoch [2/50], Loss: 1.6189\n",
      "Epoch [3/50], Loss: 0.9908\n",
      "Epoch [4/50], Loss: 1.2325\n",
      "Epoch [5/50], Loss: 0.8829\n",
      "Epoch [6/50], Loss: 1.6904\n",
      "Epoch [7/50], Loss: 1.0879\n",
      "Epoch [8/50], Loss: 1.1203\n",
      "Epoch [9/50], Loss: 0.8325\n",
      "Epoch [10/50], Loss: 1.6702\n",
      "Epoch [11/50], Loss: 1.2759\n",
      "Epoch [12/50], Loss: 1.2662\n",
      "Epoch [13/50], Loss: 0.8673\n",
      "Epoch [14/50], Loss: 0.9961\n",
      "Epoch [15/50], Loss: 1.1026\n",
      "Epoch [16/50], Loss: 1.9576\n",
      "Epoch [17/50], Loss: 0.9629\n",
      "Epoch [18/50], Loss: 0.5970\n",
      "Epoch [19/50], Loss: 0.9659\n",
      "Epoch [20/50], Loss: 1.0759\n",
      "Epoch [21/50], Loss: 1.7715\n",
      "Epoch [22/50], Loss: 0.9711\n",
      "Epoch [23/50], Loss: 1.3201\n",
      "Epoch [24/50], Loss: 0.8437\n",
      "Epoch [25/50], Loss: 1.0069\n",
      "Epoch [26/50], Loss: 0.6236\n",
      "Epoch [27/50], Loss: 1.0288\n",
      "Epoch [28/50], Loss: 1.4606\n",
      "Epoch [29/50], Loss: 1.2583\n",
      "Epoch [30/50], Loss: 0.7376\n",
      "Epoch [31/50], Loss: 1.1627\n",
      "Epoch [32/50], Loss: 0.6588\n",
      "Epoch [33/50], Loss: 0.7629\n",
      "Epoch [34/50], Loss: 1.1589\n",
      "Epoch [35/50], Loss: 0.8244\n",
      "Epoch [36/50], Loss: 1.6172\n",
      "Epoch [37/50], Loss: 1.6202\n",
      "Epoch [38/50], Loss: 1.3582\n",
      "Epoch [39/50], Loss: 1.5235\n",
      "Epoch [40/50], Loss: 0.4317\n",
      "Epoch [41/50], Loss: 0.5055\n",
      "Epoch [42/50], Loss: 1.2151\n",
      "Epoch [43/50], Loss: 2.1750\n",
      "Epoch [44/50], Loss: 1.2016\n",
      "Epoch [45/50], Loss: 1.8010\n",
      "Epoch [46/50], Loss: 0.6217\n",
      "Epoch [47/50], Loss: 2.0330\n",
      "Epoch [48/50], Loss: 1.3698\n",
      "Epoch [49/50], Loss: 1.1262\n",
      "Epoch [50/50], Loss: 1.2015\n",
      "CPU times: user 16min 18s, sys: 1.08 s, total: 16min 19s\n",
      "Wall time: 15min 23s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We use the pre-defined number of epochs to determine how many iterations to train the network on\n",
    "loss_train = []\n",
    "loss_test = []\n",
    "A_train = []\n",
    "A_test = []\n",
    "for epoch in range(num_epochs):\n",
    "\t#Load in the data in batches using the train_loader object\n",
    "    l_train = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(train_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_C(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_train += loss\n",
    "        # Backward and optimize\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    l_train /=  samples\n",
    "    loss_train.append(float(l_train.cpu().detach().numpy()))\n",
    "    \n",
    "    l_test = 0\n",
    "    samples = 0\n",
    "    for i, (images, labels) in enumerate(test_loader): \n",
    "        samples += 1\n",
    "        # Move tensors to the configured device\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # Forward pass\n",
    "        outputs = model_C(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        l_test += loss\n",
    "#         # Backward and optimize\n",
    "#         optimizer.zero_grad()\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "    l_test /=  samples\n",
    "    loss_test.append(float(l_test.cpu().detach().numpy()))\n",
    "    \n",
    "    A_train.append(train_accuracy(model_C))\n",
    "    A_test.append(test_accuracy(model_C))\n",
    "    \n",
    "    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))\n",
    "    \n",
    "plt.figure(figsize = (15,5))    \n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(loss_train, label = \"Training set\")\n",
    "plt.plot(loss_test, label = \"Test set\")\n",
    "plt.title(\"Loss Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(A_train, label = \"Training set\")\n",
    "plt.plot(A_test, label = \"Test set\")\n",
    "plt.title(\"Accuracy Curve\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"config3_model_C.jpg\")\n",
    "    \n",
    "#     for i, \n",
    "        \n",
    "\n",
    "    \n",
    "#     print(f\"Accuracy after {epoch  +1} :\")\n",
    "#     print(f\"Train set: {train_accuracy(model_A)}\")\n",
    "# print(test_accuracy(model_A))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7216376",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5c58ce18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.modules of ConvNeuralNet_C(\n",
       "  (conv_layer1): Conv2d(3, 6, kernel_size=(7, 7), stride=(1, 1))\n",
       "  (max_pool1): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer2): Conv2d(6, 12, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (max_pool2): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv_layer3): Conv2d(12, 24, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (max_pool3): MaxPool2d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=2904, out_features=128, bias=True)\n",
       "  (relu1): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_C.modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76252ca8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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