resnet_6.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/nxf-emma/APS360-Artificial-Intelligence-Fundamentals/blob/main/resnet_6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1sah8OC1-pIq",
        "outputId": "2eb76404-1e98-4715-9091-4616170c30f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#mount googledrive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import os\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.optim as optim\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "from torch.utils.data.sampler import SubsetRandomSampler"
      ],
      "metadata": {
        "id": "mDAi3Xqw-vzP"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# train"
      ],
      "metadata": {
        "id": "blytVQUDUwYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_accuracy(model, dataloader):\n",
        "    \n",
        "\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for imgs, labels in dataloader:\n",
        "        \n",
        "        if use_cuda and torch.cuda.is_available():\n",
        "            imgs = imgs.cuda()\n",
        "            labels = labels.cuda()\n",
        "\n",
        "        output = model(imgs)\n",
        "        \n",
        "        #select index with maximum prediction score\n",
        "        pred = output.max(1, keepdim=True)[1]\n",
        "        correct += pred.eq(labels.view_as(pred)).sum().item()\n",
        "        total += imgs.shape[0]\n",
        "    return correct / total"
      ],
      "metadata": {
        "id": "Y22pKAZe-yXW"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Convolutional Neural Network Architecture\n",
        "class CNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNN, self).__init__()\n",
        "\n",
        "        self.pool = nn.MaxPool2d(2, 2) #kernel_size, stride \n",
        "\n",
        "        self.conv1 = nn.Conv2d(3, 5, 3, padding = 2) #in_channels, out_chanels, kernel_size \n",
        "        self.conv2 = nn.Conv2d(5, 25, 3) #in_channels, out_chanels, kernel_size\n",
        "        self.conv3 = nn.Conv2d(25, 50, 1)\n",
        "        self.conv4 = nn.Conv2d(50, 200, 1)\n",
        "        \n",
        "        self.fc1 = nn.Linear(200*2*2, 70) #chanels*dimension\n",
        "        self.fc3 = nn.Linear(70, 32)\n",
        "        self.fc2 = nn.Linear(32, 7) #last output channel always = 7\n",
        "        self.dropout = nn.Dropout(0.4)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        # 25x25\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        # 11\n",
        "        x = self.pool(F.relu(self.conv3(x)))\n",
        "        # 5\n",
        "        x = self.pool(F.relu(self.conv4(x)))\n",
        "        # 2\n",
        "\n",
        "\n",
        "        x = x.view(-1, 200*4)\n",
        "        x = F.relu(self.dropout(self.fc1(x)))\n",
        "        x = F.relu(self.dropout(self.fc3(x)))\n",
        "        x = self.fc2(x)\n",
        "        x = x.squeeze(1)\n",
        "        return x"
      ],
      "metadata": {
        "id": "S2CVXHj6-0j2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train2(model, train_data, val_data, batch_size=3000, num_epochs=5, lr = 0.01,weight_decay = 5e-3):\n",
        "    \n",
        "    num_workers = 1\n",
        "    train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, \n",
        "                                           num_workers=num_workers, shuffle=True)\n",
        "    val_loader = torch.utils.data.DataLoader(val_data, batch_size=batch_size, \n",
        "                                          num_workers=num_workers, shuffle=True)\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9,weight_decay = weight_decay)\n",
        "\n",
        "    iters, losses, train_acc, val_acc, val_loss = [], [], [], [],[]\n",
        "\n",
        "    # training\n",
        "    n = 0 # the number of iterations\n",
        "    for epoch in range(num_epochs):\n",
        "        print(\"Epoch\", epoch)\n",
        "        n = 0\n",
        "        for imgs, labels in iter(train_loader):\n",
        "            # print(\"iteration \", n)\n",
        "\n",
        "            if use_cuda and torch.cuda.is_available():\n",
        "                imgs = imgs.cuda()\n",
        "                labels = labels.cuda()\n",
        "\n",
        "            out = model(imgs)             # forward pass\n",
        "\n",
        "            loss = criterion(out, labels) # compute the total loss\n",
        "            loss.backward()               # backward pass (compute parameter updates)\n",
        "            optimizer.step()              # make the updates for each parameter\n",
        "            optimizer.zero_grad()         # a clean up step for PyTorch\n",
        "            n += 1\n",
        "\n",
        "        # for imgs, labels in iter(val_loader):\n",
        "        #     # print(\"iteration \", n)\n",
        "\n",
        "        #     if use_cuda and torch.cuda.is_available():\n",
        "        #         imgs = imgs.cuda()\n",
        "        #         labels = labels.cuda()\n",
        "\n",
        "        #     out = model(imgs)             # forward pass\n",
        "\n",
        "        #     loss_val = criterion(out, labels) # compute the total loss\n",
        "        \n",
        "        # save the current training information\n",
        "        iters.append(epoch)\n",
        "        losses.append(float(loss)/batch_size)             # compute *average* loss\n",
        "        # val_loss.append(float(loss_val)/batch_size)\n",
        "        # calculate accuracy at each epoch instead of iteration\n",
        "        # def get_accuracy(model, data):\n",
        "        train_acc.append(get_accuracy(model, train_loader)) # compute training accuracy \n",
        "        # compute validation acc every epoch\n",
        "        val_acc.append(get_accuracy(model, val_loader))  # compute validation accuracy\n",
        "        print(\"Epoch: \", epoch, \" Train Accuracy: \", train_acc[epoch])\n",
        "        print(\"Epoch: \", epoch, \" Validation Accuracy: \", val_acc[epoch])\n",
        "\n",
        "            \n",
        "\n",
        "    # plotting\n",
        "    plt.title(\"Training Curve\")\n",
        "    plt.plot(iters, losses, label=\"Train\")\n",
        "    # plt.plot(iters, val_loss, label=\"val\")\n",
        "    plt.xlabel(\"Iterations\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()\n",
        "\n",
        "    plt.title(\"Training Curve\")\n",
        "    plt.plot(iters, train_acc, label=\"Train\")\n",
        "    plt.plot(iters, val_acc, label=\"Validation\")\n",
        "    plt.xlabel(\"Iterations\")\n",
        "    plt.ylabel(\"Training Accuracy\")\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()\n",
        "\n",
        "    print(\"Final Training Accuracy: {}\".format(train_acc[-1]))\n",
        "    print(\"Final Validation Accuracy: {}\".format(val_acc[-1]))"
      ],
      "metadata": {
        "id": "3zr9hnXl_ahW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train_path = '/content/drive/MyDrive/APS360 Team/aps360_dataset/FER-2013_final/train'\n",
        "# val_path = '/content/drive/MyDrive/APS360 Team/aps360_dataset/FER-2013_final/val'\n",
        "# test_path = '/content/drive/MyDrive/APS360 Team/aps360_dataset/FER-2013_final/test'\n",
        "\n",
        "# train_data = datasets.DatasetFolder(train_path, loader=torch.load, extensions=('.tensor'))\n",
        "# val_data = datasets.DatasetFolder(val_path, loader=torch.load, extensions=('.tensor'))\n",
        "# test_data = datasets.DatasetFolder(test_path, loader=torch.load, extensions=('.tensor'))"
      ],
      "metadata": {
        "id": "5Lb5IYAF_iP1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AlexNet"
      ],
      "metadata": {
        "id": "OsrfMfdZOV46"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alexnet = torchvision.models.alexnet(pretrained=True)"
      ],
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        "id": "7nqfB7WJOKy7",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /root/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/233M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "11cd7ea91e5140d2b383f22b08f5f9a0"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parent_path = '/content/drive/MyDrive/APS360 Team/aps360_dataset/FER-2013/'\n",
        "\n",
        "data_transform = transforms.Compose(\n",
        "    [\n",
        "        transforms.Resize((224,224)), \n",
        "        #convert image to grayscale\n",
        "        # transforms.Grayscale(num_output_channels=3),\n",
        "        #randomly adding brightness \n",
        "        # transforms.ColorJitter(brightness=(1,1.2)),\n",
        "        #apply ramdom rotation from -30 to 30 degrees\n",
        "        # transforms.RandomRotation(30),\n",
        "        #apply random horizontal flip with probability of 50% \n",
        "        transforms.RandomHorizontalFlip(p=0.5),\n",
        "        transforms.ToTensor(),\n",
        "        #normalize the data with 0.5 mean and variance.\n",
        "        transforms.Normalize((0.5073955,), (0.25,), inplace=True)\n",
        "       \n",
        "    ])\n",
        "training_set = torchvision.datasets.ImageFolder(parent_path+'train',data_transform)\n",
        "valid_set = torchvision.datasets.ImageFolder(parent_path+'valid',data_transform)\n",
        "\n",
        "# train_data = datasets.DatasetFolder(parent_path+'train', loader=torch.load, extensions=('.tensor'))\n",
        "# val_data = datasets.DatasetFolder(parent_path+'val', loader=torch.load, extensions=('.tensor'))\n",
        "# train_data = train_data.view(3,224,224)\n",
        "# val_data = val_data.view(3,224,224)"
      ],
      "metadata": {
        "id": "ZQNca4vpOVXa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_accuracy_alex(model, data):\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    # model.eval() #*********#\n",
        "    for imgs, labels in torch.utils.data.DataLoader(data):\n",
        "\n",
        "\n",
        "        #############################################\n",
        "        #To Enable GPU Usage\n",
        "        if use_cuda and torch.cuda.is_available():\n",
        "          imgs = imgs.cuda()\n",
        "          labels = labels.cuda()\n",
        "        #############################################\n",
        "        output = model(imgs)\n",
        "        pred = output.max(1, keepdim=True)[1] # get the index of the max logit\n",
        "        correct += pred.eq(labels.view_as(pred)).sum().item()\n",
        "        total += imgs.shape[0]\n",
        "    return correct / total"
      ],
      "metadata": {
        "id": "5QqUdVklPNyx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_alex(model, train, valid, batch_size=20, num_iters=1, learn_rate=0.01, weight_decay=5e-4):\n",
        "    train_loader = torch.utils.data.DataLoader(train,\n",
        "                                               batch_size=batch_size,\n",
        "                                               shuffle=True) # shuffle after every epoch\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = optim.SGD(model.parameters(), lr=learn_rate, momentum=0.9, weight_decay=weight_decay)\n",
        "\n",
        "    iters, losses, train_acc, val_acc = [], [], [], []\n",
        "\n",
        "    # training\n",
        "    n = 0 # the number of iterations\n",
        "    while True:\n",
        "        if n >= num_iters:\n",
        "            break\n",
        "        for imgs, labels in iter(train_loader):\n",
        "\n",
        "\n",
        "            #############################################\n",
        "            #To Enable GPU Usage\n",
        "            if use_cuda and torch.cuda.is_available():\n",
        "              imgs = imgs.cuda()\n",
        "              labels = labels.cuda()\n",
        "            #############################################\n",
        "\n",
        "            # model.train() #*****************************#\n",
        "            out = model(imgs)             # forward pass\n",
        "            loss = criterion(out, labels) # compute the total loss\n",
        "            loss.backward()               # backward pass (compute parameter updates)\n",
        "            optimizer.step()              # make the updates for each parameter\n",
        "            optimizer.zero_grad()         # a clean up step for PyTorch\n",
        "\n",
        "            # save the current training information\n",
        "        iters.append(n)\n",
        "        losses.append(float(loss)/batch_size)        # compute *average* loss\n",
        "        train_acc.append(get_accuracy_alex(model, train)) # compute training accuracy \n",
        "        val_acc.append(get_accuracy_alex(model, valid))   # compute validation accuracy\n",
        "        print (\"Epoch \" ,n, \"Train Accuracy \", train_acc[n])\n",
        "        print (\"Epoch \" ,n, \"Validation Accuracy \", val_acc[n])\n",
        "        n += 1\n",
        "\n",
        "        \n",
        "\n",
        "\n",
        "    # plotting\n",
        "    plt.title(\"Training Curve\")\n",
        "    plt.plot(iters, losses, label=\"Train\")\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.show()\n",
        "\n",
        "    plt.title(\"Accuracy Curve\")\n",
        "    plt.plot(iters, train_acc, label=\"Train\")\n",
        "    plt.plot(iters, val_acc, label=\"Validation\")\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Training Accuracy\")\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()\n",
        "\n",
        "    print(\"Final Training Accuracy: {}\".format(train_acc[-1]))\n",
        "    print(\"Final Validation Accuracy: {}\".format(val_acc[-1]))"
      ],
      "metadata": {
        "id": "J9L8eCjUO4sY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def save_feature(data, path):\n",
        "  \n",
        "  # classes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']\n",
        "  classes = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']\n",
        "\n",
        "\n",
        "  dataloader = torch.utils.data.DataLoader(data, batch_size=1) #save one at a time\n",
        "  i = 0\n",
        "  for imgs, labels in (dataloader):\n",
        "    features = alexnet.features(imgs)\n",
        "    #convert to tensor\n",
        "    x = torch.from_numpy(features.detach().numpy()) \n",
        "    # save into drive\n",
        "    fullPath = path + '/' + classes[labels]\n",
        "\n",
        "    # create folder\n",
        "    if not os.path.isdir(fullPath):\n",
        "      os.mkdir(fullPath)\n",
        "    # give name and squeeze https://piazza.com/class/ky528kuhk7b27q?cid=285\n",
        "    torch.save(x.squeeze(0), fullPath+'/' + str(i) + '.tensor')\n",
        "    i = i+1"
      ],
      "metadata": {
        "id": "iyShXBvXRLPK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainPath = '/content/drive/MyDrive/APS360 Team/aps360_dataset/FER_alexnet_big/train'\n",
        "valPath = '/content/drive/MyDrive/APS360 Team/aps360_dataset/FER_alexnet_big/valid'"
      ],
      "metadata": {
        "id": "OjZ_ISyvR__h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "82y8aOZASuCv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AlexNet"
      ],
      "metadata": {
        "id": "Sct7ff0TRh7r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# trainData, valData = getSmallerDataLoaders(32)\n",
        "trainData = torchvision.datasets.ImageFolder(trainPath, transform = data_transform)"
      ],
      "metadata": {
        "id": "89qQ0hVoTF3m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "save_feature(training_set, trainPath)\n",
        "save_feature(valid_set, valPath)"
      ],
      "metadata": {
        "id": "eXKlYr24TKuA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Modified from Tutorial 3a and lecture notes\n",
        "class CNNalex(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNNalex, self).__init__()\n",
        "        # nn.Conv2d(256,300,5,2)\n",
        "        self.conv1 = nn.Conv2d(256,300,3, padding = 2) # Modified to 3, in_channels, out_chanels, kernel_size\n",
        "        self.conv2 = nn.Conv2d(300,300,3)\n",
        "\n",
        "        self.pool = nn.MaxPool2d(2, 2) #kernel_size, stride \n",
        "        self.fc1 = nn.Linear(300*2*2, 32) # Modified first parameter\n",
        "        self.fc2 = nn.Linear(32, 7)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x))) # 4\n",
        "        x = (F.relu(self.conv2(x))) #2\n",
        "        x = x.view(-1, 300*2*2) # Modified size\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "clTqe1L2XYdd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tPath = '/content/drive/MyDrive/APS360 Team/Small AlexNet/train'\n",
        "vPath = '/content/drive/MyDrive/APS360 Team/Small AlexNet/valid'\n",
        "\n",
        "tData = torchvision.datasets.DatasetFolder(trainPath, loader=torch.load, extensions = ('.tensor'))\n",
        "vData = torchvision.datasets.DatasetFolder(valPath,loader=torch.load, extensions = ('.tensor'))"
      ],
      "metadata": {
        "id": "AUqv_LSqYM2k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# apr 2"
      ],
      "metadata": {
        "id": "LdHpaNyBN5Eg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "use_cuda = True\n",
        "model1 = CNNalex()\n",
        "\n",
        "if use_cuda and torch.cuda.is_available():\n",
        "  model1.cuda()\n",
        "  print('CUDA is available!  Training on GPU ...')\n",
        "else:\n",
        "  print('CUDA is not available.  Training on CPU ...')\n",
        "\n",
        "train2(model1, tData, vData, batch_size = 500, lr= 0.01, num_epochs=10)\n"
      ],
      "metadata": {
        "id": "StsTafJpRGPT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "49af4395-ffad-4020-8a55-6834b6eea7cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA is available!  Training on GPU ...\n",
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.2513149186666202\n",
            "Epoch:  0  Validation Accuracy:  0.2449150181108944\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.2513149186666202\n",
            "Epoch:  1  Validation Accuracy:  0.2449150181108944\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.2929743286077537\n",
            "Epoch:  2  Validation Accuracy:  0.28893842295904154\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.3191333728099202\n",
            "Epoch:  3  Validation Accuracy:  0.31958762886597936\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.37894040196454076\n",
            "Epoch:  4  Validation Accuracy:  0.3839509612705489\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.3591904977533178\n",
            "Epoch:  5  Validation Accuracy:  0.35775982167734743\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.4393744122052318\n",
            "Epoch:  6  Validation Accuracy:  0.4458066313736417\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.4056219304050995\n",
            "Epoch:  7  Validation Accuracy:  0.40679855112844804\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.4685638649900728\n",
            "Epoch:  8  Validation Accuracy:  0.46781833379771526\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.46926051064126234\n",
            "Epoch:  9  Validation Accuracy:  0.46921147952075787\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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weTfYtczphJdt76aV1NsxjiX52wZkcbgYKxDGmKztWOi+RLzZQI6dSkNVIbqUu0jkKw6fd4Xdy51OeelcLhKmDCaRSCrc+X9Op8mV/L5A+MsptNzCPs8AtPg9iCzCH0Xa02jkDzwx9U9S011QoAz0nQmRheGz22DPSqeTXpJ1s96hWvIa/rj6YUqVsXseMuPXBSIiIoJDhw7Zl1o2UVUOHTpERESE01FMTjmyHTbMgth7eXv+LoKChMlx8dz3aRwnktOgYIy7SIQXhPG3wt4/nU7slcTDu4lZ/ip/BNemxe12z8OF+HUH5zExMcTHx5Nbu+HIiyIiIoiJiXE6hskpSz+EoGA2VejBjz9s5pF21SgeHc4z09bQ44NFjOvbmBKFykPfGTCuI4zv4j71VKq208mztGPCUCprMnLLG4SG2D0PF+LXBSI0NJRKlSo5HcOYvCk5AVaMh5q38ubSk+QPD+Hu5hUpGBVKyYIRDJqwgtveW8gn9zamasmKGYpEZ7hnJpQ8r/PmXGFP3HSuPvQDc0vcS/v6jS++QgDz61NMxpgrsOoLSD7O7hp9mbV6L32aV6BgVCgArauX4Mv+zUlOc9Ft9EIWbz0ERSq7TzcFhbqLxIGNF9lBztPkE4R89yhbKUuju150Ok6uZwXCGHM+lwuWjIaYxry5oQBhwUH0a3n20XidmIJ880ALikeHc/fYpUz/Yw8Uvcp9igmBT2+Bg5ucyX8Bm796jhLp+9nc5CWKFQrccR68ZQXCGHO+Td/D4a0crnMfX6/YTa8m5SkeHX7eYuWKRDF1YAvqlyvEg1+s5INft6DFqrqLhCvdXSQObXHgDZzvxI4VVNr8CT+E30ibDnbPgzesQBhjzrdkNESX4Z197naE+6+rfMFFC0WFMf6+JnSsW5p/fbeB4dPXkl6surtIpKe4i8ThbTmVPHOudI59+QBHNT9lu48iOEiczZNHWIEwxpxt/zrYOo+T9fsxYdkeujYsS9lCkVmuEhEazNs9G3D/tZUYv2gHAz5fzqnC1eHubyE10V0kjuzIoTdwvj0/vE3ZxPXMq/gQNatUcCxHXmMFwhhztiXvQ0gkHyVeR2q6i4Gtqni1WlCQ8EzHmoy4pSY/rt9Prw8Xcyh/NXeRSD4On3aCo7suvqFsln40nsKLX2WR1KNdj8E5vv+8zAqEMeZvJw/Bn1+SXKs7H8Yd5eY6palULN8lbaJvy0qMvqsR6/cep+vohWwLreLu5O/UMXeROLb74hvJRnu+GAKudBLavEbBqLAc3XdeZwXCGPO35eMgLYmvgjtxIjmNQa29O3o4V4fapZh4fzOOn0ql2+iFrEivBH2+gcTD7iJxfG82B8/c8ZXfUG7/z3xToDftWjbNkX36EysQxhi39FRY9hHpFVvx31VCmxoluLr05V8K2qhCYb5+oCXRESH0GrOYucfKQu+pcOIvd5FI2JeN4TORdBzXrMdY7ypPkzufQ8Qapi+VFQhjjNu6byFhLz8U7MqRxFQG3XB5Rw8ZVSqWj6kDW1CjdAEGfL6cT3eVcA80dHwvfNrZXSx8ZN+0ZymQepBldYZTpXQRn+3Hn1mBMMa4LR6Nq0gVRqwrTfPKRWlYvnC2bLZY/nAm3d+MNjVKMnz6Wl5ZWwhXry/h6E53kTh5MFv2k1HazmWU2DCeb0I6cHuXW7N9+4HCCoQxxj3oz+44lpe6g30JqQzOhqOHjCLDgvmgTyP6NKvAmPlbeXBxPlJ6fAFHtrk7+EvMxiHn01M5NvkB/tJCFLplJFFhft3lnE9ZgTDGwOL30PACPLutDvXKFaLFVUWzfRfBQcKLXWrx5E01mPnnXnr/HMGJrp+5u+MY3znbisTxeW9S9MT/mFriQW6od1W2bDNQWYEwJtAd2w3rvmVzTFc2HlEGt67iswZdEWHA9VfxZs/6rNp5lFvnhHOg08fujv0+uw1OHb2yHRzZTsSC1/hJG9G55z+tYfoKWYEwJtAt+xBFeWF/S6qXjKZNjRI+32WX+mUZf18T/jqexM2zI9je9gPYv9Y9fGnSscvbqCqHJg8hxSXEN3uRckUv7f4Ncz4rEMYEspREWP4Jf5Vpy4KD+Xig9VUE5VA/Rc0qF2XqwBaEBQdx83dRrG75Nuz9Az7vBknHL3l7KX98RdG98/kkojc92zX3QeLAYwXCmED255dw6gj/d6INFYpG0bFO6RzdfdWS0Xz9QAsqFs3HrT8VZEGDUbB7BUzoDsknvN/QqSOkznqCP1yVadDtccJtlLhsYQXCmEClCkveJ6FwLSbtj2Hg9VcREpzzXwklC0QweUBzWlYpRu/fSzCj2kg0fhlMvANSTnq1jeMzniE85ShzKz1Fy2olfZw4cFiBMCZQbf0FDmxgvOsmShWI5LaGZR2Lkj88hLH3xHJHbAxD/qjAhLLPojsXwcQe7tNgWdDtv1Ng3QQ+52b6duucQ4kDgxUIYwLV4tGkRhTjzf116H9dZcdPy4QGB/HvbnUZ1rYqz26uzvuFH0W3L4BJvSD1VOYrpSVzYuoQ4rUYwW2eoUSBiJwN7eesQBgTiA5uhk3fMyv8ZvLny0fPJuWcTgS4L4Md1rYar91el//ua8DrUcPQrb/Cl70hNem85ZN//T+iE7YwJnoQvVrWcCCxf7MCYUwgWvI+rqAwRu5vzn3XVMp1dxvfEVuOj/s25uMTzfhXyEDY/CNMvhvSkv9e6OBmghf8l5npzbjtjnsdaT/xd/aJGhNoTh2FVRNZku8GksOL0rtZ7hxh7bpqxZk8oDnTpA0v0B82zYWv+kJaCqhy8ushJLpC+KP2UzTIpn6jzNmsQBgTaFZ+BqkneenQddzdogIFI0OdTnRBtcoU5JtBLVlQoBPD0++FjbNhyr24ln9Kvj0LeTuoNw90auF0TL9lBcKYQJKeBkvGsCWqPluDK9OvZSWnE11U2UKRTBnQgo3lejAi9W7YMJOgmUNZ5qpG1ZsHUzifjRLnK1YgjAkkG2fDsZ2MOnYDvZqUp2j+cKcTeaVgVCif9mvCkTr9eDG1D/u1EBOKP8ztjco7Hc2vXbRAiEj2d+tojHHG4tEcCSvNzzSi/3WVnU5zScJDgvm/O+oTfu1g2skH/PP2jjnWLUig8ubShcUisgoYB3ynqurjTMYYX9izCnYuZHR6H25rWJ7SBSOdTnTJgoKEJzrU4NEbqxNsxcHnvDnFVA0YA/QBNonIKyJSzZuNi0gHEdkoIptF5MkslusmIioisRnmPeVZb6OItPdmf8aYLCx5n+SgKL5Mu54B1+ftcRKsOOSMixYIdftBVXsB9wP3AEtF5FcRuWCXiSISDLwL3ATUBHqJSM1MlosGhgJLMsyrCfQEagEdgPc82zPGXI6E/ejqKXyVfh3X161CxWLWFba5OK/aIERkqIjEAY8CQ4BiwCPAxCxWbQJsVtWtqpoCTAK6ZLLcS8C/gYy3SXYBJqlqsqpuAzZ7tmeMuRxxH4MrjY9S2vFA67x99GByjjenmBYBBYBbVbWjqn6tqmmqGge8n8V6ZYFdGabjPfPOEJGGQDlVnXWp63rW7y8icSISd+DAAS/eijEBKDUJ17KxzKchVWrUp0apAk4nMnmEN43U1S/UMK2q/77cHYtIEPA60Pdyt6GqY3C3jxAbG2uN58ZkZs1UghIP8EHK/TxmRw/mEnhzBPG9iBQ6PSEihUVkrhfr7QYy9gAW45l3WjRQG5gnItuBZsB0T0P1xdY1xnhDFdfi0WymPFLpOuuSwlwSbwpEcVU9M5K4qh4BvBm0dhlQVUQqiUgY7kbn6Rm2c0xVi6lqRVWtCCwGOntOXU0HeopIuIhUAqoCS71+V8YYtx2/E7R/NR+mtmfQDVWdTmPyGG9OMaWLSHlV3QkgIhWAi57OUdU0ERkMzAWCgY9Vda2IvAjEqer0LNZdKyKTgXVAGjBIVdO9yGqMycC1aDTHiWZ7mZtpXtnueTWXxpsC8QywQER+BQS4FujvzcZVdTYw+5x5z19g2VbnTL8MvOzNfowxmTi8Ddk4i8/TOtP/hlqI2L0D5tJctECo6hzP1UbNPLOGqepB38YyxlwpXTKGdIJYVOQ2BtXw5qywMWfzdpSQdOAvIAKoKSKo6nzfxTLGXJHkBNKWj2d2elN6tmlqRw/msly0QIjIP3Df6RwDrMJ9JLEIuMG30Ywxl0tXTiA07QRz8t/GO3VKOx3H5FHeXMU0FGgM7FDV1kAD4GjWqxhjHONycWrBe6xwVaH1DTdZv0XmsnlTIJJUNQlARMJVdQNQ3bexjDGXbdP3RJ3YwTdhnbm1wXkdEBjjNW/aIOI9N8pNA34QkSPADt/GMsZcrmPz3uKkFqHK9b0IC7Exwczl8+Yqpts8T0eIyC9AQWCOT1MZYy7P/nUU3Ps744Pu4h9NrVsNc2WyLBCeLrbXqmoNAFX9NUdSGZNbHNoCwaFQKG8MbXno5zeJ0jCiWvYjMsx6yDdXJsvjT8/dyxtFJG/86zAmOyUehrHt4J3GsHg0uFxOJ8rayUNE/+9rZsp1dL+2ntNpjB/wpg2iMLBWRJYCJ0/PVNXOPktlTG7w80g4dRQqtoQ5T8KGWXDre7n2aOLgrx9QTFM4Uf8fFIgIdTqO8QPeFIjnfJ7CmNxmzyr3IDtN/wkdXoWVn8Ocp+C9FtDhFWjQB3LTzWfpqYSuGMsCrUuXdm2cTmP8hDeN1NbuYAKLywWzH4N8xaDVU+5C0LAPVLoOvh0E04fA+pnQ+S2ILuV0WgAOLp1MsbSD7Kr6FNfkC3M6jvET3gw5miAixz2PJBFJF5HjORHOGEf88QXEL0XbjmDItG3c8/FSVuw8AoUrwN3TocO/Yduv8F4zWDPV6bQAnPrtbbZqaVp3utPpKMaPXLRAqGq0qhZQ1QJAJNANeM/nyYxxwqmj8ONwiGnC5NRrmPHHHpZtP0zX9xbS75NlrNmbAM0GwIAFUKQyTOkHX93rbtB2yOENCyiXuJ7VZXtSqlCUYzmM/7mku2jUbRrQ3kd5jHHWvH/ByYMcvv5lXp69kaaVirD0mbY81r46y3ccodPbCxj4+XL+l14K+n0PNzwL62e4jyb+581Ai9lv7/f/x3GNouEtgxzZv/Ff3nTW1zXDZBAQCyT5LJExTtm3BpaOgdh+PLs0mKQ0F//qWof84SEMal2FPs0rMPa3bYxdsI05a/fRuV4ZhrV9gEpV28M3A2DiHe7G6/avQESBHIl8dN92qh/6mflFu3ND6eI5sk8TOLw5grglw6M9kAB08WUoY3KcqrthOqIQP5ftz+zV+xjapiqVi+c/s0iBiFAealeN3x5vzT+vu4rv1+6n7eu/8vjvyq7bZ8E1D8OqCTC6JWzLmd7w/zfjdQSl4s0P5cj+TGAR1YuOHponxMbGalxcnNMxTF7152T4+n4S279Oq58rUDR/ONMHtyQ0+MK/oQ4kJDN63hY+X7IDVaVH43I8VP0oRX8YCoe3QNOB0HY4hEb6JHJCwjFc/72azfka0uixmT7Zh/F/IrJcVWMze82bq5g+9XTWd3q6sIh8nJ0BjXFU0nH4/lko05CX98Zy8EQy/+5WJ8viAFA8Opznb6nJr4+1okfjckxauovmE07waoUPOdXgPlgyGt6/FuJ988Nl+YwPKMhJCrZ60CfbN8abU0x1VfXM+A+qegT3mBDG+Idf/w0n/mJN/eeYsDSe+66pRN2YQhdfz6N0wUhG3lqHXx5tRZd6ZRizeB8N425k0tXv4EpJdHfX8dNLkJaSbZGTUtIo979P2RZahSqx7bJtu8Zk5E2BCBKRwqcnRKQI3g9Vakzu9tcGWPI+afV78+D8IMoVieShdtUua1PlikTxn+71+PHh67mxVkmeWlWElsdfZm3xjvDbKPjoBti/Nltiz5/7FVcRT3qTAbnrjm7jV7wpEP8FFonISyLyErAQeM23sYzJAaow+1EIy8/7Ib3ZevAk/7qtLlFhV/b7p3Lx/LzZswFzhl5HvSrl6bizFw/yOImH9qAfXA+/vQ6u9Mvefkqai/wrx3AkqDBXtepzRVmNyYo3N8qNB7oC+z2Prqr6ma+DGeNza7+B7b+xN/Yx3lh4mNsbxXBN1WLZtvnqpaJ5v08jZg65hhMVb6Rlwsv8pI3gpxdwjXroFnEAABtCSURBVG3v7kr8Mvy0YAEtXCs4WrMPEhqRbXmNOZc3jdTNgF2q+o6qvoN7hLmmvo9mjA8ln4C5z6Cl6jJwfR0KRYXyzM1X+2RXtcsW5OO+jfloYAfGlRnBgymDSdi9nrR3W5C26P1L6kY83aUk/z6aVEKo2H6wT/Iac5o3p5hGAycyTJ/wzDMm75r/H0jYw4yYh1m1+wTDb6lFYR93cteoQmEm3N+cXvc9zKPF3mdBanVC5j7B/vduIu2wd6P4/rBiI+1SfmJf+VuQ6JI+zWuMNwVCNMPNEqrqwhqpTV52cBMsepcTV/fg8SURtL26BJ3qls6x3Te/qihjBnVCek/hnfxDyHdgFUlvNWPFt++Qnn7howlVZddPH5BPkinTwW6MM77nTYHYKiIPikio5zEU2OrrYMb4hCp89zgaGsnjR7sSEhTES7fWRnL4SiAR4frqJRj0yEus7DiLrcGVaLjyGZb8qz0/x60msxtY563fw02J0/mrSCzBZWzEOON73hSIAUALYDcQDzQF7vdlKGN8Zv0M2PIzf1QZxOxt6TzRoTqlC/rmTmdviAjXNoml9tO/sbbOE8SmraTejJt45b//5ucN+88UClVl2ZzPiZGDFGkzzLG8JrBcclcbIhIJdFLVr3wT6fJYVxvmolIS4d0mpIVF0+TA81QuWZDJ/2xOUFDuuY8gff8Gjn/Rj8JH1zItvQVTSg5lQPtYgoIg+NOO1MyXQPRjqyEo2Omoxk9k1dWGV20JIhKMu6O+XkA7YAGQqwqEMRe14HU4tot3yr3FiVR4tVudXFUcAIJL1qDwkF9Jn/86nee/RsuDA3ls3D84EVKYKUEbSG050oqDyTFZFggRuR64E7gZWAq0BCqramIOZDMm+xzaAr+/yd4KnXljYzEebleFKiWinU6VueBQgls/AdXbU/Sbf/LJgdfYL8VIDYoiNPZup9OZAHLBNggRiQf+hftooaaqdgNOWXEwedKcp9DgMO7f25nqJaMZcP1VTie6uDL1CfrnfGg5lJJ6iNDGfSGioNOpTADJ6ghiCnAr0ANIF5FvAf/oG9wElo3fwaa5zC07hLVbo/i6dx3CQi5pMEXnhIRDuxehSX/Ib/c9mJx1wX8lqjoMqIS7L6ZWwEaguIjcISL5L7SeMblK6in47glOFarKkK2N6duiIg3KF774erlNwRgIDnU6hQkwWf6M8oxB/Yuq9sddLHrhHk1uew5kM+bK/f4WHN3Bs8l3U6JgNI/eWN3pRMbkGV4fZ6tqqqrOVNW7gHLerCMiHURko4hsFpEnM3l9gIisFpFVIrJARGp65lcUkVOe+atE5H2v35Expx3ZDgteZ0Oxdkw9chUv31abfOHWCYAx3rqsfy2qeupiy3gujX0X92Wx8cAyEZmuqusyLDZRVd/3LN8ZeB3o4Hlti6rWv5x8xgAw52lcBHH/3i7c1qAsraqXcDqRMXmKL1vqmgCbVXWrqqYAk3CfnjpDVY9nmMyHNYKb7LLpB9g4i8/Ce3AyohTPdarpdCJj8hxfFoiywK4M0/GeeWcRkUEisgX3IEQZB9etJCIrReRXEbk2sx2ISH8RiRORuAMHDmRndpOXpSXDd49zLKoCIw+1ZvgtNSni455ajfFHFz3FJCIzOP+X/TEgDvhAVZOuJICqvgu8KyJ3As8C9wB7gfKqekhEGgHTRKTWOUccqOoYYAy4u9q4khzGjyx8Gw5v5RHX07SsXprO9co4nciYPMmr3lxxjwHxoedxHEgAqnmmL2Q3Zzdmx3jmXcgk3PddoKrJqnrI83w5sMWzP2OydnQXOn8UyyKvYSH1GOlAT63G+AtvGqlbqGrjDNMzRGSZqjYWkaxGYF8GVBWRSrgLQ0/c3XacISJVVXWTZ7IjsMkzvzhwWFXTRaQyUBXrYtx44/tnSFdl2JHuPHZLdWIKRzmdyJg8y5sCkV9EyqvqTgARKQ+cvlEu5UIrqWqaiAwG5gLBwMequlZEXgTiVHU6MFhE2gKpwBHcp5cArgNeFJFUwAUMUNXDl/H+TCDZ8gus+5YPpCclylfl7uYVnU5kTJ7mTYF4BFjgaUgW3DfMPSAi+YBPs1pRVWcDs8+Z93yG50MvsN5UYKoX2YxxS0uB7x7nQGhZ3ku8iW+61SU4l/XUakxec9ECoaqzRaQqUMMza2OGhuk3fJbMmEuxZDQc/B+PpTzGfa1rUq1kLu2p1Zg8xNsb5RoBFT3L1xMRVHW8z1IZcymO70Hn/ZvfgxoTX+xaPmidB3pqNSYP8OYy18+Aq4BVQLpntgJWIEzu8P1zpKel8nTSnfzfPXUID7EBdYzJDt4cQcTiHg/C7jMwuc+232DNFN5J60qrZk1oVKGI04mM8RveFIg1QCncN68Zk3ukp+Ka/Sh/SQmmRXVnRnvrqdWY7ORNgSgGrBORpUDy6Zmq2tlnqYzxxtIPCTqwgedSHua5OxoRHWHjJRiTnbwpECN8HcKYS5awn/RfXmGBqz4RtW+hzdU22pox2c2by1x/zYkgxlwK/eE5XCmn+G9wPz7uXMvpOMb4pQv2xSQiCzx/E0TkeIZHgogcv9B6xvjcjkXIn1/yQVpH7unUhmL5w51OZIxfuuARhKpe4/lrdxyZ3CM9jdQZj3BQi7KqQj8GNTyvB3ljTDbx6kY5z+hwJTMuf7pvJmNyksaNJfTgWl51PcTwbk2sp1ZjfMibG+WGAMOB/bg7zgP3jXJ1fZjLmPOdOEDqjyNZkl6bOu36UK6I9dRqjC95cwQxFKh+enwGY5ySPPd5glNP8kXRwbx9TWWn4xjj97wZMGgX7hHkjHHOrmWEr57Ix+k3MfiOjtZTqzE5wJsjiK3APBGZxdk3yr3us1TGZORKJ+GbYZzUwiQ2f5iaZQo4nciYgOBNgdjpeYR5HsbkqJSl44g+vIY3Ih7hsXb1nY5jTMDw5ka5F3IiiDGZSjxM2g8vsMJ1NTfe8QARodZTqzE55YIFQkTeUNVhIjID91VLZ7G+mExOODj9WQqlnWBJjacYelUxp+MYE1CyOoL4zPN3VE4EMeZcabuWU2TDRCYFd+Terjc7HceYgJPVndTLPX+tLyaT81wuDk5+kGAtQKnOIyhgPbUak+MuepmriFQVkSkisk5Etp5+5EQ4E7gO/vw2pRLWMKvUQG6oX9XpOMYEJG/ugxgHjAbSgNa4hxr93JehTABLT2PnFw9TbMHzLKA+N981zOlExgQsbwpEpKr+BIiq7lDVEUBH38YygejEkf1s/r8bKb9xLNPCOlK43xRKFIh0OpYxAcub+yCSRSQI2CQig4HdQH7fxjKB5s9l8yk+ux/lXEeZedWz3HTnw4SH2CWtxjjJ276YooAHgZdwn2a6x5ehTOBISk1n9sS3uGnrKyQERbP1lq/oFNva6VjGGC5SIDzdfPdQ1UeBE8C9OZLKBIRVOw6y6fOH6J46ne3R9Slx3yRKFC7tdCxjjEdWN8qFqGqaiFyTk4GM/0tJc/HR3KU0WPIQ3YPWsaf63VS843UItktZjclNsjqCWAo0BFaKyHTgK+Dk6RdV9WsfZzN+aMO+47w7YSpPHB9JyeDjnLr5Hco07uN0LGNMJrxpg4gADgE34O5yQzx/rUAYr6W7lA/mb2Hrj2P5T8hHaL6ihPb+ntAyDZyOZoy5gKwKRAkReRhYw9+F4bTz+mYy5kK2HjjB45OXc/Pe9xgVMofUci0I7TEe8hd3OpoxJgtZFYhg3JezZjYyixUIc1EulzJ+0XbGzFnCm8Fv0ThkLdp0IKE3vmTtDcbkAVkViL2q+mKOJTF+Jf5IIo9P+ZOErcuYEfUmRUiAzh8g9Xo6Hc0Y46WsCoSN6Wgumary1fJ4Xpyxjlt0HiMjPyIofwmkxxQoY4P9GJOXZFUg2uRYCuMX/kpI4qmpq/l1wx7eLjKFmxKnQ/lrofsnkM/GcjAmr8mqu+/DORnE5G0z/9zDs9PWEJlymN9KjaH00eXQbBC0exGCvblYzhiT23jTWd9lE5EOIrJRRDaLyJOZvD5ARFaLyCoRWSAiNTO89pRnvY0i0t6XOc3lO3IyhSFfrGTwxJW0KbCb+QVHUPrEWuj6IXR4xYqDMXmYz/71errpeBdoB8QDy0Rkuqquy7DYRFV937N8Z+B1oIOnUPQEagFlgB9FpJqqpvsqr7l0P2/YzxNTV3PkZAof1d1Imy2vIvlLQu/voXQ9p+MZY66QL3/eNQE2q+pWABGZBHQBzhQIVT2eYfl8/H35bBdgkqomA9tEZLNne4t8mNd4KSEplZEz1/Nl3C5qlYxkTtU5FF33KVS6Dm7/BPIVdTqiMSYb+LJAlAV2ZZiOB5qeu5CIDAIeBsJw3619et3F56xbNpN1+wP9AcqXL58toU3WFm45yGNf/cneY6d4tEUhHjjwEkHrFkHzwdD2BTulZIwf8WkbhDdU9V1VvQp4Anj2Etcdo6qxqhpbvLjdletLp1LSeWHGWu78cAlhIUHM6hbF4E3/IGjvKug2Ftq/bMXBGD/jy3/Ru4FyGaZjPPMuZBLuoU0vZ13jQyt2HuHRyX+w9eBJ+raoyFOl4wj/7lGILgX3fQ+l6zod0RjjA748glgGVBWRSiIShrvReXrGBUQk42j0HYFNnufTgZ4iEi4ilYCquHuXNTkoJc3Ff+Zu4PbRC0lOc/HFvQ0YETSW8FkPQoUW0P9XKw7G+DGfHUF4xpIYDMzF3a/Tx6q6VkReBOJUdTowWETaAqnAETwj1XmWm4y7QTsNGGRXMOWsdXuO8/DkVWzYl0D3RjE837oo0d/eC7sWQ4sHoc1wO6VkjJ8TVf/ody82Nlbj4uKcjpHnpaW7+GD+Vt748X8UjAzj1a51aBu9Eyb3gVNHocs7UOd2p2MaY7KJiCxX1djMXrOfgOYMl0t55Ks/+HbVHjrWLc3ILrUpvOELmPooRJeGf/wApeo4HdMYk0OsQBjA3cneCzPW8u2qPTzWvjqDri0Pc56AuI+hcmu4/WOIKuJ0TGNMDrICYQB486dNfLpoB/2vq8wDjfLBp51g1xJoOQzaPA9BwU5HNMbkMCsQhvGLtvPGj5vo3iiGpxqkIGNaQfJxuH0c1O7qdDxjjEOsQAS4b1ftZvj0tbSrWZJ/3VAQGXcjBIXCfT9AqdpOxzPGOMgKRACbt/EvHpn8B00qFuHt2yoTMv4mSE2C+76FElc7Hc8Y4zArEAFq+Y7DDPh8OdVLRfNR77pEfNUDDm2BPl9bcTDGAFYgAtKGfce5d9wySheM5NN7GxM9dyhs/w1u+8DdI6sxxpALOuszOWvX4UTuHruUyLBgxvdrQrFl/4U/J0HrZ6BeT6fjGWNyETuCCCAHEpLpPXYJyWkuvhrQnHLbp8L816BBb7juMafjGWNyGTuCCBDHk1K55+Ol/HU8mXH3NqZawlKYOQyuugE6vQEiTkc0xuQyViACQFJqOv/4NI5NfyXwfp9GNAyLh8n3QPEa0P1TCA51OqIxJheyAuHn0tJdDJ64gmXbD/P6HfW5vmQKTLgDwqPhzskQUcDpiMaYXMraIPyYy6U8MXU1P67/i5durc0t1fPDuJsgOQH6fQcFzxvF1RhjzrAC4adUlZdnr2fqingebleNPo3LwMQ74K/1cNdX1iurMeairED4qffmbWHsgm30bVGRIa2vghkPwpafofM7UKWN0/GMMXmAtUH4oYlLdvKfuRu5tX4Znu9UE/ntv7DyM/elrA37OB3PGJNHWIHwM7NX7+WZaau5oUYJ/tO9HkGrJ8MvI6FuD/fNcMYY4yUrEH5kwaaDDJu0ikblC/PunQ0J3bkAvh0EFa91n1qyex2MMZfACoSfWLXrKP0/i6Ny8XyMvacxkUc3waTeUPQq6PEZhIQ5HdEYk8dYgfADm/9K4N5xSymaP4zx/ZpQMP0wTOgOoRHuK5YiCzsd0RiTB1mByON2Hz1Fn7FLCQ4K4vP7mlIiPA0mdofEg3Dnl1CovNMRjTF5lF3mmocdOpFMn7FLOJGcxpf9m1OhUDhMuhP2rYaeX0CZBk5HNMbkYVYg8qgTyWn0HbeM3UdO8fk/mlKzdDTMegQ2zYWO/4XqHZyOaIzJ4+wUUx6UlJpO//FxrNt7nNG9G9K4YhFY+BbEjYWWQ6HxP5yOaIzxA1Yg8ph0lzJs0ioWbjnEf26vyw01SsKar+GH56FWV2gzwumIxhg/YQUiD1FVnvlmNXPW7uP5TjXp2jAGdi6GbwZA+eZw62gIsv+kxpjsYd8mechrczcyadkuhtxQhX7XVIKDm+GLnlCoHPSc6L6s1RhjsokViDxizPwtjJ63hbualufhdtXgxAGY0A0k2H2vQ1QRpyMaY/yMXcWUB0yO28UrszfQsW5pXuxSG0k95T5ySNgH98yEIpWdjmiM8UNWIHK5uWv38eTUP7m2ajH+7476BOOCr++H3cvdXWiUa+x0RGOMn7ICkYst2nKIIV+spE5MId7v3YiwkCCY8xRsmAkdXoWrb3E6ojHGj1kbRC61Zvcx7h8fR/kiUXzStzH5wkNg8fuw+D1oOhCaDXQ6ojHGz1mByIW2HjjBPR8vpWBkKJ/d14TC+cJg/UyY8yTU6ATtX3Y6ojEmAFiByGX2HUuiz9ilAHx2XxNKF4yE+DiYeh+UbQRdP4SgYIdTGmMCgU8LhIh0EJGNIrJZRJ7M5PWHRWSdiPwpIj+JSIUMr6WLyCrPY7ovc+YWRxNT6DN2CcdOpfLJvU2oXDw/HN4KE3tAdCnoNQnCopyOaYwJED5rpBaRYOBdoB0QDywTkemqui7DYiuBWFVNFJGBwGtAD89rp1S1vq/ynXYqJZ2Pf9/m6914Ze7afew4lMgn/RpTJ6YgJHrGddB0uGsq5C/udERjTADx5VVMTYDNqroVQEQmAV2AMwVCVX/JsPxioLcP82QqMSWN/8zdmNO7zVRkaDBv9WpAi6uKQWqSu+vuo7vg7m+hWBWn4xljAowvC0RZYFeG6XigaRbL3wd8l2E6QkTigDTgVVWddu4KItIf6A9QvvzlDYxTJF8YG0fmjq6xg0UICQ4ClwumDYSdi+D2j6FCc6ejGWMCUK64D0JEegOxwPUZZldQ1d0iUhn4WURWq+qWjOup6hhgDEBsbKxe5r4JD8lljb4/vQBrv4a2L0Dtbk6nMcYEKF82Uu8GymWYjvHMO4uItAWeATqravLp+aq62/N3KzAPCIzh0ZaNhd/fgNj73GM7GGOMQ3xZIJYBVUWkkoiEAT2Bs65GEpEGwAe4i8NfGeYXFpFwz/NiQEsytF34rf/NhdmPQtX2cNNrIOJ0ImNMAPPZKSZVTRORwcBcIBj4WFXXisiLQJyqTgf+A+QHvhL3l+FOVe0MXA18ICIu3EXs1XOufso+iYdh3E0+2fQlO7IdStVxtzsE54qzf8aYAObTbyFVnQ3MPmfe8xmet73AeguBOr7MdkZQMBSvniO7uqiYxnDDsxCe3+kkxhiTOxqpHRVREO4Y73QKY4zJdayrDWOMMZmyAmGMMSZTViCMMcZkygqEMcaYTFmBMMYYkykrEMYYYzJlBcIYY0ymrEAYY4zJlKheVieouY6IHAB2XMEmigEHsylOXmefxdns8zibfR5/84fPooKqZjoamd8UiCslInGqGut0jtzAPouz2edxNvs8/ubvn4WdYjLGGJMpKxDGGGMyZQXib2OcDpCL2GdxNvs8zmafx9/8+rOwNghjjDGZsiMIY4wxmbICYYwxJlMBXyBEpIOIbBSRzSLypNN5nCQi5UTkFxFZJyJrRWSo05mcJiLBIrJSRGY6ncVpIlJIRKaIyAYRWS8izZ3O5CQRecjz72SNiHwhIhFOZ8puAV0gRCQYeBe4CagJ9BKRms6mclQa8Iiq1gSaAYMC/PMAGAqsdzpELvEmMEdVawD1CODPRUTKAg8CsapaGwgGejqbKvsFdIEAmgCbVXWrqqYAk4AuDmdyjKruVdUVnucJuL8AyjqbyjkiEgN0BD5yOovTRKQgcB0wFkBVU1T1qLOpHBcCRIpICBAF7HE4T7YL9AJRFtiVYTqeAP5CzEhEKgINgCXOJnHUG8DjgMvpILlAJeAAMM5zyu0jEcnndCinqOpuYBSwE9gLHFPV751Nlf0CvUCYTIhIfmAqMExVjzudxwki0gn4S1WXO50llwgBGgKjVbUBcBII2DY7ESmM+2xDJaAMkE9EejubKvsFeoHYDZTLMB3jmRewRCQUd3GYoKpfO53HQS2BziKyHfepxxtE5HNnIzkqHohX1dNHlFNwF4xA1RbYpqoHVDUV+Bpo4XCmbBfoBWIZUFVEKolIGO5GpukOZ3KMiAjuc8zrVfV1p/M4SVWfUtUYVa2I+/+Ln1XV734hektV9wG7RKS6Z1YbYJ2DkZy2E2gmIlGefzdt8MNG+xCnAzhJVdNEZDAwF/dVCB+r6lqHYzmpJdAHWC0iqzzznlbV2Q5mMrnHEGCC58fUVuBeh/M4RlWXiMgUYAXuq/9W4ofdblhXG8YYYzIV6KeYjDHGXIAVCGOMMZmyAmGMMSZTViCMMcZkygqEMcaYTFmBMMZDRE54/lYUkTuzedtPnzO9MDu3b4wvWIEw5nwVgUsqEJ4O27JyVoFQVb+769b4HysQxpzvVeBaEVnl6fM/WET+IyLLRORPEfkngIi0EpHfRGQ6nruKRWSaiCz3jBPQ3zPvVdy9fq4SkQmeeaePVsSz7TUislpEemTY9rwM4y9M8Nyxi4i86hmz408RGZXjn44JGAF9J7UxF/Ak8KiqdgLwfNEfU9XGIhIO/C4ip3vubAjUVtVtnul+qnpYRCKBZSIyVVWfFJHBqlo/k311BerjHl+hmGed+Z7XGgC1cHcj/TvQUkTWA7cBNVRVRaRQtr97YzzsCMKYi7sRuNvT/cgSoChQ1fPa0gzFAeBBEfkDWIy7I8iqZO0a4AtVTVfV/cCvQOMM245XVRewCvepr2NAEjBWRLoCiVf87oy5ACsQxlycAENUtb7nUSlD3/8nzywk0gp3L5/NVbUe7v55rmQYyuQMz9OBEFVNwz3Q1RSgEzDnCrZvTJasQBhzvgQgOsP0XGCgpyt0RKTaBQbLKQgcUdVEEamBe9jW01JPr3+O34AennaO4rhHbVt6oWCesToKejpQfAj3qSljfMLaIIw5359AuudU0Se4x2KuCKzwNBQfAG7NZL05wABPO8FG3KeZThsD/CkiK1T1rgzzvwGaA38ACjyuqvs8BSYz0cC3IhKB+8jm4ct7i8ZcnPXmaowxJlN2iskYY0ymrEAYY4zJlBUIY4wxmbICYYwxJlNWIIwxxmTKCoQxxphMWYEwxhiTqf8HOcSIZQrfF7cAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.46926051064126234\n",
            "Final Validation Accuracy: 0.46921147952075787\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(model1, tData, vData, batch_size = 500, lr= 0.005, num_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wNJ6kh1heP9W",
        "outputId": "a0a4b7a0-8789-4389-bb78-1c1fa4302461"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.48671148420355986\n",
            "Epoch:  0  Validation Accuracy:  0.48537196990805237\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.4928419659340277\n",
            "Epoch:  1  Validation Accuracy:  0.48927277793257173\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.49928593820753076\n",
            "Epoch:  2  Validation Accuracy:  0.49233769852326553\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.49381726984569296\n",
            "Epoch:  3  Validation Accuracy:  0.48704374477570356\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.5074018600438887\n",
            "Epoch:  4  Validation Accuracy:  0.5023683477291725\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.5074366923264482\n",
            "Epoch:  5  Validation Accuracy:  0.5004179437169128\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.5171897314431014\n",
            "Epoch:  6  Validation Accuracy:  0.5126776260796879\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.5023163467902052\n",
            "Epoch:  7  Validation Accuracy:  0.5037614934522151\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.5176077188338152\n",
            "Epoch:  8  Validation Accuracy:  0.5146280300919476\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.5263157894736842\n",
            "Epoch:  9  Validation Accuracy:  0.513234884368905\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.5263157894736842\n",
            "Final Validation Accuracy: 0.513234884368905\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(model1, tData, vData, batch_size = 500, lr= 0.005, num_epochs=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BsQH88RBYtsI",
        "outputId": "ed5940c8-1e1e-4db1-bccf-7920c74d4e96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.528719216970288\n",
            "Epoch:  0  Validation Accuracy:  0.5188074672610755\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.5237730328468424\n",
            "Epoch:  1  Validation Accuracy:  0.51797157982725\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.5157267755756034\n",
            "Epoch:  2  Validation Accuracy:  0.5043187517414321\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.5373576230450381\n",
            "Epoch:  3  Validation Accuracy:  0.5224296461409863\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.5395520568462852\n",
            "Epoch:  4  Validation Accuracy:  0.5215937587071607\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.544080253579017\n",
            "Epoch:  5  Validation Accuracy:  0.5338534410699359\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.5466578424884183\n",
            "Epoch:  6  Validation Accuracy:  0.5358038450821956\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.5504893935699606\n",
            "Epoch:  7  Validation Accuracy:  0.5355252159375871\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.5497927479187711\n",
            "Epoch:  8  Validation Accuracy:  0.5413764279743661\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.5475983141175241\n",
            "Epoch:  9  Validation Accuracy:  0.5288381164669824\n",
            "Epoch 10\n",
            "Epoch:  10  Train Accuracy:  0.5513602006339475\n",
            "Epoch:  10  Validation Accuracy:  0.5327389244915018\n",
            "Epoch 11\n",
            "Epoch:  11  Train Accuracy:  0.5509770455257933\n",
            "Epoch:  11  Validation Accuracy:  0.5475062691557537\n",
            "Epoch 12\n",
            "Epoch:  12  Train Accuracy:  0.5481904629210352\n",
            "Epoch:  12  Validation Accuracy:  0.5321816662022848\n",
            "Epoch 13\n",
            "Epoch:  13  Train Accuracy:  0.5651537845275001\n",
            "Epoch:  13  Validation Accuracy:  0.546113123432711\n",
            "Epoch 14\n",
            "Epoch:  14  Train Accuracy:  0.560834581490125\n",
            "Epoch:  14  Validation Accuracy:  0.5449986068542769\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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qMP27KnWhuz6OZUtCOmufuNwpJbmdSct9K6WUkyRl5rJ8dzLje0VWu+RQHu/6bZRSysW+jE2gqNgwqbd3zH0oyesThLc0oXkK/XsqdV5xseHz2AT6twmjVcPa7g7H4bw6QQQFBZGamqofag5ijCE1NZWgINeUEFDK063ef5KEtByvmTl9MXfPpHaqyMhIEhMT8dQyHNVRUFAQkZHeM85bqaqYuz6BesH+XNWlibtDcQqvThD+/v60bt3a3WEopbxQ6uk8lsSf4LZ+rQjy/02pOK/g1U1MSinlLPM2JVJQZJjspc1LoAlCKaUqzBjD3A0J9GpZn3aNQ9wdjtNoglBKqQpafzCNAylnvHJoa0maIJRSqoLmbkggJNCPa7pFuDsUp9IEoZRSFZCRXcDC7ccZ27MpwQFePc5HE4RSSlXEN5sTySssZlLvFu4Oxek0QSillJ3Odk5HNatL12Z13R2O02mCUEopO21JSGfXiSyvnTl9MU0QSillp7nrE6jl78uY7k3dHYpLaIJQSik7nM4rZMG2Y1zXPYKQIH93h+MSmiCUUsoO87ccIzu/iEl9vL9z+ixNEEopZYe5G47QoXEIPZvXc3coLqMJQimlyhF3LINtiRlM6tMcEXF3OC6jCUIppcoxd30CAX4+3NCzmbtDcSnvngaolHK73IIiPlx9iB1HM6gT6EdIkB8hQf7UCbKehwb5USfQn5AgvxLb/An08/GIb+s5+UV8u+Uoo7s2oV5wgLvDcSlNEEoppyguNszfeoyXftjFsYxcWoYFk1tQRFZuIdn5ReUe7+cj55PJucRivQ4J8qNOoB+htfzp07oBPZvXc1oy+X77cbJyC2tU5/RZTk0QInI18G/AF5hljPnnRe9PBV4Gjto2vWmMmWV7rwjYbtt+xBgzxpmxKqUcZ8OhNF74Lp6tiRlENavLvyb2oG+bsHPvFxYVcyaviMzcArJyCzmdV0iW7XlWieency/cfjQ9l6zcLNv+hRQVW8sJd20Wym39WjKmezNqBTh28Z6564/QumFt+rZu4NDzVgdOSxAi4gvMBEYCicAGEZlvjIm/aNfPjTH3l3KKHGNMD2fFp5QrbE1IZ8aS3aRnF9A4NIgmdQNpHBJE47pBNAkNsraFBhFay88jmlOq6khqNv/8YScLt5+gSWgQr97Unet7NMPH58Lfzc/Xh7rBPtQNrvx8AmMMmTmFzN92jNlrD/Hnedv5+8JdTOgVyS39WtK6Ye0q/jawNymL2MOneHxUR6/496koZ95B9AH2GWMOAIjIXGAscHGCUMrrnMjI5aXFu/h601Ea1gmkS9NQEk9ls/FwGqeyC36zf5C/D41tCcNKGoHnn9uSSXhIoMcubZmRU8DM5fv4aPUhfH2Eh69oz91DWju12qmIUDfYn9v6teTWvi1YfzCN2b8e5qM1h5i16iBD2odzW7+WXN6xEb4+lftwn7shAT8f4cbomrkOuzMTRDMgocTrRKBvKfvdKCJDgD3Aw8aYs8cEiUgsUAj80xjz7cUHisg9wD0ALVrUvPZB5XlyC4p4b8UB3vp5P0XFhmnDLuMPw9tSJ9Dvgn2SM/NIysrlREYuSZnW40RmHkmZuWxLTGdJRi55hcW/OX/9YP9ziaNpvSAGtm3I8A6NqB3onu7EgqJi5qw/wr+W7iE9p4Dx0ZE8elUHGocGuTQOEaFvmzD6tgkjOTOXOesT+Gz9Ye7+JJZm9Wpxc98WTOrdnLA6gXafM6+wiK83JTKyc2PCQ+w/zpuIMcY5JxYZD1xtjLnL9vo2oG/J5iQRCQNOG2PyROReYKIx5nLbe82MMUdFpA3wEzDCGLP/UteLiYkxsbGxTvldlCqPMYbvth3nn4t2cTQ9h1Fdm/DEqE60CAuu9Pkycwo5kZnLCVsCSco4+9xKJEfSssnIKSDQz4dhHcIZHRXB5R0buaQMhDGG5buTefH7nexPOUP/NmH89dpOdGnqORVOC4qKWRafxCdrD7P2QCoBvj5c0y2CW/u1JLpF+Z3a87ceY/qczXx8Rx+Gtg93UdSuJyIbjTExpb3nzK8dR4GSJQ8jOd8ZDYAxJrXEy1nASyXeO2r7eUBEfgZ6ApdMEEq5y/bEDJ77Lo4Nh07RKSKUGRO60/+ysPIPLMPZ5pO6wf50aFL6msdFxYbYQ2ks2nGCRTuOszguiQBfH4a0b8iorhFc0bkxdWs5PlnsPJ7Ji9/vZNW+k7RpWJtZt8cwolMjj2uj9/f1YVRUBKOiItiXnMXstYeZt+ko32w+Spemodzev+xO7bnrj9CsXi0Gt23o4sg9hzPvIPywmo1GYCWGDcDNxpi4EvtEGGOO257fAPzZGNNPROoD2bY7i4bAWmBsKR3c5+gdhHK15MxcXlq8m3mbEgmrHcCjV3ZgQkzzSrd3V0VxsWFzwikWbj/Bou3HOZaRi7+vMLBtQ0Z3jWBk58bUr121MfzJWbm8umQPn8cmULeWPw+NaMct/Vri71t95tueySvkm81Hmb32MLuTsggN8mNCTHNuvahT+3DqGYa+/DOPjGzP9BHt3Bix85V1B+G0BGG78GjgNaxhrh8YY14UkeeAWGPMfBH5BzAGq58hDZhmjNklIgOAd4BirNnerxlj3i/rWpoglKvkFhTx/qqDzFy+j8Iiw+8GteL+4W09psKnMYatiRks2n6chTuOk5CWg6+PMOCyMEZ1jeDKLo1pWIG2+Jz8ImatPMDbv+ynoKiYKf1b8cDl7ao0AsndjDFsOHSKT9Ye4ocdJygsNgxu15Db+rVkRKfGzFiym3d+2c/qxy8nom4td4frVG5LEK6kCUI5mzGGhdtP8I9FO0k8lcNVXRrz5OhOtAyr+nBKZzHGEHcsk4Xbj7Nw+3EOpWbjI9C3dRijo5pwVZcmNLpEh3JxseF/W4/y0g+7OZ6Ry9VdmvD4qI60csDwUU+SnJnL3A0JfLbuCCcyc2lWrxan8wqJaVmf96f2dnd4TqcJQqkq2nE0g+cWxLP+UBodm4Tw9LWdGVDN2qaNMew6kcWi7cf5fvtx9qecQQR6t2zAqKgmXN21yblvy+sPpvHC9/Fss010++s1nS6Y6OaNCouKWbbzfKf2x7/rwxAv7pw+q0oJQkTCLupM9kiaIJQzJGflMmPxbr7cmEiD4AD+eGUHJvZ2Tz+Do+1NyrL6LHYcZ9eJLACiW9SjfnAAP+5KpkloEI9d3aHUiW4eIzcD/IPB17HNXTn5RQ6fke2pqpog9gJbgA+BRcZDbzk0QShHyi0o4oPVB5n50z7yi4qZOqAVD4xoR6iH9DM42oGU0+dGQx1OzebuwW24e3Abz/6QzDsNr/eELjfA6JfK31+VqqoJQoArgDuA3sAXwEfGmD2ODrQqNEEoRzDG8MOOE/x90U4S0nK4olNj/nJNJ4eUbVAOtu4dWPQY+NWCR+IhuObVSnKEshJEuePTjGWpMWYycDcwBVgvIr+ISH8Hx6qUW1ijWtKY9O6vTPt0E8H+fvz3zr7MmhKjycETFRXC2plQvzUU5sCmj90dkVcqd6KcbbbzrcBtQBLwADAf6AF8CbR2ZoBKOVNyVi5fbzrKF7EJHEg5Q/1gf56/viuTezfHrxqN769xdi2A9MMw6TNY9x9Y/x70v9/hfRE1nT0zqdcCs4HrjTGJJbbHish/nBOWUs5TWFTM8t0pfBGbwE+7kikqNsS0rM994y/jmqgIt9U1UnYyBla/Dg0ug/ajQHxgziTYOR+63uju6C4teaeVyBq0hvBO0KgjhDYDD5uBXpI9/yd0uFTHtDHm/xwcj1JOsz/lNF/GJjJvUyIpWXk0rBPIXYNbc1NMcy4Lr+Pu8JS9jqyFY5vg2n+Bjw+0u8pqavr1P56dIH54Ag78DJT4OA0IgfAOVrII72gljvAOUDfSIxKHPQliiYhMMMakA9jKYMw1xlzl3NCUqrozeYUs3H6cL2IT2HDoFL4+wvAOjZjYuznDOoRXqzIRymbNGxAcBt0nW699fKDvffDDnyExFiJL7W91r6Mb4cByGPkc9LzNuptI2WU9knfCniWw+b/n9z+bOMI72pKHexKHPQki/GxyADDGnBKRRk6MSakqMcawOSGdLzYksGDrMc7kF9GmYW0eH9WRcT2bXXLmsKoGTu6F3Qth2BPgX6IERs9bYPmL8OvbML7MqjzuseIVCKoHMXdAYAi0Gmg9SspO+23i2LsEtpRMHHVsiaNTibuOjk5LHPYkiCIRaWGMOQIgIi254B5JKc9w8nQe39g6nPcmnyY4wJdroiKY2Ls5vVrW97hqo6oS1r4JfkHQ+64LtweGWN/M178Dmc9DaFP3xFeapDjY/b2V1AJLr8wLWMN0L5U4ziaMs8nj4sTRNBruWe7w0O1JEH8BVonIL4AAg7Et0qOUuxUWFbNibwqfb0jgx53JFBYbolvU4/9ujOKabk0vWKhHVXOnU2DLHOhxM9QupcxJ33tg3duwYRaMeNr18V3Kyletb/59KvmxGdwAWg6wHiWVTBy+VavUeynl/t9jjPlBRKKBfrZNDxljTjolGqXsdOjkGb6ITWDepkSSMvNoWCeAOwa1ZkKvSNo1LuNbmqq+NrwHRfnQ/w+lv1+/FXQYDbEfwuBHIaByizU5VOp+iPsaBjzg+Il8l0ocDmTv16siIBkIAjqLCMaYFU6LSqlLOJ1XyIvfxzNnfQI+AsM7NOK5sc25vGMj7XD2ZvnZ1hDRDqOhYRnrM/SbBru+g+1fQK+pLgvvklb9y/p23//+8vf1QPZMlLsLeBBrRbgtWHcSa4HLnRuaUhdas/8kf/pyG8czcrh7cGvuGtzG5WsfKzfZ+hnkpFnfxMvSciA0ibKGvEZPce9Q0YxE2DoXYn4HdarnuB57vnI9iFWD6bAxZjjW0p/pZR+ilONk5xfy7Pw4bn5vHQF+Pnx53wD+ck1nTQ41RXGRVVajWQy06Ff2viLQ7/eQstM258CNVr8OGBgw3b1xVIE9CSLXGJMLICKBxphdQAfnhqWUZePhNEb/eyUfrTnE7wa2YuH0wfRqWd/dYSlX2r0Q0g5Ydw/23BF0vRFqh1tDXt3ldLJVH6r7JKjX3H1xVJE9fRCJIlIP+BZYKiKngMPODUvVdLkFRby6dA/vrTxAs3q1mHN3P/pf5t0L1qhLWPMG1GsJna6zb3+/QIi5E375J5zcBw3bOje+0qydaXWoD3rE9dd2IHtGMd1ge/qsiCwH6gI/ODUqVaNtTUjnj19uZV/yaW7u24InR3fS4ao11ZF1kLAORr0MPhVYmyLmDlj1qlXI75oZzouvNDmnYMP71joVYZe59toOVub/dSLiC8QZYzoCGGN+cUlUqkbKLyzmjZ/28tbP+wmvE8jHd/RhaA1Y8lGVYe0b1gzknrdU7LiQxtB1PGz5DC7/K9Sq55z4SrPuXcjPgsF/dN01naTMPghjTBGwW0RauCgeVUPtPJ7J2JmreeOnfVzfoxmLHx6iyaGmS90PO7+zZk0HVGJNjn73QcEZ2Dzb8bFdSt5pa7Jeh9HQuIvrrusk9ty31wfiRGQ9cObsRmPMGKdFpWqMwqJi/vPLfv79417q1grgvdtjGNm5sbvDqh7SDlqTw7y1hMivb1nrO1R2BnJEd2vY67p3oe808HVBM2XsB1YTkxfcPYB9CeIpp0ehaqR9yVn88YutbE3M4NpuETw/tiv1azunZIBXKS6GpU9ZdYlGvQR973V3RI53JhU2fwrdJlrNRZXVbxp8fqtVC6nzWMfFV5qCXOvfpPVQz6woWwn2dFJrv4NyqKJiwwerDvLykt3UDvDlzZt7cm03Dyqu5skKcuGbeyH+W6jVAH75P6vsdVCouyNzrNj3raVEqzoDucNoqNfCmjjn7ASxeTacToIbZzn3Oi5U7jwIEckSkUzbI1dEikQk056Ti8jVIrJbRPaJyOOlvD9VRFJEZIvtcddF74eKSKKIvGn/r6Q82aGTZ5j4zlpeXLiToe3DWfLwUE0O9spOg0/GWslh5PNw6zzITrW+tXqTglxY9461EFCjjlU7l4+vtVbEkTVwbLNj4itNUYE1MS6yD7Qa7LzruFi5CcIYE2KMCTXGhAK1gBuBt8o7zjYCaiYwCugMTBaRzqXs+rkxpoftcXHqffyOXMcAACAASURBVB7Qmk9eoLjY8MnaQ4z690p2J2Xx6k3defe2XoSHBLo7tOoh7QC8P9JaSW38hzBwOjSLhs7Xw5o3rUqn3mLbXMg+WX5ZDXv1vNWqpvqrE1dI3vYFZByBIY96VZ9QhaqbGcu3gD2ryfUB9hljDhhj8oG5gN33eCLSC2gMLKlIjMrzJJ7K5tb31/H0/+Lo3boBSx4ewrjoSF2fwV6JG2HWSDhzEm7/H3Qdd/69y5+CwlxY8bL74nOk4mIr4UX0gFaDHHPOoLrQ4xbYMQ+yTjjmnCUVF1lzLppEQbsrHX9+N7KniWlcicd4EfknkGvHuZsBCSVeJ9q2XexGEdkmIl+JSHPbNX2AV4BHy4ntHhGJFZHYlBQv+gblJXILivh03WGufm0lWxPS+ce4KD7+XW8i6tYq/2Bl2fU9fHSNNczzrmW/Le3csC1E32aNnjl1yC0hOtTexZC61/6yGvbqey8UF1p/J0eL/x+k7rNGLnnZlx57RjGVnN9eCByiAncC5VgAzDHG5InIvcDHWFVifw8sNMYklvUt0xjzLvAuQExMjK5y5wGy8wtZviuFhTuOs3xXMtn5RfRvE8ZL47vRvIEH1OevTta9C4seg6Y94ebPL10RdOjjsPVzWP53GPeua2N0tDVvQN3mVtOZI4VdBu2vtmY4D3oE/B1U6NEYa0GgsHbQyftG/tsziul3lTz3UaBklapI27aS504t8XIW8JLteX9gsIj8HqgDBIjIaWPMbzq6lftl5Rbw065kFm0/wc97ksktKKZhnQBu6NmM0VER9G8Tho+Pd32zcqqSw1g7jLZGxZQ1USw0wpoUtuo1q3Jok66ui9WREjfC4dVw1T+cM2eh3zT4ZBHs+Mrql3CEPYshaTtc/3bFSoFUE/asB/Ex8KAxJt32uj7wijHmjnIO3QC0E5HWWIlhEnDzReeOMMYct70cA+wEMMbcUmKfqUCMJgfPkpFTwI87k1i4/QQr9qaQX1hMo5BAJsY0Z1RUBL1bNcBXk0LFFeTCN/dYzRa974ZR/2ffB8/AB63mkx+fg1u+cH6czrD2DQisazWZOUPrIdCos1XltcctVW8OMgZWzrCG0UZNcEyMHsaeNN3tbHIAMMacEpGe5R1kjCkUkfuBxYAv8IExJk5EngNijTHzgekiMgar6SoNmFqZX0K5Rnp2Pkvik1i0/Tir9p2koMjQtG4Qt/VryaiuTYhuUV/vFKoiOw3mTIaEX+HKF6w5APZ+iNWqbzWdLHsGDq9x6jKUTnHqkJUUB0yHQCctGSti3UXMfwAOrYLWVRyOenAFJG6Aa16xZnx7ITGm7KZ7EdkKDDPGnLK9bgD8YoyJckF8douJiTGxsbHuDsPrpJ7OY0l8Egu3H2ft/lQKiw2R9WtxTVQEo6Ii6B5ZV0cjOULaAfjveGsVsnHvWJVAKyo/G96Ittrw71xSvTpMF/3Z6h94aBuEOnFeTEEO/KsLNO8Hkz+r2rk+vg5S9sCDWx3Xp+EGIrLRGFPq1G977iBeAdaKyJe21xOAFx0VnPI8yVm5LI6z7hR+PZBKsYFWYcHcM6QNo6Mi6NI0VJOCIyXGwmcTwRRZw1hb9q/ceQKCYdjjsOBB2L0IOo52bJzOkp0Gm2ZbzTTOTA4A/rWsUuArZlhJuUGbyp0nYYN1B3HlC9U6OZTHnk7qT0QklvNrUI8zxsQ7Nyzlaqmn81iw9RgLd5xgw6E0jIHLwmtz//C2jIqKoGOTEE0KzrDre/jqTmuE0q3zoGG7qp2vx63WSKAfn4P2V1WPjtONH1pVVwdUsayGvWLuhFX/skaJjfpn5c6x8hWrWa9XZcfwVA/2dFL3w1oT4k3b61AR6WuMWef06JRLGGO46Z217E85Q8cmITw0oj2jo5rQrrGT2oKVZd07VtNKs2iY/DnUcUB5c18/a/Lcl1Ng2+fQ4+byj3Gnwjzr73DZCNeVxw6NgC7jYPN/YfiTFa9jdWIH7FkEw/8CgXWcE6OHsGcm9dvA6RKvT9u2KS8RdyyT/SlneG5sF354aAgPXtFOk4MzFRfD4r9Ycxw6jIYp3zkmOZzVeaw1d2L5361RUZ5s+5dWgTtHldWwV79p1qI+Wz6t+LErX4GAEOhzt+Pj8jD2JAgxJXqyjTHF2Nd3oaqJpfFJiMDoqAh3h+L9CnKsb/dr37TWOZg42+o7cCQRuOJZyEiwqqJ6KmOs5rDGUdBmmGuv3Sza6qhe9x+rVIa9Tu6DuG+g951WE5OXsydBHBCR6SLib3s8CBxwdmDKdZbGJ9GrRX0a1tHCeU51JtWqxrpzPlz5orWWg7P6CNoMgzbDrc7YXLuKL7vevmWQssvxZTXs1e8+a3jtnh/sP2bVv8AvEPr/wWlheRJ7EsR9wACsyW6JQF/A+++taojEU9nEH8/UVdzA+naffsQqipd/xmoKcpRz1Vi3wISPrQ5ZZ38oXvEM5KRZ39I90ZrXIaTphcUHXanjdRAaaU2cs0f6EavSbPSUS5c98TL2jGJKxpoFDYCI1AKuBb685EGq2lganwRQcxNE+hGrXMLeJdawxcKL2uz9g62hkf61rZ8BwXY8t/30D7aeF2TD93+0mlSmzIcW/VzzuzXtac2nWDvTai/3pA+1Y1usv/fI5903yczXD/reA0ufhhPbrWqsZVn9OiBWqfUawq6+BNvaDlcBk4GRwCo0QXiFpfFJtG1Uhzbh3j0a45yiAkhYdz4ppOyyttdvZX0zbNLV6tgtyLYe+WesO4tzr20/s09CevaF2wtzLn3d+q3glnlW9VVXuvwpiJ9vlQMf7UElwde+aXX09pri3jiib4ef/2mtFXH9zEvvl5UEmz6B7pOgbqTr4nOzMhOEiAzFqp80GlgPDATaGGOyXRCbcrKM7ALWHUzjniGVnCxUXZxOttq79yyG/cshLwN8/K0JaT1vs+YLhLWtepNPcbGVJPKzrXH9BTnnE0eTbu5ZFjTsMutDMPZD6Pd7aNDa9TFcLD0BdnxtjSQKquveWGrVt4YCb5ptdexfajTZ2jehuAAGPezK6NzukglCRBKBI1hDWh81xmSJyEFNDt5j+e5kioqN9zUvFRfD8c2wd6mVFI5tsrbXaQKdx1iLurQZ5vgPbB8fq0kpoDbgwGGrVTX0z7B1rjXs9cb33B2NNXLobF0kT9D3Ptgwy5qwN/Sx376fnWYVQuwyzkq4NUhZdxBfAdcDE4EiEfkfoGsueJGl8UmEhwTSI7Keu0OputwM2P+TlRT2LoUzyYBAZAwM/yu0G2l9i/ep0CKK3iE0wvowXvUvq/28vLZ2Z8pJh40fWR+2ntJU07AdtB1pJYmBD1qjlEpa9w7kn7YWBKphLpkgjDEPicjDwDCsvoeXgLoichPWYj6nL3Ws8nx5hUX8vDuZMT2aVs8KrMZAym5rBbK9S+HIWmvFsKB60HaEteB92xFQu6G7I/UMZ8uBL/sb3PqV++LY9LH1Yeuqshr26jcN/jvOmuPQfdL57XlZ1h1Ph2ugcWf3xecmZfZB2CbILQeWi4g/5zuq3wL0/7xqbM3+VM7kF3Fl5ybuDqXijm2GL6eeX2KzcVdrLH27qyCyt3MWm6nuatWDwY9YI3YOrXLces8VUZhvdQa3HgoR3V1//bJcdjk07AC/vgXdJp7vj9rwPuSmw5Cad/cA9s2DAMAYU2CM+c62mE/zcg9QHm1pfBLBAb70vyzM3aFUjDHw3SNWB/C1r8HDcTBttdXB2LK/Joey9LnHmnew7Fnr7+hqcV9D1jFrzQdPI2JNnDu+FY78am0ryLGGCLcZDs16uTc+N6lUg6wxpozxfMrTFRcblsUnMbR9OEH+1aDaZ0lx31idziOegZjfeU47dnXgX8sqB564waoi60pny2o06mw1/XmibpOsJspf37Jeb5pt9WUNedS9cblRDeyxU9uOZpCclVf9Ri8V5ltlrBt1ubCdWNmvxy0Q1s76O1akBlFVHVgOSTsqtkqeqwUEW186dn0Hqfth9b+tek0tB7o7MrfRBFEDLY0/ga+PcHlHD5pZa4+NH8KpgzDyb9VjnQNP5OsHI56Ck7th6xznX6+42Jpg9vU91jDjqPHOv2ZV9L4bEPjsJshMtO4ePDWhuYA960Es4LfDWzOAWOAdY4yH1xNWF1sSl0SfVg2oFxzg7lDsl5sJv/yftfB82yvcHU311mkMNI2G5f+AruOdtyLakXVWSfPjW6B5X7jm1d8OIfU0dZtBl+thxzyrI72G/7dmVzVXrDUg3rM9MoEsoL3ttapGDp08w97k09WveWn1vyE7FUY+V6O/0TnE2XLgmYnW2H9Hyzxu3TF8cKW11sO4WXDHYquMSXXQ/37w8YOhj9f4/9bsGfIxwBjTu8TrBSKywRjTW0TinBWYco5qWZwv85g1mqTreKsAnaq6NkOtoZ0rZ0D0bY4peVGQC7/OhBWvWGUpBv8RBj1S/VZdaxYNfz4Egbpolj13EHVEpMXZF7bnZ//F850SlXKapfFJdGwSQvMGDl6kxpmW/92aBDfiKXdH4l1GPAM5p6peDtwY2LUQ3uprdX5fNhz+sB5GPF39ksNZmhwA++4g/gisEpH9gACtgd+LSG3gY2cGpxwr7Uw+sYfTuH+4iyuKVkXyTmtZyL7TrIqoynGa9rBKXqydaXXOhlTirjJlN/zwuFXmpGEHuO0b685EeYVy7yCMMQuBdsBDwINAB2PM98aYM8aY18o6VkSuFpHdIrJPRB4v5f2pIpIiIltsj7ts21uKyCbbtjgRua9yv54q6cedSRQbGFmdZk8ve9YqC12Dx6I71eV/haJ8WPFSxY7LSYcfnoC3B0DiRrj6n9aERU0OXsXeaae9gFa2/buLCMaYT8o6wLaGxEys9SMSgQ0iMt8YE3/Rrp8bYy4uzHIc6G+MyROROsAO27HH7IxXlWJJfBIRdYPo2swNZacr4+BKaznIK56F4AbujsY7nS0HvvEjaxnNBuWUfi8ugs3/tZqSslOtY0c8rTWvvFS5dxAiMhuYAQwCetseMXacuw+wzxhzwBiTD8wFxtoTlDEm3xiTZ3sZaE+cqmw5+UWs3JvCyM6NkeowMsMYq25QaDOrHLNynqF/ttbH+OnFsvc78iu8NxwWTLfWz7jnZxjzuiYHL2bPHUQM0NlWuK8imgEJJV6fXc/6YjeKyBBgD/CwMSYBQESaA98DbYE/lXb3ICL3APcAtGjR4uK3VQmr9p0kt6C4+oxeOltSY+xbVokI5TwhTWzlwF+1qr5GdLvw/cxjVrLe/qVVy2ncLGvCW3X4oqGqxJ5v5jsAZzVaLwBaGWO6AUsp0eltjEmwbW8LTBGR33yyGWPeNcbEGGNiwsM9aIEWD7Q0/gQhgX70bV0NivMV5sOPf9OSGq408EGrDtGPfzu/rSAXVsyAN2KsZUsHPwr3b4BuEzQ51BD23EE0BOJFZD1wttkHY8yYco47yoVVXyNt284xxqSWeDkLa80JLtrnmIjsAAZjLWKkKqio2PDjzmSGdWxEgF81aK2L/cAq5X3LPC2p4Sq16lnzFpY+ZfX95GbAkr9Y/w4dr4UrX/CM5UqVS9mTIJ6t5Lk3AO1EpDVWYpiEtb71OSISYYw5bns5Bthp2x4JpBpjckSkPlb/x78qGUe54o9l0ikipHq0zVfCpiOnSD2Tz5XVoXkpN6NESQ0PrfrprfrcbS2O89lNUJAN4R3htm+teQ2qRio3QRhjfqnMiY0xhSJyP7AY8AU+MMbEichzQKwxZj4wXUTGAIVAGjDVdngn4BURMVhzL2YYY7ZXJo7yHEg5zfUzVzOsQzgvje9WveoT2WlpfBL+vsKwDtWgGW71vyEnTUtquIN/LbjyeVj8F2sSXe87wdff3VEpN5JL9T2LyCpjzCARyeLCYn2CtdicR42VjImJMbGxsRU+zhjDB6sP8c9FO2kUEsTrk3vQq6X3DKk0xnD5K78QWb8Ws+8sbYyAB8k4Cm9EQ6fr4EYn1AhSSv2GiGw0xpQ6MvWSDdLGmEG2nyHGmNASjxBPSw5VISLcOag186YNwNdHuOmdX5m5fB/FxW5YccsJ9qec5uDJM9Wjeennv4MptiZvKaXczq4eSxHxFZGmItLi7MPZgblat8h6fDd9EFd3bcLLi3cz5cP1pGTllX+gh1tiK853hacniKR42PKZVfJBS2oo5RHsmSj3AJCENQz1e9vjOyfH5RahQf68Obknf78hivUH0xj9+kpW7zvp7rCqZElcEt0i6xJR18PnEmhJDaU8jj13EGfrL3UxxkTZHt3KPaqaEhFu7tuC/90/kLq1/Ln1/XXMWLybwqJid4dWYcmZuWxJSGdkJw+/ezi4AvYuhsGPaEkNpTyIPQkiAWsFuRqlY5NQ5t8/kAm9Inlz+T4mv/crxzNy3B1WhSzbmQzAyC4enCCKi0uU1LjX3dEopUqwd0W5n0XkCRF55OzD2YF5guAAP14a353XJvYg/lgmo/69kmW2Nv3qYGn8CZo3qEWHxh5c2z7+Gzi22eqY1pIaSnkUexLEEaz+hwAgpMSjxri+ZzMWPDCIZvVqcdcnsTy3IJ78Qs9ucjqTV8jq/amM7NTEcycAFubBsr9B467QbaK7o1FKXcSeiXJ/K2+fmqBNeB2+/v0A/rFwFx+sPkjs4TTemNyTlmG13R1aqVbsSSG/sJgrPbl5KfYDSD+sJTWU8lCXvIMQkddsPxeIyPyLH64L0XME+vny7Jgu/OfWXhw6eYZrXl/Fgq2euUTFkvgk6gX7E9OyvrtDKV1uBvzyErQeqiU1lPJQZd1BzLb9nOGKQKqTq7s2oWuzUB6Ys5kH5mxmzf5UnrmuM0H+nvEtuKComJ92JTOiUyP8fD20ON+q17SkhlIe7pIJwhiz0fazUrWYvF1k/WC+uLc/ryzZw39+2c+mw6d48+aetPOADuENh9LIyCnw3NnTGUfh17cg6iZrXWSllEeyZ6JcOxH5SkTiReTA2YcrgvN0/r4+PD6qIx/f0YeTp/O47s1VfLEhgYqvreRYS+OTCPDzYXA7Dy3Ot1xLaihVHdjT/vAh8DZWxdXhwCfAf50ZVHUztH04ix4cTHSL+jw2bxsPf76F03mFbonFGMPS+CQGt21I7UB7lxx3oaQ42PoZ9LkH6rd0dzRKqTLYkyBqGWN+xKr8etgY8yxwjXPDqn4ahQYx+86+PDKyPfO3HuO6N1ax46jr5xfuPJ5F4qkcz11adNmzEBhiLU6jlPJo9iSIPBHxAfaKyP0icgNQx8lxVUu+PsL0Ee2Yc3c/cvKLGPf2GtYdSC3/QAdaGp+ECIzwxPIaB1fA3iUwSEtqKFUd2FuLKRiYDvQCbgWmODOo6q5vmzC+nz6IyPq1mPbpJhLSsl127aU7T9CzeT3CQwJddk27nCupEaklNZSqJspMECLiC0w0xpw2xiQaY35njLnRGPOri+KrtsLqBDLr9hgKi4q58+MNZOUWOP2ax9Jz2HE0k5Gdmzj9WhUW97WW1FCqmilropyfMaYIaz1oVQltwuvw1i292J9yhgfnbqHIyYsQLdtp1YnyuP6Hwjz48TlbSY2b3B2NUspOZQ1zWQ9EA5ttM6e/BM6cfdMY87WTY/MKg9o15NnrOvPU/+J46YddPDG6k9OutTQ+iTbhtWnbqBJdRGvehJSdUK8l1Gtx/hESUfUyGBvet0pq3KolNZSqTuwZBxkEpAKXY61NLbafmiDsdFv/VuxJOs07Kw7QtlEdJsQ0d/g1MnIKWLs/lTsHt674wVlJVv+AXxAUnLnwPR8/qBtZImlUMIHkpMOKl6HNMLhMS2ooVZ2UlSAa2cp67+B8YjjLOxZsdqGnr+vMgZOn+cs3O2jdsDYxrRw7iufn3ckUFpvKzZ7e/iWYIrhnuZUAMhKtb/zpRy587F0Gp09ceGx5CWT9u1pSQ6lqqqwE4Ys1nLW0/6s1QVSQv68Pb93ci+vfWs29szfy7R8G0rxBsMPOvzQ+iYZ1AujRvBLF+bbOhabREN7Bet2wrfUoTUFuxRIIWCU1IrpXPC6llFuVlSCOG2Oec1kkNUDdYH/euz2GG95azd2fxPLVtAHUccBs5/zCYn7ZncLoqAh8fSr4Lf3EdkjaDqNetm9//yD7E0hGgtV8FfO7isWklPIIZQ1z1fYAJ2jbqA4zb45mb/JpHpq7hWIHjGz69UAqWXmFlVv7Yetc8PGHqPFVjgM4n0DajoBeU2HYn6FOI8ecWynlUmUliCr3KIrI1SKyW0T2icjjpbw/VURSRGSL7XGXbXsPEVkrInEisk1EvGq5sSHtw3nqmk4s25nES4t3V/l8S+JPUMvfl4FtG1bswKJC2PYFtL9KZzYrpX6jrHLfaVU5sW2S3UxgJJAIbBCR+caY+It2/dwYc/9F27KB240xe0WkKbBRRBYbY9KrEpMnmTKgFXuST/OfX/bTvnEdxkVHVuo8xhiWxSczpH3Diq9Hsf9HOJMM3SdX6tpKKe/mzNVk+gD7jDEHjDH5wFxgrD0HGmP2GGP22p4fA5IBD61dXTkiwt/GdKF/mzAen7edjYdPVeo8249mcCIzt3Kzp7fOgVoNoN2Vlbq2Usq7OTNBNAMSSrxOtG272I22ZqSvROQ3EwREpA8QAOwv5b17RCRWRGJTUlIcFbfL+Pv68NYt0UTUC+Le2bEcTc+p8DmWxifhI3B5xwq28+ecgl0Lrb4Hv4AKX1cp5f3cvR7lAqCVMaYbsBT4uOSbIhKBtfTp74wxxRcfbIx51xgTY4yJCQ+vnjcY9WsH8P6UGPIKirnr41jOVHAdiaXxSfRu1YAGtSv4IR/3LRTlafOSUuqSnJkgjgIl7wgibdvOMcakGmPybC9nYVWLBUBEQoHvgb94e3HAto1CeOPmnuw+kcnDn9s/sulIaja7TmRVrvbS1jnQsAM07VnxY5VSNYIzlxzbALQTkdZYiWEScHPJHUQkwhhz3PZyDLDTtj0A+Ab4xBjzlRNjhLwseLXzhbN/L34E1XP6LOBhHRrxl2s68/x38byydDd/uqpjuccsibcmpV1Z0f6H1P2QsA6ueFZnNyulLslpCcIYUygi9wOLsWZlf2CMiROR54BYY8x8YLqIjMFazjQNmGo7/CZgCBAmIme3TTXGbHF4oEUFVjNL+hE4ddha1Cb/9IX7BIa6JIHcMbAVe5OymLl8P+0ahXB9z9K6bM5bGp9Eh8YhtAir4IzsrXMBgW5eNXpYKeVgYox3VM2IiYkxsbGxVT+RMVYH7sVlJEo+8rMuPOZSCaRRZwi7rEKXzy8s5rb317E5IZ3P7+lHzxall844dSafXi8s5ffD2vLoVR3sv0BxMfy7uxXX7d9WKDallPcRkY3GmJjS3vPAVe3dTMSaNBbcAJr2+O37pSWQjITzzw+uPJ9AxBfuXAKRpf7tSxXg58Pbt/bi+pmruWf2Rv73h4E0rffbBXZ+2pVMsaHis6ePrIGMIzDiqYodp5SqcTRBVJQ9CSQ3HdIOwn9vhF/+D275skKXaFA7gFlTYhj31hru/iSWL+/rT3DAhf9US+OTaBIaRFSzuhWLf8scCKgDHa+p2HFKqRrH3cNcvY8I1KoPzaJhwP2wd4m11GYFtW8cwhuTexJ/PJM/frH1gpFNuQVF/LInhSs6N0Iq0veRnw3x30Ln6yGgdoVjUkrVLJognKn33VYH9i92Vkq9yPCOjXhyVCcW7TjBa8v2nNu+et9JcgqKKj57etd3Vgd890mVikcpVbNognCmoFDoNw12f2+V1a6Euwa3ZkKvSF7/aR/ztx4DrOalOoF+9GtTwQJ7W+dA3RbQcmClYlFK1SyaIJyt773WKKcVlbuLEBFeuKErvVvV509fbmXzkVMs25nMsA7hBPpVoDhf5jE48LN19+Cj/+xKqfLpJ4Wz1aoPfe6B+PmQvLNSpwj08+U/t/YiPCSQW2et4+TpvIrPnt72OZhibV5SStlNE4Qr9P8D+AfDihmVPkVYnUDen9IbAD8fYViHChTnM8aaHNe8b4XnZSilai5NEK4Q3AD63AVxX8PJvZU+TYcmIcy+qy+vTuxB3Vr+9h94bDOk7NK7B6VUhWiCcJX+D4BvIKx8pUqniW5RnzHdm1bsoK1zrWt3GVelayulahZNEK5SJxxi7rCW+Ew76LrrFubD9i+h42ioVc9111VKVXuaIFxp4HTw8YNVr7rumvuWQk6arvuglKowTRCuFNIEek2BLZ9ZdZtcYctnUDscLhvhmusppbyGJghXG/gQiA+ses3518pOgz2LIeom8NWyW0qpitEE4Wp1m0GPW2DzbGvymjPtmAfFBdBDm5eUUhWnCcIdBj1sTVpb/W/nXmfLZ9C4KzSJcu51lFJeSROEO9Rvac1J2PgRZCU55xopu+HYJu2cVkpVmiYIdxn0CBTlw5rXnXP+rXOsBYuiJjjn/Eopr6cJwl3CLrM6j2M/gDMnHXvu4iJrvkXbERBSwZpNSillownCnQb/EQpyYO2bjj3vwRWQeVSbl5RSVaIJwp3C20PXcbD+PWtIqqNsnQOBdaHDaMedUylV42iCcLfBj1qrvP36tmPOl5cFOxdA1xvAP8gx51RK1UiaINytcWfoNAbWvQM56VU/X/x8KMiG7jdX/VxKqRrNqQlCRK4Wkd0isk9EHi/l/akikiIiW2yPu0q894OIpIvId86M0SMM+RPkZcD6d6t+rq1zoEEbaN6n6udSStVoTksQIuILzARGAZ2BySLSuZRdPzfG9LA9ZpXY/jJwm7Pi8ygR3az+grUzrSaiyko/AodWWp3TIo6LTylVIznzDqIPsM8Yc8AYkw/MBcbae7Ax5kegCp+W1cyQP0FuutVhXVnbPrd+dpvomJiUUjWaMxNEMyChxOtE27aL3Sgi20TkKxFpXpELiMg9IhIrIrEpKSlVidX9mkVD2yusIa/5Zyp+vDGwZQ60HGTN1FZKqSpydyf1AqCVMaYbC4z+nAAACXRJREFUsBT4uCIHG2PeNcbEGGNiwsPDnRKgSw39M2SnQuyHFT82MRbS9uuyokoph3FmgjgKlLwjiLRtO8cYk2qMybO9nAX0cmI8nq95H2g91CriV5BTsWO3fgZ+taCz3a14SilVJmcmiA1AOxFpLSIBwCRgfskdRCSixMsxwE4nxlM9DP0znEmGTZ/Yf0xBrlXau9N1EBTqvNiUUjWK0xKEMaYQuB9YjPXB/4UxJk5EnhORMbbdpotInIhsBaYDU88eLyIrgS+BESKSKCJXOStWj9JqILQcaC0oVJhX/v4Ae36A3AxtXlJKOZQYY9wdg0PExMSY2NhYd4fhGAd+hk/GwjWvQu87y9//s4lwfCs8HAc+vk4PTynlPURkozEmprT33N1JrUrTeihE9oFV/4LC/LL3PZ0Ce5dCt5s0OSilHEoThCcSsfoiMhJg29yy993+JZgirdyqlHI4TRCequ0IaNoTVr4CRYWX3m/rHIjoAY06uS42pVSNoAnCU529izh1yLpLKE1SHJzYBj20MJ9SyvE0QXiy9ldDkyhYOcNaJe5iWz4DHz/oOt71sSmlvJ4mCE8mYtVoSt0Hcd9c+F5RoXVn0e4qqB3mnviUUl5NE4Sn63gdhHeCFS9DcfH57QeWw+kk6KGd00op59AE4el8fGDIo5CyC3aWmIi+dQ7Uqg/trnRfbEopr6YJojrocgOEtTt/F5GbAbu+t/oe/ALdHZ1SyktpgqgOfHytu4ikHbBnEcR9C4W52ryklHIqTRDVRdfxUL81/PKS1bzUsD00jXZ3VEopL6YJorrw9YPBf4TjW+DIWl1WVCnldJogqpPuk6BuC0B0WVGllNP5uTsAVQG+/jDmdUjeCXVLW71VKaUcRxNEdXPZcOuhlFJOpk1MSimlSqUJQimlVKk0QSillCqVJoj/b+/eY+wo6zCOf5+0KhRJUYuotHEbU2kqsZcAoVYNESVVG6r8g4qKwcRL5GLFkIKJf5omEC+JRkNAS2JTYwpIY7TQ4AWCFyqlN6gIEYWtLa1BK0IUKI9/zLt62p1zduvuds70PJ9kc2bm7M559mTm/GbeOfO+ERFRKwUiIiJqpUBEREStFIiIiKiVAhEREbVku+kMk0LSAeDPE1jFLOCvkxRnqrUpK7Qrb5uyQrvytikrtCvvRLK+0fapdU8cNwVioiT9zvZZTecYjzZlhXblbVNWaFfeNmWFduWdqqxpYoqIiFopEBERUSsF4n9ubDrAUWhTVmhX3jZlhXblbVNWaFfeKcmaaxAREVErZxAREVErBSIiImoNfIGQtFzSI5Iek7S66Ty9SJoj6eeSHpb0kKSrms40FknTJD0o6cdNZxmLpFMkbZD0e0m7JS1tOlM3klaVbWCXpPWSTmg6UydJ35W0X9KujmWvlrRZ0qPl8VVNZhzRJev1ZTvYIel2Sac0mbFTXd6O566WZEmzJuO1BrpASJoGfAt4L7AA+LCkBc2m6ulF4GrbC4Bzgc/1eV6Aq4DdTYcYp28Am2zPBxbSp7klnQ5cCZxl+0xgGvChZlONshZYfsSy1cDdtucBd5f5frCW0Vk3A2fafivwB+DaYx2qh7WMzoukOcAFwBOT9UIDXSCAc4DHbP/R9vPAD4CVDWfqyvZe21vL9DNUH2B9Ozi1pNnA+4Gbms4yFkkzgXcCNwPYft7235tN1dN04ERJ04EZwF8aznMY2/cATx+xeCVwS5m+BfjAMQ3VRV1W23fZfrHM/gaYfcyDddHlvQX4GnANMGnfPBr0AnE68GTH/DB9/IHbSdIQsBj4bbNJevo61Qb7UtNBxmEucAD4XmkSu0nSSU2HqmN7D3AD1ZHiXuCg7buaTTUup9neW6b3Aac1GeYoXAb8tOkQvUhaCeyxvX0y1zvoBaKVJL0SuBX4vO1/NJ2njqQVwH7bDzSdZZymA0uAb9teDDxL/zSBHKa03a+kKmpvAE6S9NFmUx0dV9+v7/vv2Ev6ElXT7rqms3QjaQZwHfDlyV73oBeIPcCcjvnZZVnfkvQyquKwzvZtTefpYRlwoaQ/UTXdvUvS95uN1NMwMGx75IxsA1XB6EfvBh63fcD2C8BtwNsazjQeT0l6PUB53N9wnp4kfQJYAVzi/r5h7E1UBwvby/42G9gq6XUTXfGgF4gtwDxJcyW9nOpC38aGM3UlSVRt5Lttf7XpPL3Yvtb2bNtDVO/rz2z37VGu7X3Ak5LOKIvOBx5uMFIvTwDnSppRtonz6dML6kfYCFxapi8F7mgwS0+SllM1j15o+7mm8/Rie6ft19oeKvvbMLCkbNMTMtAFolyEuhy4k2oH+6Hth5pN1dMy4GNUR+Pbys/7mg51HLkCWCdpB7AI+ErDeWqVs5wNwFZgJ9V+3FfdQkhaD/waOEPSsKRPAmuA90h6lOosaE2TGUd0yfpN4GRgc9nPvtNoyA5d8k7Na/X3mVNERDRloM8gIiKiuxSIiIiolQIRERG1UiAiIqJWCkRERNRKgYgoJP2zPA5J+sgkr/u6I+Z/NZnrj5gKKRARow0BR1UgSqd5vRxWIGy34c7nGHApEBGjrQHeUW6QWlXGtLhe0pYyPsCnASSdJ+leSRspd11L+pGkB8pYDZ8qy9ZQ9by6TdK6smzkbEVl3bsk7ZR0cce6f9ExPsW6ctc0ktaoGhNkh6Qbjvm7EwNjrKOeiEG0Gvii7RUA5YP+oO2zJb0CuE/SSO+pS6jGDXi8zF9m+2lJJwJbJN1qe7Wky20vqnmti6ju2l4IzCp/c095bjHwFqquvO8DlknaDXwQmG/b/TSQTRx/cgYRMbYLgI9L2kbVvfprgHnlufs7igPAlZK2U40hMKfj97p5O7De9iHbTwG/BM7uWPew7ZeAbVRNXweBfwE3S7oI6Ot+gqLdUiAixibgCtuLys/cjvEXnv3vL0nnUfUxtNT2QuBBYCJDgf67Y/oQML30H3YOVV9MK4BNE1h/RE8pEBGjPUPVUduIO4HPlq7WkfTmLoMJzQT+Zvs5SfOphoUd8cLI3x/hXuDicp3jVKpR7e7vFqyMBTLT9k+AVVRNUxFTItcgIkbbARwqTUVrqcaqHqLqY19UI8/VDZe5CfhMuU7wCFUz04gbgR2Sttq+pGP57cBSYDvVADrX2N5XCkydk4E7JJ1AdWbzhf/vX4wYW3pzjYiIWmliioiIWikQERFRKwUiIiJqpUBEREStFIiIiKiVAhEREbVSICIiotZ/AIC/RtoPb8PyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.560834581490125\n",
            "Final Validation Accuracy: 0.5449986068542769\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(model1, tData, vData, batch_size = 500, lr= 0.005, num_epochs=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Yp1GOSwydwOu",
        "outputId": "b1086144-19b5-43a1-d53b-46728165fa21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.5656066042007732\n",
            "Epoch:  0  Validation Accuracy:  0.5424909445528002\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.549270263680379\n",
            "Epoch:  1  Validation Accuracy:  0.5277235998885483\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.5692988261520777\n",
            "Epoch:  2  Validation Accuracy:  0.5427695736974087\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.5599637744261381\n",
            "Epoch:  3  Validation Accuracy:  0.5282808581777654\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.5722595701696332\n",
            "Epoch:  4  Validation Accuracy:  0.5452772359988854\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.5531714793270402\n",
            "Epoch:  5  Validation Accuracy:  0.5338534410699359\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.5773799157058762\n",
            "Epoch:  6  Validation Accuracy:  0.5416550571189747\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.5748371590790344\n",
            "Epoch:  7  Validation Accuracy:  0.5483421565895793\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.5801316660280749\n",
            "Epoch:  8  Validation Accuracy:  0.5500139314572304\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.5751854819046293\n",
            "Epoch:  9  Validation Accuracy:  0.5458344942881025\n",
            "Epoch 10\n",
            "Epoch:  10  Train Accuracy:  0.5780417290745062\n",
            "Epoch:  10  Validation Accuracy:  0.5438840902758428\n",
            "Epoch 11\n",
            "Epoch:  11  Train Accuracy:  0.5837890556968198\n",
            "Epoch:  11  Validation Accuracy:  0.5539147394817497\n",
            "Epoch 12\n",
            "Epoch:  12  Train Accuracy:  0.5812811313525376\n",
            "Epoch:  12  Validation Accuracy:  0.5488994148787963\n",
            "Epoch 13\n",
            "Epoch:  13  Train Accuracy:  0.5880037618865164\n",
            "Epoch:  13  Validation Accuracy:  0.5475062691557537\n",
            "Epoch 14\n",
            "Epoch:  14  Train Accuracy:  0.575533804730224\n",
            "Epoch:  14  Validation Accuracy:  0.539704653106715\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.575533804730224\n",
            "Final Validation Accuracy: 0.539704653106715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(model1, tData, vData, batch_size = 500, lr= 0.001, num_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "j14HJ_-Ei8Uc",
        "outputId": "173c30c4-5f17-441a-b853-8da957b66fd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.5955623672019228\n",
            "Epoch:  0  Validation Accuracy:  0.5586514349400947\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.5984186143717998\n",
            "Epoch:  1  Validation Accuracy:  0.5603232098077459\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.5982444529590024\n",
            "Epoch:  2  Validation Accuracy:  0.5575369183616606\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.5982792852415619\n",
            "Epoch:  3  Validation Accuracy:  0.5617163555307885\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.5987321049148351\n",
            "Epoch:  4  Validation Accuracy:  0.5586514349400947\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.598801769479954\n",
            "Epoch:  5  Validation Accuracy:  0.5583728057954862\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.5999860670869762\n",
            "Epoch:  6  Validation Accuracy:  0.5583728057954862\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.6008220418684036\n",
            "Epoch:  7  Validation Accuracy:  0.5603232098077459\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.6000905639346547\n",
            "Epoch:  8  Validation Accuracy:  0.5597659515185288\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.598941098610192\n",
            "Epoch:  9  Validation Accuracy:  0.5580941766508777\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.598941098610192\n",
            "Final Validation Accuracy: 0.5580941766508777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "use_cuda = True\n",
        "model2 = CNNalex()\n",
        "\n",
        "if use_cuda and torch.cuda.is_available():\n",
        "  model2.cuda()\n",
        "  print('CUDA is available!  Training on GPU ...')\n",
        "else:\n",
        "  print('CUDA is not available.  Training on CPU ...')\n",
        "\n",
        "train2(model2, tData, vData, batch_size = 500, lr= 0.01, num_epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1aCIcFg-YM-E",
        "outputId": "6fdd7c6e-37c1-4a28-da85-22dbc49e880f"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CUDA is available!  Training on GPU ...\n",
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.38169215228673936\n",
            "Epoch:  0  Validation Accuracy:  0.3839509612705489\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.4892194085478421\n",
            "Epoch:  1  Validation Accuracy:  0.47952075787127335\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.5304956633808213\n",
            "Epoch:  2  Validation Accuracy:  0.5162998049595988\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.573339370928977\n",
            "Epoch:  3  Validation Accuracy:  0.5444413485650599\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.5843463722177714\n",
            "Epoch:  4  Validation Accuracy:  0.545555865143494\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.6121077014176739\n",
            "Epoch:  5  Validation Accuracy:  0.5536361103371412\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.6252743042251558\n",
            "Epoch:  6  Validation Accuracy:  0.5578155475062692\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.668083179490752\n",
            "Epoch:  7  Validation Accuracy:  0.5606018389523544\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.6968894771674388\n",
            "Epoch:  8  Validation Accuracy:  0.561994984675397\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.726531749625553\n",
            "Epoch:  9  Validation Accuracy:  0.5789913624965172\n",
            "Epoch 10\n",
            "Epoch:  10  Train Accuracy:  0.7920164408373681\n",
            "Epoch:  10  Validation Accuracy:  0.5728615213151296\n",
            "Epoch 11\n",
            "Epoch:  11  Train Accuracy:  0.8332578633877878\n",
            "Epoch:  11  Validation Accuracy:  0.5848425745332961\n",
            "Epoch 12\n",
            "Epoch:  12  Train Accuracy:  0.8135427914591243\n",
            "Epoch:  12  Validation Accuracy:  0.556143772638618\n",
            "Epoch 13\n",
            "Epoch:  13  Train Accuracy:  0.8028492807133651\n",
            "Epoch:  13  Validation Accuracy:  0.5327389244915018\n",
            "Epoch 14\n",
            "Epoch:  14  Train Accuracy:  0.8674283325786338\n",
            "Epoch:  14  Validation Accuracy:  0.5533574811925327\n",
            "Epoch 15\n",
            "Epoch:  15  Train Accuracy:  0.9289421435786687\n",
            "Epoch:  15  Validation Accuracy:  0.5541933686263583\n",
            "Epoch 16\n",
            "Epoch:  16  Train Accuracy:  0.9452088195339441\n",
            "Epoch:  16  Validation Accuracy:  0.5675675675675675\n",
            "Epoch 17\n",
            "Epoch:  17  Train Accuracy:  0.9307882545543209\n",
            "Epoch:  17  Validation Accuracy:  0.5438840902758428\n",
            "Epoch 18\n",
            "Epoch:  18  Train Accuracy:  0.9374760528057403\n",
            "Epoch:  18  Validation Accuracy:  0.5288381164669824\n",
            "Epoch 19\n",
            "Epoch:  19  Train Accuracy:  0.9611620049461841\n",
            "Epoch:  19  Validation Accuracy:  0.5750905544719978\n",
            "Epoch 20\n",
            "Epoch:  20  Train Accuracy:  0.9804242572015744\n",
            "Epoch:  20  Validation Accuracy:  0.5678461967121761\n",
            "Epoch 21\n",
            "Epoch:  21  Train Accuracy:  0.9609181789682678\n",
            "Epoch:  21  Validation Accuracy:  0.5586514349400947\n",
            "Epoch 22\n",
            "Epoch:  22  Train Accuracy:  0.9548225295203595\n",
            "Epoch:  22  Validation Accuracy:  0.5497353023126219\n",
            "Epoch 23\n",
            "Epoch:  23  Train Accuracy:  0.9921627364241179\n",
            "Epoch:  23  Validation Accuracy:  0.5675675675675675\n",
            "Epoch 24\n",
            "Epoch:  24  Train Accuracy:  0.9792747918771117\n",
            "Epoch:  24  Validation Accuracy:  0.551407077180273\n",
            "Epoch 25\n",
            "Epoch:  25  Train Accuracy:  0.9932425371834617\n",
            "Epoch:  25  Validation Accuracy:  0.5798272499303427\n",
            "Epoch 26\n",
            "Epoch:  26  Train Accuracy:  0.9965167717440524\n",
            "Epoch:  26  Validation Accuracy:  0.5784341042073001\n",
            "Epoch 27\n",
            "Epoch:  27  Train Accuracy:  0.9966561008742902\n",
            "Epoch:  27  Validation Accuracy:  0.5834494288102535\n",
            "Epoch 28\n",
            "Epoch:  28  Train Accuracy:  0.9971089205475635\n",
            "Epoch:  28  Validation Accuracy:  0.5625522429646141\n",
            "Epoch 29\n",
            "Epoch:  29  Train Accuracy:  0.9976662370685151\n",
            "Epoch:  29  Validation Accuracy:  0.5909724157146837\n",
            "Epoch 30\n",
            "Epoch:  30  Train Accuracy:  0.9981538890243478\n",
            "Epoch:  30  Validation Accuracy:  0.5817776539426024\n",
            "Epoch 31\n",
            "Epoch:  31  Train Accuracy:  0.9986415409801804\n",
            "Epoch:  31  Validation Accuracy:  0.5893006408470326\n",
            "Epoch 32\n",
            "Epoch:  32  Train Accuracy:  0.9983628827197046\n",
            "Epoch:  32  Validation Accuracy:  0.572582892170521\n",
            "Epoch 33\n",
            "Epoch:  33  Train Accuracy:  0.9986415409801804\n",
            "Epoch:  33  Validation Accuracy:  0.5781554750626916\n",
            "Epoch 34\n",
            "Epoch:  34  Train Accuracy:  0.9985718764150615\n",
            "Epoch:  34  Validation Accuracy:  0.5814990247979939\n",
            "Epoch 35\n",
            "Epoch:  35  Train Accuracy:  0.9987460378278589\n",
            "Epoch:  35  Validation Accuracy:  0.5923655614377263\n",
            "Epoch 36\n",
            "Epoch:  36  Train Accuracy:  0.9987808701104184\n",
            "Epoch:  36  Validation Accuracy:  0.5873502368347729\n",
            "Epoch 37\n",
            "Epoch:  37  Train Accuracy:  0.9988157023929778\n",
            "Epoch:  37  Validation Accuracy:  0.583170799665645\n",
            "Epoch 38\n",
            "Epoch:  38  Train Accuracy:  0.9987808701104184\n",
            "Epoch:  38  Validation Accuracy:  0.599052660908331\n",
            "Epoch 39\n",
            "Epoch:  39  Train Accuracy:  0.9988157023929778\n",
            "Epoch:  39  Validation Accuracy:  0.5881861242685985\n",
            "Epoch 40\n",
            "Epoch:  40  Train Accuracy:  0.99867637326274\n",
            "Epoch:  40  Validation Accuracy:  0.5814990247979939\n",
            "Epoch 41\n",
            "Epoch:  41  Train Accuracy:  0.9987460378278589\n",
            "Epoch:  41  Validation Accuracy:  0.5976595151852884\n",
            "Epoch 42\n",
            "Epoch:  42  Train Accuracy:  0.9988853669580967\n",
            "Epoch:  42  Validation Accuracy:  0.5904151574254667\n",
            "Epoch 43\n",
            "Epoch:  43  Train Accuracy:  0.9987808701104184\n",
            "Epoch:  43  Validation Accuracy:  0.5906937865700752\n",
            "Epoch 44\n",
            "Epoch:  44  Train Accuracy:  0.99867637326274\n",
            "Epoch:  44  Validation Accuracy:  0.593201448871552\n",
            "Epoch 45\n",
            "Epoch:  45  Train Accuracy:  0.9987808701104184\n",
            "Epoch:  45  Validation Accuracy:  0.5901365282808582\n",
            "Epoch 46\n",
            "Epoch:  46  Train Accuracy:  0.9987808701104184\n",
            "Epoch:  46  Validation Accuracy:  0.5887433825578156\n",
            "Epoch 47\n",
            "Epoch:  47  Train Accuracy:  0.9987808701104184\n",
            "Epoch:  47  Validation Accuracy:  0.5865143494009474\n",
            "Epoch 48\n",
            "Epoch:  48  Train Accuracy:  0.998537044132502\n",
            "Epoch:  48  Validation Accuracy:  0.5909724157146837\n",
            "Epoch 49\n",
            "Epoch:  49  Train Accuracy:  0.9986067086976209\n",
            "Epoch:  49  Validation Accuracy:  0.5893006408470326\n",
            "Epoch 50\n",
            "Epoch:  50  Train Accuracy:  0.9988157023929778\n",
            "Epoch:  50  Validation Accuracy:  0.5909724157146837\n",
            "Epoch 51\n",
            "Epoch:  51  Train Accuracy:  0.9988157023929778\n",
            "Epoch:  51  Validation Accuracy:  0.582613541376428\n",
            "Epoch 52\n",
            "Epoch:  52  Train Accuracy:  0.9988505346755373\n",
            "Epoch:  52  Validation Accuracy:  0.5862357202563389\n",
            "Epoch 53\n",
            "Epoch:  53  Train Accuracy:  0.9987460378278589\n",
            "Epoch:  53  Validation Accuracy:  0.5915296740039008\n",
            "Epoch 54\n",
            "Epoch:  54  Train Accuracy:  0.9988157023929778\n",
            "Epoch:  54  Validation Accuracy:  0.5842853162440791\n",
            "Epoch 55\n",
            "Epoch:  55  Train Accuracy:  0.9988505346755373\n",
            "Epoch:  55  Validation Accuracy:  0.5943159654499861\n",
            "Epoch 56\n",
            "Epoch:  56  Train Accuracy:  0.9987808701104184\n",
            "Epoch:  56  Validation Accuracy:  0.5987740317637225\n",
            "Epoch 57\n",
            "Epoch:  57  Train Accuracy:  0.9988157023929778\n",
            "Epoch:  57  Validation Accuracy:  0.5820562830872109\n",
            "Epoch 58\n",
            "Epoch:  58  Train Accuracy:  0.9987808701104184\n",
            "Epoch:  58  Validation Accuracy:  0.5887433825578156\n",
            "Epoch 59\n",
            "Epoch:  59  Train Accuracy:  0.9988853669580967\n",
            "Epoch:  59  Validation Accuracy:  0.5962663694622458\n",
            "Epoch 60\n",
            "Epoch:  60  Train Accuracy:  0.9988157023929778\n",
            "Epoch:  60  Validation Accuracy:  0.5948732237392031\n",
            "Epoch 61\n",
            "Epoch:  61  Train Accuracy:  0.9988505346755373\n",
            "Epoch:  61  Validation Accuracy:  0.5904151574254667\n",
            "Epoch 62\n",
            "Epoch:  62  Train Accuracy:  0.9988505346755373\n",
            "Epoch:  62  Validation Accuracy:  0.5976595151852884\n",
            "Epoch 63\n",
            "Epoch:  63  Train Accuracy:  0.9988157023929778\n",
            "Epoch:  63  Validation Accuracy:  0.5957091111730287\n",
            "Epoch 64\n",
            "Epoch:  64  Train Accuracy:  0.9987808701104184\n",
            "Epoch:  64  Validation Accuracy:  0.5904151574254667\n",
            "Epoch 65\n",
            "Epoch:  65  Train Accuracy:  0.9989201992406562\n",
            "Epoch:  65  Validation Accuracy:  0.5971022568960713\n",
            "Epoch 66\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-5a36776b45dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CUDA is not available.  Training on CPU ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mtrain2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-11-1611722d8874>\u001b[0m in \u001b[0;36mtrain2\u001b[0;34m(model, train_data, val_data, batch_size, num_epochs, lr)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Epoch\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mimgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0;31m# print(\"iteration \", n)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1186\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1187\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1150\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1153\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    988\u001b[0m         \u001b[0;31m#   (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/multiprocessing/queues.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    102\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m                     \u001b[0mtimeout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeadline\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonotonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_readable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_poll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    415\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/multiprocessing/connection.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(object_list, timeout)\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m                 \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    922\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileobj\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m             \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    416\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# apr 3\n",
        "first model, increase weight decay, smaller batch size"
      ],
      "metadata": {
        "id": "dkGGdhyKNKa6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "use_cuda = True\n",
        "model3 = CNNalex()\n",
        "\n",
        "if use_cuda and torch.cuda.is_available():\n",
        "  model3.cuda()\n",
        "  print('CUDA is available!  Training on GPU ...')\n",
        "else:\n",
        "  print('CUDA is not available.  Training on CPU ...')\n",
        "\n",
        "train2(model3, tData, vData, batch_size = 256, lr= 0.01, num_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1s-0W4UHBVut",
        "outputId": "d69bc212-5cb1-41a0-d466-696437cff6af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA is available!  Training on GPU ...\n",
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.4471071789334355\n",
            "Epoch:  0  Validation Accuracy:  0.4460852605182502\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.5163189243791145\n",
            "Epoch:  1  Validation Accuracy:  0.49568124825856785\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.5566198753004284\n",
            "Epoch:  2  Validation Accuracy:  0.5307885204792421\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.6063952070779198\n",
            "Epoch:  3  Validation Accuracy:  0.5572582892170521\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.6007523773032847\n",
            "Epoch:  4  Validation Accuracy:  0.546113123432711\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.6624055174335575\n",
            "Epoch:  5  Validation Accuracy:  0.5728615213151296\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.5413981678219374\n",
            "Epoch:  6  Validation Accuracy:  0.4714405126776261\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.7464558152495733\n",
            "Epoch:  7  Validation Accuracy:  0.5809417665087768\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.7290048416872758\n",
            "Epoch:  8  Validation Accuracy:  0.5625522429646141\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.7403253335191055\n",
            "Epoch:  9  Validation Accuracy:  0.5463917525773195\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.7403253335191055\n",
            "Final Validation Accuracy: 0.5463917525773195\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(model3, tData, vData, batch_size = 256, lr= 0.01, num_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9rK9AwyNFpl8",
        "outputId": "50b5edb1-f4e5-44e6-89b9-d692fb0fdd46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.7751924483611411\n",
            "Epoch:  0  Validation Accuracy:  0.5661744218445249\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.7860252882371381\n",
            "Epoch:  1  Validation Accuracy:  0.5266090833101142\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.845344665435926\n",
            "Epoch:  2  Validation Accuracy:  0.5530788520479242\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.8452750008708071\n",
            "Epoch:  3  Validation Accuracy:  0.5525215937587071\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.8927165697168136\n",
            "Epoch:  4  Validation Accuracy:  0.5759264419058233\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.8240969730746456\n",
            "Epoch:  5  Validation Accuracy:  0.5202006129841181\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.9100282141488731\n",
            "Epoch:  6  Validation Accuracy:  0.566731680133742\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.9074157929569124\n",
            "Epoch:  7  Validation Accuracy:  0.5569796600724436\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.8648507436692326\n",
            "Epoch:  8  Validation Accuracy:  0.556143772638618\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.9198509178306454\n",
            "Epoch:  9  Validation Accuracy:  0.5511284480356645\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.9198509178306454\n",
            "Final Validation Accuracy: 0.5511284480356645\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(model3, tData, vData, batch_size = 256, lr= 0.001, num_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VcuMjE-7JTkr",
        "outputId": "7e5e6122-4ac1-4c0c-e90f-9583aa2428fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.9935211954439375\n",
            "Epoch:  0  Validation Accuracy:  0.5965449986068543\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.9954718032672681\n",
            "Epoch:  1  Validation Accuracy:  0.5984954026191139\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.9963774426138144\n",
            "Epoch:  2  Validation Accuracy:  0.5987740317637225\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.9968999268522066\n",
            "Epoch:  3  Validation Accuracy:  0.5993312900529395\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.9973179142429204\n",
            "Epoch:  4  Validation Accuracy:  0.5976595151852884\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.9974572433731582\n",
            "Epoch:  5  Validation Accuracy:  0.5948732237392031\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.9979100630464315\n",
            "Epoch:  6  Validation Accuracy:  0.6012816940651993\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.9977010693510746\n",
            "Epoch:  7  Validation Accuracy:  0.5987740317637225\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.9983280504371451\n",
            "Epoch:  8  Validation Accuracy:  0.6004458066313736\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.9981190567417882\n",
            "Epoch:  9  Validation Accuracy:  0.6007244357759821\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.9981190567417882\n",
            "Final Validation Accuracy: 0.6007244357759821\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# apr 3\n",
        "2nd train, earlier adaptive lr"
      ],
      "metadata": {
        "id": "bX_4stpENjQR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "use_cuda = True\n",
        "model5 = CNNalex()\n",
        "\n",
        "if use_cuda and torch.cuda.is_available():\n",
        "  model5.cuda()\n",
        "  print('CUDA is available!  Training on GPU ...')\n",
        "else:\n",
        "  print('CUDA is not available.  Training on CPU ...')\n",
        "\n",
        "train2(model5, tData, vData, batch_size = 256, lr= 0.01, num_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qVhFogFRNqUK",
        "outputId": "9596492d-1d4b-4e6f-dff7-a2d29409ce13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA is available!  Training on GPU ...\n",
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.4705841373785224\n",
            "Epoch:  0  Validation Accuracy:  0.4683755920869323\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.5323417743564736\n",
            "Epoch:  1  Validation Accuracy:  0.5135135135135135\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.5588491413842349\n",
            "Epoch:  2  Validation Accuracy:  0.5299526330454165\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.5939949144867464\n",
            "Epoch:  3  Validation Accuracy:  0.5553078852047925\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.6091469574001185\n",
            "Epoch:  4  Validation Accuracy:  0.5536361103371412\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.6333553937789543\n",
            "Epoch:  5  Validation Accuracy:  0.5583728057954862\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.671810233724616\n",
            "Epoch:  6  Validation Accuracy:  0.5692393424352187\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.6715315754641402\n",
            "Epoch:  7  Validation Accuracy:  0.5458344942881025\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.7248249677801386\n",
            "Epoch:  8  Validation Accuracy:  0.571468375592087\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.7666237068515099\n",
            "Epoch:  9  Validation Accuracy:  0.5739760378935637\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.7666237068515099\n",
            "Final Validation Accuracy: 0.5739760378935637\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(model5, tData, vData, batch_size = 256, lr= 0.001, num_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bR1_B72gNyjJ",
        "outputId": "2c2972bc-5083-4ad9-9adf-4b28783ce767"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.8529729353164512\n",
            "Epoch:  0  Validation Accuracy:  0.5873502368347729\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.8732453237660663\n",
            "Epoch:  1  Validation Accuracy:  0.5859570911117303\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.8873523982026542\n",
            "Epoch:  2  Validation Accuracy:  0.5870716076901644\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.9014246403566826\n",
            "Epoch:  3  Validation Accuracy:  0.5834494288102535\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.905987669371974\n",
            "Epoch:  4  Validation Accuracy:  0.5856784619671218\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.9227768295656414\n",
            "Epoch:  5  Validation Accuracy:  0.5851212036779047\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.9316242293357484\n",
            "Epoch:  6  Validation Accuracy:  0.5867929785455559\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.9424222369291859\n",
            "Epoch:  7  Validation Accuracy:  0.5845639453886876\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.9499808422445923\n",
            "Epoch:  8  Validation Accuracy:  0.5859570911117303\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.9515831272423282\n",
            "Epoch:  9  Validation Accuracy:  0.5851212036779047\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Zeo6wdg4TVJY4jPHbF8+46UEOZ0LDpghkwYYFUF0ZmvObdscShzF+UnXVVGnHQs/hoblGehZUlrrFnYwJAkscxvjpm0+hcA0cHeRG8brSs9y9VVeZILHEYYyfFk2HuM4w4luhu0anFOgx1OatMkFjicMYv+zZCTkvwagLIK5jaK8VyHKlm+qq0F7HtAuWOIzxy7L/QdWe0FZT1QpMgIpdsPmL0F/LtHmWOIzxg6qrpko90i3zGmrpE9y9jecwQWCJwxg/bFoM+ctC1wW3vs69IHmwLexkgsIShzF+WDQDYjrAkd8J3zXTs+CbT6CmOnzXNG2SJQ5jwq18Fyyb5XpSJXQJ33UDE6C82JV0jGkBSxzGhFvOi1BREr5qqlq14zmsW65pIUscxoTbohluXEX/ceG9bpe+0G2ADQQ0LWaJw5hw2roS8ha40ob4sJhlIAvWf+zW/zDmMFniMCacFs2AqFg46iJ/rp8+Acp2wtYcf65v2gRLHMaES2WZmwl3+NnQsYc/MQRq562ydg5z+CxxGBMuq16FPTuCv8pfc3TtD13620BA0yKWOIwJl0XToWs6DDjJ3zgCE6ydw7SIJQ5jwmH7Ovj6fRh7GUT5/LULZEFpIRSs8jcO02qF9C9YRKaIyGoRWSsitzfweryIPOu9/pmIBLztySIyX0R2ichf6x1ztIgs8455WMSPrinGNNOiGSBRMPq7fkdi4zlMi4UscYhINPAIcAaQAVwsIhn1drsK2KGqg4GHgN9528uAXwK3NnDqvwNXA0O825TgR29MEFVXwpL/wpDJkNTb72igWwCS+tp4DnPYQlniOBZYq6rrVLUCmAlMrbfPVGC693gWMElERFV3q+qHuASyl4j0BpJU9VNVVWAGcF4I34MxLfflXNi1JTzTpzeFiCt1rP/IzdJrTDOFMnH0BTbUeZ7nbWtwH1WtAoqA5EOcM+8Q5wRARK4RkWwRyS4oKGhm6MYE0aLp0Lk3DD7N70j2CWTB7gLYtsbvSEwr1GYbx1X1UVXNVNXMlJQUv8Mx7VVRHqydB6MvhegYv6PZJ3CCu7duueYwhDJxbAT61Xme5m1rcB8RiQG6AIWHOGfaIc5pTORY/DRojetNFUm6D4ROqdbOYQ5LKBPH58AQERkgInHARcDsevvMBmorfi8A3vHaLhqkqpuBYhEZ5/Wm+h7wcvBDNyYIaqph8X9g4ETXIB1JRFx1Va61c5jmC1ni8NosrgfmAiuB51Q1R0TuE5Fzvd0eA5JFZC1wM7C3y66I5AJ/Aq4Qkbw6PbJ+DPwbWAt8BbweqvdgTIusmw9FG/wdKd6Y9CzYle/GmBjTDCGtdFXVOcCcetvuqvO4DJh2kGMDB9meDYwMXpTGhMjC6ZCYDMPO8juShgW8dchzP4TkQf7GYlqVNts4boyvdm2F1XPgqIshJt7vaBrW4wjomGIDAU2zWeIwJhS+eAZqqsK/yl9z1I7nsHYO00yWOIwJNlU3xUj/4yFlqN/RNC4wAYrzYEeu35GYVsQShzHBtv4jKFwb2aWNWjZvlTkMljiMCbZFMyC+C2S0gtlwUoZBh+62sJNpFkscxgTTnh2w4mU4chrEJfodzaFFRUH6eBtBbprFEocxwbT0Oagqax3VVLUCE2DnN7Bzw6H3NQZLHMYEj6obu9F7NPQ+yu9omq52PIe1c5gmssRhTLBsXARbcyJn+vSm6jkCErpC7gd+R2JaCUscxgTLoichNhFGXuB3JM1T285hDeSmiSxxGBMM5SWw7HkYcT4kJPkdTfOlZ8GOr6F4k9+RmFbAEocxwbD8Bajc3fqqqWoFvPEcVuowTWCJw5hgWDTdjYlIO8bvSA5P6pEQn2Tdck2TWOIwpqXyl8PGhW76dBG/ozk8UdFuihQrcZgmsMRhTEstmgHRcXDURX5H0jKBLChcAyX5fkdiIpwlDmNaonIPLJ0Jw8+BxO5+R9My6TaewzSNJQ5jWmLlK1BWFLmr/DVH76MgrpNVV5lDssRhTEssnA7dBkDgBL8jabnoGOh3nJU4zCFZ4jDmcG1b63ohjb3MDaJrCwIToGAV7N7mdyQmgrWRv3ZjfLB4Bkg0jL7U70iCx+atMk1gicOYw1FVAUv+C0PPgM6pfkcTPH3GuGlTcm08hzk4SxzGHI4vX4fdBa1r+vSmiI6FfsdaA7lpVEgTh4hMEZHVIrJWRG5v4PV4EXnWe/0zEQnUee0Ob/tqEZlcZ/tNIpIjIstF5BkRSQjlezDmABWlMO9e6BaAwaf6HU3wpU9ws/yWbvc7EhOhQpY4RCQaeAQ4A8gALhaRjHq7XQXsUNXBwEPA77xjM4CLgBHAFOBvIhItIn2BG4BMVR0JRHv7GRM+79wP27+Ccx52I67bmtp5q9Z/7G8cJmKFssRxLLBWVdepagUwE5hab5+pwHTv8SxgkoiIt32mqpar6tfAWu98ADFABxGJARIBm87ThM/6T+DTv8ExV8PAk/yOJjT6Hg0xCdZAbg4qlImjL1B3Lco8b1uD+6hqFVAEJB/sWFXdCDwIfANsBopU9c2GLi4i14hItohkFxQUBOHtmHavYje8/GPo2h9OvcfvaEInJt5N1mgN5OYgWlXjuIh0w5VGBgB9gI4i8t2G9lXVR1U1U1UzU1JSwhmmaavevg+2r4Pz/gbxnfyOJrQCEyB/GezZ4XckJgI1KXGISEcRifIeHyEi54pI7CEO2wj0q/M8zdvW4D5e1VMXoLCRY08FvlbVAlWtBF4AxjflPRjTIrkfwmf/gON+uG+sQ1uWngUofPOp35GYCNTUEsf7QILXOP0mcBnw5CGO+RwYIiIDRCQO14g9u94+s4HaSX4uAN5RVfW2X+T1uhoADAEW4KqoxolIotcWMglY2cT3YMzhKd8FL1/nphaZdJff0YRHWqab8deqq0wDYpq4n6hqqYhcBfxNVX8vIksaO0BVq0TkemAurvfT46qaIyL3AdmqOht4DPiPiKwFtuP1kPL2ew5YAVQB16lqNfCZiMwCFnnbFwOPNvdNG9Ms8+6BHevh+3MgrqPf0YRHbAfom2kN5KZBTU4cInI8cCmuCy24ZNAoVZ0DzKm37a46j8uAaQc59gHggQa23w3c3cS4jWmZde/B5/+CcddBejurFQ1MgA8ehLLi1rmOugmZplZV3QjcAbzolQYGAvNDF5YxEaC8BGZfD90HwSm/8Dua8AtkgdbAhs/8jsREmCaVOFT1PeA9AK+RfJuq3hDKwIzx3Vt3wc4NcOVciEv0O5rwSzsWomIh9wMYcprf0ZgI0tReVf8VkSQR6QgsB1aIyM9CG5oxPvpqPmQ/DuOvh/7H+R2NP+ISoe9Ym7fKHKCpVVUZqloMnAe8jhtHcVnIojLGT2XFMPsnkDwETv6539H4Kz0LNi12PcuM8TQ1ccR64zbOA2Z7Yyg0dGEZ46M3fwHFG+G8v7veRe1ZYAJotbVzmP00NXH8E8gFOgLvi0g6UByqoIzxzdp5sGg6jL8B+h3jdzT+63ecW6zKuuWaOpraOP4w8HCdTetF5OTQhGSMT8qKYPYNkDIMJt7hdzSRIb6TW9zJ2jlMHU1tHO8iIn+qnTRQRP6IK30Y03bMvRNK8t1cVLG2zMtegSzYuNCtQ2IMTa+qehwoAb7j3YqBJ0IVlDFh9+WbsPgpmHCjm1bc7JM+AWoqIW+B35GYCNHUkeODVPXbdZ7fe6gpR4xpNfbsgFdugJ4ZcNL/8zuayNN/HEiUq64aONHvaEwEaGqJY4+I7J0SVESygD2hCckcoOBLqKrwO4q26407YddWV0UVE+93NJEnIQl6H2UN5GavpiaOHwKPiEiuiOQCfwWuDVlUxqmpgXcegEeOgXd+5Xc0bdPq1+GL/8IJt7hGYNOw9CzIy4bKMr8jMRGgSYlDVb9Q1aOAI4EjVXUMcEpII2vvyorh2Uvh/d9Dh27wxTNQXel3VG1L6XZ45afQayScaBMhNCowAarLYWO235GYCNCsFQBVtdgbQQ5wcwjiMQCFX8G/T4Uv58IZf4Cpf4PdBbD2bb8ja1veuB1KC91Av5g4v6OJbP2PB8TW5zBA0xvHGyJBi8Lss2YezLoSomPgey/DgBNcSSOxByx5GoZO8TvCtmHlq7D0WTdeo/eRfkcT+Tp0hdSRljgM0LI1x23KkWBShQ//DE9fAF37w9XzXdIAiI6FIy909fGl2/2Nsy0o3Q6v3gSpo1zbhmma9AmQ9zlUlfsdifFZo4lDREpEpLiBWwnQJ0wxtn0VpfD8D2De3TDiPLhqLnRL33+f0Ze4vvTLn/cnxrZkzs9cF9zz/uGSsmmawASoKoONi/yOxPis0cShqp1VNamBW2dVbUk1l6m1cwM8PtklhEl3wQVPNLw8aepI9wt5ydPhj7EtWfEyLJ/lxmukjvQ7mtaldgXE9VZd1d61pKrKtFTuR/DoRNiRC5c866pNpJGmo9GXuimut6wIV4Rty+5t8OrN0Hu0GyFumiexO/QcYfNWGUscvlCFz/8NM851XW2vfgeOmHzo40ZNg6gYN+7ANN9rt0B5setFZVVUhyeQ5aZYt67h7ZoljnCrqnBjB167BQZNgqvfhh5DmnZsxx4wZDIsfQ6qq0IbZ1uz/AVY8RJMvB16ZfgdTeuVngWVpa7ka9otSxzhVLIFpp/t1ns44Ra4+BlI6NK8c4y+BHZtga/eCU2MbdGurS5R9xkL43/qdzStW3qWu7duue1aSBOHiEwRkdUislZEbm/g9XgRedZ7/TMRCdR57Q5v+2oRmVxne1cRmSUiq0RkpYgcH8r3EDQbF7n2jPxlrgF80l0QFd388ww5HRKTrZG8qVThtZuhYrdXRWV9OlqkU4pbr8TmrWrXQpY4RCQaeAQ4A8gALhaR+nUEVwE7VHUw8BDwO+/YDOAiYAQwBfibdz6AvwBvqOow4ChgZajeQ9B8MRMen+LaJ66cCyPPP/xzxcS5to7Vc1yXUtO45c/Dylfg5Duh5zC/o2kb0rPgm0+turQdC2WJ41hgraquU9UKYCYwtd4+U4Hp3uNZwCQREW/7TFUtV9WvgbXAsSLSBTgReAxAVStUdWcI30PLVFe5mVdfvBb6HQvXzA/OKOXRl0B1hY3pOJSSLTDnVkg7Bsb/xO9o2o5AFlTsgvwv/I7E+CSUiaMvsKHO8zxvW4P7qGoVUAQkN3LsAKAAeEJEFovIv0UkMlciLN0OT38bPn0Ejr0WLnvRNW4HQ+qRbmK+Jda76qBU3ejwyj2uiupwqgVNw9K9FRasnaPdam2N4zHAWODv3gy9u4ED2k4AROSa2qVuCwoKwhmjG2fxr5Nh/ccw9RE48/fB7f4p4kodGxdCwergnbctWfY/WP0anPKLpvdaM03TuRckD7bxHO1YKBPHRqBfnedp3rYG9xGRGKALUNjIsXlAnqp+5m2fhUskB1DVR1U1U1UzU1JSWvhWmmHFbDezbWUZXDEHxnw3NNcZNQ0k2kodDSne7KYV6XccjPux39G0TelZ8M0nUFPtdyTGB6FMHJ8DQ0RkgIjE4Rq7Z9fbZzZwuff4AuAdVVVv+0Ver6sBwBBggarmAxtEZKh3zCQgMoZR19TA/F/Dc5dBz+FwzbvQ75jQXa9TT9fDaumz9uWtSxVevdFNxDf1b1ZFFSqBE9xgyvxlfkdifBCyxOG1WVwPzMX1fHpOVXNE5D4ROdfb7TEgWUTW4tb3uN07Ngd4DpcU3gCuU9Xa/44/AZ4WkaXAaODXoXoPTVZeAs9+F977nZsW5IrXIKl36K87+hIo2QxfzQ/9tVqLL2bCl2+47s49BvsdTdsV8MZzWLfcdkncD/y2LTMzU7OzQ7RyWeFXMPMS2LYGJv8ajru28fmmgqmqHP44FAadAhc8Hp5rRrLiTfDIOOg1wiXvqNbWhNfK/GU09MyAi626tK0SkYWqmll/u32zWmLtPNcIvmuL6zU17ofhSxoAMfGurWPlq7Ancnslh4UqzL7BTT0/9a+WNMIhkOVKHDU1fkdiwsy+XYdDFT76Czw9Dbr0c+0ZA0/yJ5bRl7i1oHNe8Of6kWLxU7D2LTj1Xkge5Hc07UP6BCjbCVtz/I7EhJkljuaq3AMvXA1v3QXDz3EjwbsF/Iunt1dd0J57VxXlwXfMwM0AACAASURBVNw7XYPtMT/wO5r2o7adw7rltjuWOJqjdtGlZbPc+IBp0yG+k78xicBRF7slPbet8TcWP6jC7J+4nmXn/p9VUYVT1/7uZgs7tTv2LWuq9R+79ozCdW5W2xN/Ft72jMYc+Z32O6Zj0XQ3U/Dp90H3AX5H0/6kT3DfjXbQycbsY4mjKT5/DKafA/FJbv2MoWf4HdH+OqfC4FNdV9T2NKZj5zcw9xcw4EQ4+kq/o2mfAllQWggFq/yOxISRJY7GVFXAKze6abkHTnQr9aUMPdRR/hh9MZRsgq/f8zuS8FCFl68HFM61XlS+sfU52iX7th1MdSXMmAoLn4CsG+GS56BDV7+jOrgjzoCEru2nuir7cZckT78fuqX7HU371S0ASX0tcbQztqrNwUTHwqCT4ZirYNQFfkdzaLEJLs7FT0FZUfNXFmxNduTCm7+EgSfD0Vf4HU37JgKBCa6dSTVy2v1MSFmJozEn3dY6kkat0ZdAVRnkvOh3JKFTU+OqqCTK9aKyf1T+S8+C3QXts1dfO2WJoy3pMxZ6DIUlz/gdSegs/g/kfgBTfg1d+x16fxN6AW99jsX/gd2F/sZiwsISR1tSu07Hhk/dHFptTXkJvHM/9BsHYy7zOxpTq/tAtw75xw/DHwa6+cJeu9WVfHdt9Tu69uvr9+F/V4QkmVsbR1tz5IXw9r2ukXzSL/2OJrg+/DPs3goXz7QqqkgiAtd+AJsWu8GAuR+6v7/P/+Ve73GEK5WkZ7n7zqn+xtse1FS7ZavLiiAu+IukWuJoa5J6u9lyv5gJJ/+87XRT3bkBPvkrjPoOpB3tdzSmvpg46H+cu51wi+uVuPkLl0RyP4Sl/3M94cCtHlibRNKzoEv9FaVNiy3+D2xZBtOedB1ngswSR1s0+hKYdSXkvu/Gn7QFb9/n7ifd5W8cpmmiYyEt090m3AjVVZC/1CWR9R9Bzktu1D9AtwFuIGHgBJdIrO2qZcqK4O1fQf/xkHFeSC5hiaMtGnoWxHdx1QUDJ/odTcttXAjLnnO/ZO2fSusUHQN9x7pb1g2uKiV/mUsiuR+5pQEWP+X27drfTWUSmOASStd0q5psjvcfdKP5p/wmZJ+bJY62KDYBRn3b9a4680FISPI7osOnCnN/Dh1TYMJNfkdjgiUqGvqMdrfjr3PdrLfmuCSy/kO3iuMX3mDWpLR9SSQ9yzXGWyJpWOFX8OnfYcyl7rMNEUscbdVRl7g65RUvw9hW3ANp5Wz45hM45y8Q39nvaEyoREVB6ih3G/dDl0gKVnklkg/comlLZ7p9O/fZl0QCJ7j1VyyROG/d5RZ4OyW0HWMscbRVaZmQPMRVV7XWxFFVDm/d7dYbse637UtUFPTKcLdjr3Ylz21fuiSS+5Hrarrsf27fTr28JJIFgRMh5Qh/Y/fLuvdg1auuHTDEPdcscbRVtWM63r7XFV9b46p4C/4FO76G777gqjZM+yXiJhhNGeoW61KFwrX7GttzP9y3CuYZv4fjrvU33nCrqYY37nDtQ+OuC/nl2khfTdOgIy8ExHXNbW12F8J7v4fBp8HgSX5HYyKNCPQYApnfh2//G25eCT9ZBIMmuVJqWxwA25hF010b0Wm/Ckn32/oscbRlXfq6iRq/mOnqjFuT934HFbvc7LfGHIqIK1VP/StEx8HsG1rf3/zhKityMyqkZ0HG1LBc0hJHWzf6Uij6pnUt77ltDWQ/BkdfDj2H+R2NaU2S+sDkB9zf+8LH/Y4mPN77PZRuD2n32/pCmjhEZIqIrBaRtSJyewOvx4vIs97rn4lIoM5rd3jbV4vI5HrHRYvIYhF5NZTxtwnDznIrF7amdTreugtiOsDEO/2OxLRGY77rptx/6263SmRbVvgVfPZP9557HxW2y4YscYhINPAIcAaQAVwsIhn1drsK2KGqg4GHgN95x2YAFwEjgCnA37zz1fopsDJUsbcpsR1gxLdgxWwo3+V3NIe27j1YPQdOvAU6pfgdjWmNRODch93jV37attdDf/MXEJMQ8u639YWyxHEssFZV16lqBTATqF8BNxXw5h1gFjBJRMTbPlNVy1X1a2Ctdz5EJA04C/h3CGNvW0ZfCpW73ZiOSFZTDW/+HLr0h+N+5Hc0pjXr2h9OvcctMLXkab+jCY2v5u/7kdW5V1gvHcrE0RfYUOd5nretwX1UtQooApIPceyfgduARlu+ROQaEckWkeyCgoLDfQ9tQ79jofugyK+u+mKmm4bi1LvD0jPEtHGZV7kG4zfuhOLNfkcTXNVVMPdONx2LDz+yWlXjuIicDWxV1YWH2ldVH1XVTFXNTElp51UeIjD6YtdguCPX72gaVrHbTWSYdgyM/Lbf0Zi2IMpbJbK6HF69qW1VWS2aDltXuF6HPvzICmXi2AjUnZEuzdvW4D4iEgN0AQobOTYLOFdEcnFVX6eIyFOhCL7NOfIiInpMx0cPw658mPxrmz7CBE/yIDjlF/Dl67D8eb+jCY49O2H+A24iyOHn+BJCKBPH58AQERkgInG4xu7Z9faZDVzuPb4AeEdV1dt+kdfragAwBFigqneoapqqBrzzvaOq3w3he2g7uvaDgSe56qpI699evMmtHjfiW65azZhgGvdj6Hs0zPkZ7GoD1dbv/yHs3W/rC1ni8Nosrgfm4npAPaeqOSJyn4ic6+32GJAsImuBm4HbvWNzgOeAFcAbwHWqWh2qWNuN0ZfCzvXwzcd+R7K/d+6HmirXmGlMsEVFw9RH3IDS13/mdzQts20tfPYPN/9c7yN9CyOkc1Wp6hxgTr1td9V5XAZMO8ixDwAPNHLud4F3gxFnuzHsbIjr7KZbD0zwOxpn0xJXCsq6AboF/I7GtFU9h8NJt7kfKSPOh4xzD31MJHrzF26MU5i739bXqhrHTQvFJcKI8yDnxcgY06HqvgiJ3d0iTcaEUtaNbtr2125xVT2tzVfvuLaaE2+FTj19DcUSR3tTO6Zj5St+RwKrX3fTZE+8AxK6+B2NaeuiY2Hq32DPdteVtTWprnLdirsFYJz/Y5wscbQ3/ce5NZ79HhRVVeFKGz2GwtHf9zcW0370PtKtJPnFM/Dlm35H03QLn4CCla77bUy839FY4mh3atfpyP3A33l8sh+H7V+5L0K0LQtjwujEn0HKMHj1RjezbKTbswPm/9qtdjjsbL+jASxxtE9HXeTu/RrTsWcHvPdbGDgRhpzmTwym/YqJd72sSja7CTUj3Xu/d98ZH7vf1meJoz3q2h8GnOiqq/wYTfv+g24Q0+kPRMwXwbQzaZlw/HWw8ElY967f0RzctjWw4FEY+z3XsB8hLHG0V0dd4qYf+eaT8F63dhrosZdB6sjwXtuYuk7+uZvDbfYNkdHLsCFzfx4R3W/rs8TRXmWcC3Gdwj/x4by73QptJ/8ivNc1pr7YDm7FwJ3r4Z1f+R3NgdbOgzVz4aSfRdwSA5Y42qu4jpBxHuS85CYYDIf1H7tuwBNuCvs00MY0KH08HHuNKwWvD3PpuzHVVa600W0AHPdDv6M5gCWO9mz0xVBRAivDsJBiTY3rO5/U19UtGxMpJt0NXfrB7Ouhco/f0TgLn4CCVRHT/bY+SxztWf/xbj7/L8JQXbXsf7BpsfuSxiWG/nrGNFV8J7diYOFaePc3fkfjdb99wHVgGXaW39E0yBJHexYV5cZ0rHsPdm449P6Hq6IU3r4X+oyBUQ1OTWaMvwad7Houffx/sPGQy/2E1ru/c+NLJkdO99v6LHG0d0ddBCgsDeGYjk8fgeKNbq2NKPuTMxHq9PuhUyq8dB1UlfsTQ8GX8Pm/YOzlEd3r0L7F7V23gFsQZskzoRnTUbIFPnjILTiTPj745zcmWBK6wNkPuak9PvijPzG8+XOITXRdhSOYJQ7jqqu2fwUbFgT/3PPvh+oKOPXe4J/bmGAbOgWOvNAljvxl4b32mnmw5k03JUqEdb+tzxJHI5746GveWbXF7zBCL2MqxHYM/sSH+cth8VOuu2PyoOCe25hQmfJb6NANXvoxVFeG55rVla7XYfeBEdn9tj5LHAdRVV3D/7LzuPLJbK57ehFbi8v8Dil04ju5AYE5LwavO6KqK3YndHEDmIxpLRK7w1l/hPylbknjcMh+Arat9rrfxoXnmi1gieMgYqKjePG68dx82hG8tXILk/70Hk99up6aGh/mdgqH0ZdAeTGsei0451vzlpsD6KTb3a83Y1qTjKnu9u5voWB1aK9Vuh3e/TUMOAmGnhnaawWJJY5GxMdEc8OkIbzx0xMY2acLv3hpOdP++Qmr80v8Di340idAl/7Bqa6qrnJrbSQPhmOuavn5jPHDmQ+6GRZevh5qqkN3nfe87rcRNPvtoVjiaIKBKZ3479XH8eC0o1hXsIuzHv6AP8xdRVllCP+Ywi0qyo0k/2o+FG1s2bkWPemK3afd51ZdM6Y16tQTzvg95C1wU5KEQsFqWPAvOPoK6DUiNNcIAUscTSQiXHB0Gm/fMpFzR/fhkflfMeXP7/PR2m1+hxY8wRjTUVa0b9GZVlLsNuagRk2DI6bA2/fB9nXBP//cn7vJRiO8+219ljiaqXvHOP70ndE8/YPjALj0359x87NLKNzl04ChYOo+0E1D0pIxHR/80dXZnn5/qyl2G3NQIm5sR3Ssm369piZ4517zFqx9C066DTr2CN55wyCkiUNEpojIahFZKyK3N/B6vIg8673+mYgE6rx2h7d9tYhM9rb1E5H5IrJCRHJE5KehjL8xWYN78MaNJ3L9yYN5ZekmJv3pPZ7L3oD6sTBSMI2+BArXQF5284/dkQuf/t2do8/ooIdmjC+S+sDkB9xyywufCM4593a/HeS6q7cyIUscIhINPAKcAWQAF4tIRr3drgJ2qOpg4CHgd96xGcBFwAhgCvA373xVwC2qmgGMA65r4JxhkxAbza2Th/LaDScwOKUTt81aysX/+pR1BRG6KExTZEx1C8ccTiP5vHshKgZOsbU2TPiVVVaTnbud7bsrgn/yMZe5pY7fuis487p9/hhs+9IlpFbQ/ba+UJY4jgXWquo6Va0AZgJT6+0zFZjuPZ4FTBIR8bbPVNVyVf0aWAscq6qbVXURgKqWACuBviF8D01yRK/OPHft8TzwrZHkbCpmyp8/4C/z1lBe1QobzxOS3JiO5S9AZTPGrmxYADkvwPgb3C80Y8KgcFc5/8vewLX/yWbsr97ign98Qub9b3HRo5/wxEdfs2lnkMYlicA5D7sq3Fd+2rLpeUq3u1l4B0507SetUEwIz90XqJua84DjDraPqlaJSBGQ7G3/tN6x+yUIr1prDPBZMIM+XFFRwqXHpXPa8F7c++oKHpr3JbO/2Mhvzj+SYwd09zu85hl9CSx9Fla/BiO/jaqyKr+ET74qJLVLAkNTOxNI7kh0lNeGoeqK3Z1SIesGf2M3bZqq8lXBLt5asZV5K7ew6JsdqEJqUgLnj+1L1qAe5GwqZm5OPve+soJ7X1nBkWldmDwilckjejG4Z+fDv3i3dDjtXphzq1s5c8ylh3eed3/jxkxF8Oy3hxLKxBEyItIJeB64UVWLD7LPNcA1AP379w9bbD2TEnjkkrFcMHYrv3hpOd/55ydcdEw/7jhjOF0SW0nX1MCJaFIauz+bwT82jmTOss2s27b/KoHxMVEc0aszQ1M7c6Z8zCl5n1M8+S8kxXX0KWjTVlVV1/B57g7mrdzCvJVbWF9YCsDIvkn8dNIQTh3eixF9khDvn/AZo3pz6+ShrCvYxdycLczNyecPc1fzh7mrGZjS0UsiqRyV1mXvMU2WeZUrjc+9AwadAkm9m3f81lWumuro70Mv32rZW0xC1ZgrIscD96hqbcP2HQCq+ps6+8z19vlERGKAfCAFuL3uvvX2iwVeBeaq6p+aEktmZqZmZx9GY28LlVZU8ed5a3jsw6/plhjLL8/O4Nyj+jT/jzVMVJUVm4uZs2wzqdkPcknFLLIq/srAgYM5c1RvThnWk+27K1iVX8KqzcWs3lLCus2FPFv5E4q0I+dUPEC3jq5EMjS1M8NSOzM0NYkjenUiMa5V/kYxPikuq+S91QW8vXIL81cXULSnkrjoKMYPTmbS8F5MGtaTPl07NPl8+UVlvLUinzdy8vl03Xaqa5TUpAROH9GLySNSOXZAd2Kjm1hzX/gV/H28SxwX/bfppQZVeOrbruPJDYuhY3KT4/eLiCxU1cwDtocwccQAXwKTgI3A58AlqppTZ5/rgFGq+kMRuQg4X1W/IyIjgP/i2kn6AG8DQ4AaXJvIdlW9samx+JU4auVsKuKOF5axNK+IE49I4YHzRtKve2Ssgqeq5GxyyWLOss3kFpYSJXBe/zL+tOVKdp/4SzqecuvBT/DhQzDvHpaf+h8WMIrV+SWs2lLCl/kl7PEGSIpAevdEL6EkMcxLKul1q7tMu7dheylvr9zCvJVb+XRdIVU1SveOcZw8tCenZfRkwpAUOsW3/AfIztIK3l65lbk5+by/poCyyhq6JsYyaVgvJo/oxYlHpJAQG934ST7+Pzc7wrcfg1EXNO3CX74J/53m1qVpJcsnhz1xeBc9E/gzEA08rqoPiMh9QLaqzhaRBOA/uLaK7cBFqrrOO/bnwJW4nlQ3qurrIjIB+ABYhksiAHeq6pzG4vA7cQBU1ygzPsnlwbmrqVblp5OO4AcnDGj6r5wgqk0Wr3nJYn1hKdFRwvEDkzlzVG8mj+hFcqd4eGyyW8byus8a/lW1qwAeHgMDToCLn9nvpZoa5ZvtpazKL2F1fgmrtxSzKr+E3G27qZ3uKyE2iiE995VOhqUmMTS1MymdI2+NZRN8NTXK0o1FzFvhqqBWeVP5DErpyKkZvThteC/G9O8W0h8Xeyqqee/LAt7MyWfeyi0Ul1XRITaak45IYfLIXpwyrBddOjRQxVxTDY+d5rqgX7fg0OMwqivhb8cDCj/6pNX0pPIlcUSKSEgctTbt3MPds3N4a8UWhqV25jfnj2JM/9BPAqiqLNtYxGvLNvP6sny+2e6SxfhByZw1qjenj0ile8d6f8wLn3Q9SH7wDqQdfeBJX70ZFk2HH38KPYY0KY6yymrWbNnFqvxiL6GUsHJzCdvqDKBM7hi3t7pruJdMjujVmQ5xh/gVaCLenopqPlq7jXkrt/D2qq0UlJQTHSVkpnfjtIxeTBreiwE9/Gknq6yu4bN125mbk8/cnHy2lpQTEyUcPyiZ00ekMjmjFz2TEvYdsHUl/PNEGHY2TDvE+I5P/w5v3A4XP+vW/GglLHFESOKo9cbyfO6ZncOWkjIuG5fOzyYPpXNCcBvPVZWleUWuGmr5ZjZs30NMlDB+cA/OGpXK6RmpdKufLOoqK4IHj4Ax33XTTNe1daWr5z3majjz9y2OtXBXuavm8kooq/KL+XLLroaru3p1ZkBKR9KTOxJI7ki3xNiIbTcysLWkjHdWul5QH67dRlllDZ3iYzhpaAqnDe/FxKEpdE2MrF/gNTXKkrydzM3J582cLXy9bTciMKZf172N64EeHeH9P8A798OFT7lVLhuyuxD+bwz0GQuXvdiqelJZ4oiwxAFQUlbJg3NXM+PT9fTsHM+9545g8ojUFv0TVFW+qE0WyzaTt8Mli6zBPThrVG9Oy+jVeLKo7/kfuKkRblkNsXV+bT11gZv87YYlbv2CEKhf3VVbSskt3FfdBdA5Pob0HoleIkncm1ACyYmkdI63pBJmqsrqLSVeFdRWlmzYCUDfrh28UkVPjhuQTFxM65jxSFVZs3UXc5fnM3dFPss3uo6cQ3t15ozhyVy75moSyrchP/604e/Ca7dC9mPwo4+h5/AwR98yljgiMHHUWrJhJ3e8sIyVm4s5dXgv7ps6olk9RlSVJRt2eskin407XbKYMKQHZ47qzekZvQ7/F93at+Gp82HakzDiW/tvO/0BGH/94Z23BcqrqsnbsYf1hbvJ3Vbq7gvdfd6OPVTVySodYqNJT04kPTmRQHLHfcmlR0d6JyUQZY3zQVFRVcPnudt5y2uvyNvhBt4dldaFU4f34tSMXgxL7dwmknjejlLezNnCGzn5ZOduZxi5zI7/JcuTJ1Nx9iMcnV6nXWbrSvh7FmR+/8BSeytgiSOCEwe4+tXHP/yah+Z9SbQIt5w+lMvHBw7aMKiqLN6wkzlLN/P6cpcsYqOFCYNrk0VqcMaN1FTDQyMhdSRc+j/3/B8ToLLUNQrGRFZDdlV1DZt2lpFbuHu/hJJbWMo3haVUVO+bpC4uOop+3TvsSyh1Si19u3YgxoeOC5GmukYp3F3O1uJythSXsbXE3W8pLqegxN1vLSmjoKScGnXjeyYM7sGpGa7L7H5tAm1Q4a5y5q3cQuKHv+Wcoqe5ouI2lnU4llOG9WRor058K+cndN25nMofZ5PQpaff4TabJY4ITxy1Nmwv5RcvLee9LwsY1bcLvzl/FCP7dgFc1c1ir2Tx+rLNbCoqIy46ihO8ksWpGQfpAdJS8+6Fj/4CN6+AL99wDebfmeHmtWpFqmuU/OIy1m/bzfrtpS65bPPuC0v3tqcAxEQJfbt1qFf95e77de9AfEzrbqivmxC21iaA4nK2lJSxtU6C2LarguoGVr1M7hhHSud4eiUl0LNzPD2T4jkqrSsnDElpn50Yqsqp/scJVOwu4q6+j/HmV6UcXf4Zj8c9yH2Vl/FEzRn0Tko44AdKenJH0pMTI3ackyWOVpI4wJUmXlm6mfteyWH77gouHx9AEF5fvpnNXrI48Yh9ySIpyI3qB9i2Bv6aCSf9P8h+3K3s9/3XW1Uj36GoKgUl5eQWlu4trawvLGV9YSm523ZTUl61d18R6NOlA6ldEugQG01CbBTxMdHEe/cJsVEkxEYTH7P/fUKd1xu6j69zXFx01GFV69RPCK6kUJsQapNE0xNCr6QEeiXFk9LZ3fdMSiClU3yraZ8Iq7xs10V37OXoGb+j5pHjqaxR5p74Il/vqHB/S97fVf2JGHt2jvdKvokEenSkf3evarVHYui/342wxNGKEketotJKfvvGSp5ZsMFLFimcdWQqk4aHIVnU9+9TvanWFa5+B/o20D23jVJVdpRW7qv+8tpVthSXU15VTVllzX735ZU1lFVVU1l9+N8tEUioTSZ17htKUmWVNXuTRMGucksIfpr7c/jkrzDifDfp5yXPwRGTD9itaE8l3xSWsn777r0/TmoTy9aS/df26d4xrk4b3f73XUPco9ASRytMHLXydpTSpUNs0LvrNkv24/DqTTDqO/Dtf/kXRytSXaP7JZL9EkxlNWVV++7LKqsp956X13leVlm93+P9793+cTFR+yWEnknx9LSE4I+KUvhHllstcNAk+O7zzS6Zl1ZUeaXd2mSy7/Gmoj37TcyblBCzfwnFK7GkJyeS0qnlPQoPljgis2LN7CetWwRMTzLqO67KKsu3tbNanegoITEuhggbomBCKS4RzvsHvH4bTDm82W8T42IY3juJ4b2TDnitrLKavB2l5NZpm1u/vZRlG4t4fXn+fqXNxLho0pM78uy144JeQ2GJwzRNfCf3RTDGNK7/cXDteyE5dUJsNIN7dm5wevjK6ho27tizN6HkFu5m0849dA7C/F71WeIwxpg2IDY6ikCPjm5Ee4hZxacxxphmscRhjDGmWSxxGGOMaRZLHMYYY5rFEocxxphmscRhjDGmWSxxGGOMaRZLHMYYY5qlXcxVJSIFwPrDPLwHsC2I4bR29nnsY5/F/uzz2KetfBbpqppSf2O7SBwtISLZDU3y1V7Z57GPfRb7s89jn7b+WVhVlTHGmGaxxGGMMaZZLHEc2qN+BxBh7PPYxz6L/dnnsU+b/iysjcMYY0yzWInDGGNMs1jiMMYY0yyWOA5CRKaIyGoRWSsit/sdj59EpJ+IzBeRFSKSIyK2fiwgItEislhEXvU7Fj+JSFcRmSUiq0RkpYgc73dMfhKRm7zvyXIReUZEEvyOKdgscTRARKKBR4AzgAzgYhHJ8DcqX1UBt6hqBjAOuK6dfx61fgqs9DuICPAX4A1VHQYcRTv+TESkL3ADkKmqI4Fo4CJ/owo+SxwNOxZYq6rrVLUCmAlM9Tkm36jqZlVd5D0uwf1j6OtvVP4SkTTgLODffsfiJxHpApwIPAagqhWqutPfqHwXA3QQkRggEdjkczxBZ4mjYX2BDXWe59HO/1HWEpEAMAb4zN9IfPdn4Dagxu9AfDYAKACe8Krt/i0ioV/0OkKp6kbgQeAbYDNQpKpv+htV8FniME0mIp2A54EbVbXY73j8IiJnA1tVdaHfsUSAGGAs8HdVHQPsBtptm6CIdMPVTgwA+gAdReS7/kYVfJY4GrYR6FfneZq3rd0SkVhc0nhaVV/wOx6fZQHnikgurhrzFBF5yt+QfJMH5KlqbQl0Fi6RtFenAl+raoGqVgIvAON9jinoLHE07HNgiIgMEJE4XOPWbJ9j8o2ICK4Oe6Wq/snvePymqneoapqqBnB/G++oapv7VdkUqpoPbBCRod6mScAKH0Py2zfAOBFJ9L43k2iDnQVi/A4gEqlqlYhcD8zF9Yp4XFVzfA7LT1nAZcAyEVnibbtTVef4GJOJHD8BnvZ+ZK0Dvu9zPL5R1c9EZBawCNcbcTFtcPoRm3LEGGNMs1hVlTHGmGaxxGGMMaZZLHEYY4xpFkscxhhjmsUShzHGmGaxxGHMIYjILu8+ICKXBPncd9Z7/nEwz29MKFjiMKbpAkCzEoc30V1j9kscqtrmRhmbtscShzFN91vgBBFZ4q25EC0ifxCRz0VkqYhcCyAiE0XkAxGZjTeKWkReEpGF3joN13jbfoubRXWJiDztbast3Yh37uUiskxELqxz7nfrrH/xtDdCGRH5rbdmylIReTDsn45pN2zkuDFNdztwq6qeDeAlgCJVPUZE4oGPRKR2JtSxwEhV/dp7fqWqbheRDsDnIvK8qt4uIteruL+54QAAAX1JREFU6ugGrnU+MBq3vkUP75j3vdfGACNw03V/BGSJyErgW8AwVVUR6Rr0d2+Mx0ocxhy+04HvedOwfAYkA0O81xbUSRoAN4jIF8CnuAk0h9C4CcAzqlqtqluA94Bj6pw7T1VrgCW4KrQioAx4TETOB0pb/O6MOQhLHMYcPgF+oqqjvduAOmsv7N67k8hE3Kypx6vqUbj5i1qynGh5ncfVQIyqVuEWIJsFnA280YLzG9MoSxzGNF0J0LnO87nAj7wp5xGRIw6yiFEXYIeqlorIMNzyu7Uqa4+v5wPgQq8dJQW3yt6CgwXmrZXSxZt48iZcFZcxIWFtHMY03VKg2qtyehK31nYAWOQ1UBcA5zVw3BvAD712iNW46qpajwJLRWSRql5aZ/uLwPHAF4ACt6lqvpd4GtIZeFlEEnAloZsP7y0ac2g2O64xxphmsaoqY4wxzWKJwxhjTLNY4jDGGNMsljiMMcY0iyUOY4wxzWKJwxhjTLNY4jDGGNMs/x9UwY2SljCiJgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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85CeH/N4Hsta2YMlr2zoSQns7QmmxmD6+mk9dOIWzjxvJqbXD3XvI+qSBkyD280s/ny677DI+9alP8dxzz7Fr1y5GjBjBHXfcwfz586murmbOnDns2bPnoPY9Z84cHn74YU499VTuv/9+nnrqqUOKtX1YcQ8pfnAy2xF+/8oGtqTtCMePreQDb5zAOceN5MxJIxgyaOD869mRy9/Sw2Do0KGcf/75fPjDH2b27Nls27aNIUOGMHz4cNauXcujjz7a7X0gAM4991zmzJnDTTfdREtLCz/72c/46Ec/CsD27ds56qijaG5u5vvf/37H0OGVlZVs3759n31NnTqVFStWsGzZMo477jgeeOAB3vKWt+TlfQ8Em3c28cwrG/ndsvX8btkGXt2UjFA6dlg5F54whnOOG8mbj6thdGV5gSM1O3BOEIfJ7Nmzufzyy5k7dy7HH38806dP5/jjj+eYY47h7LPP7rHsjBkzeP/738+pp57K6NGjOeOMMzrWfelLX+Kss85i1KhRnHXWWR1JYdasWVx99dV885vf7GicBigvL+e+++7jve99Ly0tLZxxxhl87GMfy8+b7ofa2xGeXrae3y/bwOLXtnVqR/inc451O4L1Gx7u2w7YQPpc29sR2hPC/BWbacpoRzjnuJGcM3kkp4xzO4IdmTzctw14EcGOxha27GpOHrub2LyrmS27mtiyq5nNu5rYmj5v2Z1us6uJrbubO+5lcPzYSj7odgQbQPL6DZc0E/gGyT2pvxMRt3dZPwG4FxgFbAI+EBEN6bqrgM+nm345Ir6bz1jtyLG7qTU5kLcf4Hc3d57f1bz34L9777LM4Si6qhxUwvCKUqoryqiqKKW2uoKqwaVUV5Ry7KihbkewASlvCUJSMXAncBHQAMyXNC8ilmRsdgfwvYj4rqS3Al8FPihpBHALUAcEsCAtu/lA44gInwvuRfk+JbmnuZVFq7eyYOVm/rZh595f9xlJIHPIia4GlxZTVVFKVUUZVYNLmTJmaMd0dUVZpyRQnW43fHCpb2JjlkU+axBnAssiYjmApLnAZUBmgpgG/HM6/STwcDp9MfBERGxKyz4BzAQePJAAysvL2bhxIzU1NU4SvSAi2LhxI+XlvfdL+rUtu1mwcjPPrdrMc6u2sOS1rTS3Jklo5NBBjBhSStXgMsaPqODU2qq9B//0AD98cBnVQ9KD/+BS36TGrBflM0GMA17NmG8AzuqyzZ+BK0hOQ10OVEqq6absuK4vIOka4BqA8ePH7xNAbW0tDQ0NrF+//uDfhXVSXl5ObW3tQZVtbGll0eptPL8qTQgrt7BmW3L9R3lpEafUVvGRc45lxvgqZkyoZuTQQb0ZupkdoEK3sn0G+HdJc4DfAquB1h5LZIiIu4G7IenF1HV9aWkpkyZN6p1I7YCt2bonTQRJQli0elvHvQlqqwdz5qQRHcnghKOG+TSPWR+TzwSxGjgmY742XdYhIl4jqUEgaSjw7ojYImk1cF6Xsk/lMVY7RE0tbSx5fVtHMnhu5WZe25rUDspKijhl3HDmnD2RGeOrmTG+itHD3OBr1tflM0HMByZLmkSSGGYBV2ZuIGkksCki2oCbSHo0ATwO/Iuk6nT+bel66yPWbd/Dcyu3dCSDv6ze2tF4fPTwcqZPqOafxlczY0I1044a5ttVmh2B8pYgIqJF0rUkB/ti4N6IWCzpNqA+IuaR1BK+KilITjF9Ii27SdKXSJIMwG3tDdZ2+DW3tvHS69tZsHITz61KkkLD5mRIibLiIk4cN4wPvHECp0+oZsb4asYOd+3ArD/o11dS28HZsKMxPVWUJIMXGrZ03Nx+zLBBHYlg+vhqTho3jEEl7jlkdqTyldTWrZbWNpau3Z4kg7T9YOXGXQCUFIkTxw1n9pnjk7aDCdUcPbzcXYbNBggniAFm884mnn91c0f7wcJXt7CrKek4NnLoIE6fUMWVZ45nxoRqTh433NcVmA1gThD9WFtb8PK6HTy3anPHxWjL1+8EoLhInHBUJe85vbbjlFFt9WDXDsysgxNEP7J1dzMLX917qmjhqi1sb0xu+lNdUcrpE6p594xaZoyv5tRjhlNR5j+/mXXPR4gjVFtbsHzDzk4Xor28bgcRUCSYMqaSd512NKenbQcTaypcOzCzA+IEcYTY0djCnzNqB8+t2sLW3cntLIeVlzBjQjWXnHJ0R+2gsry0wBGb2ZHOCaIPighWbtyV0XawhaVrtnXcl2Dy6KG8/aSxac+iKo4dOZSiItcOzKx3OUH0ARHBsnU7+OWL61iwcjPPr9rMxp1NAAwdVML08VVc9NbJzBhfxfRjqhle4dqBmeWfE0SBRASLVm/j0UWv89jiNR29i44dOYTzpo5OehZNqGLy6EqKXTswswJwgjiMWtuC51Zt5rFFa3hs0RpWb9lNcZF407E1/OPZk3jbtDGM8SB2ZtZHOEHkWXNrG39cvpHHFq3hv5asZf32RsqKi/i7ySO54cLJXHjCGKqHlBU6TDOzfThB5MGe5laefnkDjy1awy9fXMvW3c1UlBVz/tTRXHzSWM6fOsq9jMysz3OC6CU7Glt4auk6Hl20hqdeWsfOplaGlZdw4bQxzDxxLOdOGeVhK8zsiOIEcQi27Grily+u47FFr/PblzfQ1NLGyKFlXDZ9HDNPHMub3lDju6SZ2RHLCeIArdu+h/9avJbHF6/hD69spKUtOHp4Of9w1njeftJRnD6h2r2OzKxfcILIQcPmXTy2aA2PL15D/crNRMCkkUO4+txjeftJYzl53HAPY2Fm/Y4TRDdeWb+jozvqX1ZvBeCEo4ZxwwVTmHnSWKaMGeqkYGb9mhNEKiJY8vo2Hl+0hkcXreHldTsAOO2YKm56+/FcfOJYJo4cUuAozcwOn7wmCEkzgW+Q3JP6OxFxe5f144HvAlXpNjdGxCOSJgIvAkvTTf8YER/LR4wbdzTyrd8u57FFa1i1aRdFgjMnjeAfzprGxSeN5ajhg/PxsmZmfV7eEoSkYuBO4CKgAZgvaV5ELMnY7PPADyPiLknTgEeAiem6VyLitHzF166spIjv/3ElZ0wawX877w1cNG0MNUMH5ftlzcz6vHzWIM4ElkXEcgBJc4HLgMwEEcCwdHo48Foe48mqsryUBV+4yNcomJl1kc9O+uOAVzPmG9JlmW4FPiCpgaT2cF3GukmSnpf0G0l/l+0FJF0jqV5S/fr16w86UCcHM7N97TdBSKrJ4+vPBu6PiFrgHcADkoqA14HxETEd+GfgB5KGdS0cEXdHRF1E1I0aNSqPYZqZDTy51CD+KOlHkt6hA+vXuRo4JmO+Nl2W6SPADwEi4g9AOTAyIhojYmO6fAHwCjDlAF7bzMwOUS4JYgpwN/BB4GVJ/yIpl4P1fGCypEmSyoBZwLwu26wCLgCQdAJJglgvaVTayI2kY4HJwPJc3pCZmfWO/SaISDwREbOBq4GrgGfTtoE39VCuBbgWeJyky+oPI2KxpNskXZpu9mngakl/Bh4E5kREAOcCL0haCDwEfCwiNh3C+zQzswOk5HjcwwZJG8QHSGoQa4F7SGoCpwE/iohJ+Q4yF3V1dVFfX1/oMMzMjiiSFkREXbZ1uXRz/QPwAPD3EdGQsbxe0v/XGwGamVnfk0uCmBrdVDMi4n/1cjxmZtZH5NJI/V+SqtpnJFVLejyPMZmZWR+QS4IYFRFb2mciYjMwOn8hmZlZX5BLgmhNB9UDQNIEkiEyzMysH8ulDeJ/AL+T9BtAwN8B1+Q1KjMzK7j9JoiIeEzSDOCN6aIbImJDfsMyM7NCy3U011ZgHcmVztMkERG/zV9YZmZWaPtNEJL+CbieZCylhSQ1iT8Ab81vaGZmVki5NFJfD5wBrIyI84HpwJaei5iZ2ZEulwSxJyL2AEgaFBEvAVPzG5aZmRVaLm0QDemFcg8DT0jaDKzMb1hmZlZoufRiujydvFXSkyS3Bn0sr1GZmVnB9Zgg0nsyLI6I4wEi4jeHJSozMyu4HtsgIqIVWJp5JbWZmQ0MubRBVAOLJT0L7GxfGBGXdl/EzMyOdLkkiC/kPQozM+tzcmmkPuh2B0kzgW8AxcB3IuL2LuvHA98FqtJtboyIR9J1NwEfIbmK+5MR4SHGzcwOo1yupN7O3tFby4BSYGdEDNtPuWLgTuAioAGYL2leRCzJ2OzzJPeqvkvSNOARYGI6PQs4ETga+KWkKWmbiJmZHQb7vVAuIiojYliaEAYD7wb+I4d9nwksi4jlEdEEzAUu67p7oD3RDAdeS6cvA+ZGRGNE/A1Ylu7PzMwOk1yupO4QiYeBi3PYfBzwasZ8Q7os063AByQ1kNQerjuAsmZmlke5nGK6ImO2CKgD9vTS688G7o+I/y3pTcADkk7KtbCka0jvTTF+vHvimpn1plx6Mb0rY7oFWMG+p4qyWQ0ckzFfmy7L9BFgJkBE/EFSOTAyx7JExN3A3QB1dXW+y52ZWS/KpRfTPx7kvucDkyVNIjm4zwKu7LLNKuAC4H5JJ5Dcb2I9MA/4gaR/I2mkngw8e5BxmJnZQdhvG4Sk76aD9bXPV0u6d3/lIqIFuBZ4HHiRpLfSYkm3SWq/yO7TwNWS/gw8CMxJ2zkWAz8ElpCM+/QJ92AyMzu8FNHzmRlJz0fE9P0tK7S6urqor68vdBhmZkcUSQsioi7bulx6MRVJqs7Y2Qhyv1WpmZkdoXI50P9v4A+SfpTOvxf4Sv5CMjOzviCXRurvSapn7z2or+hyNbSZmfVDuVwH8UaSe0L8ezo/TNJZEfGnvEdnZmYFk0sbxF3Ajoz5HekyMzPrx3JJEIqMrk4R0YYbqc3M+r1cEsRySZ+UVJo+rgeW5zswMzMrrFwSxMeAN5NcDd0AnAVcnc+gzMys8HLpxbSOZJgMACQNBi4BftRtITMzO+LlNNy3pGJJ75D0APA34P35DcvMzAqtxxqEpLeQDLD3DpLB8s4Gjo2IXYchNjMzK6BuE0R6E59VJF1aPxMR2yX9zcnBzGxg6OkU00MkQ22/H3iXpCHsvTe1mZn1c90miIi4AZhEMhbTecBSYJSk90kaenjCMzOzQumxkTq9N8OTEXENSbKYTXI3uRWHITYzMyugnK+Ijohm4OfAz9OurmZm1o/l1M21q4jY3duBmJlZ33JQCcLMzPq/vCYISTMlLZW0TNKNWdb/H0kL08dfJW3JWNeasW5ePuM0M7N95XI/iJ+xb/fWrUA98K2I2NNNuWLgTuAikjGc5kual3mzoYj4VMb21wGZ97neHRGn5fpGzMysd+U0mivJPSC+nT62AduBKel8d84ElkXE8ohoAuaS9IDqzmzgwVyCNjOz/MulF9ObI+KMjPmfSZofEWdIWtxDuXHAqxnz7SPB7kPSBJJutL/OWFye3uq0Bbg9Ih7OUu4a4BqA8ePH5/BWzMwsV7nUIIZK6jj6ptPtF8o19VIcs4CHIqI1Y9mEiKgjGQvq65Le0LVQRNwdEXURUTdq1KheCsXMzCC3GsSngd9JegUQyS/9/5YOvfHdHsqtBo7JmK9Nl2UzC/hE5oKIWJ0+L5f0FEn7xCs5xGtmZr0gl/tBPCJpMnB8umhpRsP013soOh+YLGkSSWKYRVIb6ETS8UA18IeMZdXAroholDSSZBTZr+XwfszMrJfkeiX16cDEdPtTJRER3+upQES0SLoWeBwoBu6NiMWSbgPqI6K96+osYG7mfa+BE4BvSWojOQ12e2bvJzMzyz91Pi5n2SC5SdAbgIVAextBRMQn8xzbAamrq4v6+vpCh2FmdkSRtCBt791HLjWIOmBa7C+TmJlZv5JLL6ZFwNh8B2JmZn1LLjWIkcASSc8Cje0LI+LSvEVlZmYFl0uCuDXfQZiZWd+TSzfX3xyOQMzMrG/pNkFI+l1EnCNpO50H6xNJL6ZheY/OzMwKptsEERHnpM+Vhy8cMzPrK3K6UC4duntM5vYRsSpfQZmZWeHlcj+I64BbgLVAW7o4gFPyGJeZmRVYLjWI64GpEbEx38GYmVnfkcuFcq+S3EHOzMwGkFxqEMuBpyT9gs4Xyv1b3qIyM7OCyyVBrEofZenDzMwGgFwulPufhyMQMzPrW3q6UO7rEXGDpJ/R+UI5wGMxmZn1d7SCD6gAAA3uSURBVD3VIB5In+84HIGYmVnf0tOV1AvSZ4/FZGY2AOVyodxk4KvANKC8fXlEHJvHuMzMrMByuQ7iPuAuoAU4H/ge8J+57FzSTElLJS2TdGOW9f9H0sL08VdJWzLWXSXp5fRxVW5vx8zMeksu3VwHR8SvJCkiVgK3SloAfLGnQun4TXcCFwENwHxJ8yJiSfs2EfGpjO2vA6an0yNIhveoI2kgX5CW3Xxgb8/MzA5WLjWIRklFwMuSrpV0OTA0h3JnAssiYnlENAFzgct62H428GA6fTHwRERsSpPCE8DMHF7TzMx6SS4J4nqgAvgkcDrwASCXUz7jSIbpaNeQLtuHpAnAJODXB1JW0jWS6iXVr1+/PoeQzMwsVz0miPQ00fsjYkdENETEP0bEuyPij70cxyzgoYhoPZBCEXF3RNRFRN2oUaN6OSQzs4Gt2wQhqSQ9YJ9zkPteDRyTMV+bLstmFntPLx1oWTMzy4OeGqmfBWYAz0uaB/wI2Nm+MiJ+sp99zwcmS5pEcnCfBVzZdSNJxwPVwB8yFj8O/Iuk6nT+bcBN+3k9MzPrRbn0YioHNgJvJelRpPS5xwQRES2SriU52BcD90bEYkm3AfURMS/ddBYwNyIio+wmSV8iSTIAt0XEpgN4X2ZmdoiUcVzuvEJqAP6NvQlBGaujrw33XVdXF/X19YUOw8zsiCJpQUTUZVvXUw2imKQ7q7Ksy55VzMys3+gpQbweEbcdtkjMzKxP6amba7aag5mZDRA9JYgLDlsUZmbW53SbINxryMxsYMtlqA0zMxuAnCDMzCwrJwgzM8vKCcLMzLJygjAzs6ycIMzMLCsnCDMzy8oJwszMsnKCMDOzrJwgzMwsKycIMzPLygnCzMyycoIwM7Os8pogJM2UtFTSMkk3drPN+yQtkbRY0g8ylrdKWpg+5mUra2Zm+dPTHeUOiaRi4E7gIqABmC9pXkQsydhmMnATcHZEbJY0OmMXuyPitHzFZ2ZmPctnDeJMYFlELI+IJmAucFmXba4G7oyIzQARsS6P8ZiZ2QHIZ4IYB7yaMd+QLss0BZgi6feS/ihpZsa6ckn16fK/z/YCkq5Jt6lfv35970ZvZjbA5e0U0wG8/mTgPKAW+K2kkyNiCzAhIlZLOhb4taS/RMQrmYUj4m7gboC6uro4vKGbmfVv+axBrAaOyZivTZdlagDmRURzRPwN+CtJwiAiVqfPy4GngOl5jNXMzLrIZ4KYD0yWNElSGTAL6Nob6WGS2gOSRpKcclouqVrSoIzlZwNLMDOzwyZvp5giokXStcDjQDFwb0QslnQbUB8R89J1b5O0BGgFPhsRGyW9GfiWpDaSJHZ7Zu8nMzPLP0X0j1P3dXV1UV9fX+gwzMyOKJIWRERdtnW+ktrMzLJygjAzs6ycIMzMLCsnCDMzy8oJwszMsnKCMDOzrJwgzMwsKycIMzPLygnCzMyycoIwM7OsnCDMzCwrJwgzM8vKCcLMzLJygjAzs6ycIMzMLCsnCDMzy8oJwszMsnKCMDOzrPKaICTNlLRU0jJJN3azzfskLZG0WNIPMpZfJenl9HFVPuM0M7N9leRrx5KKgTuBi4AGYL6keRGxJGObycBNwNkRsVnS6HT5COAWoA4IYEFadnO+4jUzs87yWYM4E1gWEcsjogmYC1zWZZurgTvbD/wRsS5dfjHwRERsStc9AczMY6xmZtZFPhPEOODVjPmGdFmmKcAUSb+X9EdJMw+gLJKukVQvqX79+vW9GLqZmRW6kboEmAycB8wGvi2pKtfCEXF3RNRFRN2oUaPyFKKZ2cCUzwSxGjgmY742XZapAZgXEc0R8TfgryQJI5eyZmaWR/lMEPOByZImSSoDZgHzumzzMEntAUkjSU45LQceB94mqVpSNfC2dJmZmR0meevFFBEtkq4lObAXA/dGxGJJtwH1ETGPvYlgCdAKfDYiNgJI+hJJkgG4LSI25StWMzPblyKi0DH0irq6uqivry90GGZmRxRJCyKiLtu6vNUgjhi7NsE3T4OiUigug+LS9JFOH87lRSUQrdDaAm0t0NacPGfOtzZDW2vGuq7zPZTd33z7NMCgoTBoGJQPS547TVfunc5cX1JW2L9lPrQ2Q7SBijIeKnRU3YtI/oYtjdDalDzap1saobUxeU8Hvawl+a6WDoaS8uS5dDCUDIbS8h6eyzuXKSnv259ju7bW9HNM//dam/b+H7Y2JeuLSqCoOPludEwX753umE+XqRiKCt0/KDdOEEUlcOrsnr8E7f8ojdu7LG/Z+0/YljEdbYV+V8mXsD3ptD8y57tbVzIICNi1ETavgD3boHEbtOzZ/2uWlCfJoyNxtE8P3zexdEwP77y8bEj3B46I5O/Qshua9/TwvAead3f/3Lw797LRmu3D3Zss2g8M2R49rTuYsh0H/mwH74xkQC+eFSgqgeJBSfIvHpR8T1qb9n5mrU0Hv++SbhJHR9LpZl1JefI9bf9/bWve//9vx/KM/9Pu/n/bl7c15/F/WfsmkswEk5lIOqZLkvlOZdLEM/oEeMe/9nqUThDlw+Dt/6t399nW2s0Xcj9f4LbmzgftXA/q2eZ7+9dZS1OSIBu3pklje5I4Oqa37k0mjdv3Tu9c3nn5/g5eKkoTy/Dk/XQ9YB/swa+opJtftRVQVgEVNd3/Ai4qSg4UEcnfNtrSR+Z013Vd1re1Hdy6jv2m343yYRkH7LLOB+/eXlZctv9fum2tPSfjg33etSn7uu5+qBSXpbXxHGrupYOTHyM91uhL0uey9H+rLPvyouK0Bt+a/N3aWpP/+Y7p9uUtXbbpbrv9lN9nu7ZkuqXx4P4v9sMJIh/aq5Wl5YWOpPeUlEFJDQypOfh9tLVB044uiWUb7Nm6b2Jp3J4kz6ynKCr2czojy3Oxv+p5UVScno4cenheLyJJEq1NexNCPn4QGeAEYYdTUVHyC7h8GAwvdDB2RJL2noKyvDsyWkrMzOywc4IwM7OsnCDMzCwrJwgzM8vKCcLMzLJygjAzs6ycIMzMLCsnCDMzy6rfjOYqaT2w8hB2MRLY0EvhHOn8WXTmz6Mzfx579YfPYkJEZL0lZ79JEIdKUn13Q94ONP4sOvPn0Zk/j736+2fhU0xmZpaVE4SZmWXlBLHX3YUOoA/xZ9GZP4/O/Hns1a8/C7dBmJlZVq5BmJlZVk4QZmaW1YBPEJJmSloqaZmkGwsdTyFJOkbSk5KWSFos6fpCx1RokoolPS/p54WOpdAkVUl6SNJLkl6U9KZCx1RIkj6V/p8skvSgpH50C8nEgE4QkoqBO4G3A9OA2ZKmFTaqgmoBPh0R04A3Ap8Y4J8HwPXAi4UOoo/4BvBYRBwPnMoA/lwkjQM+CdRFxElAMTCrsFH1vgGdIIAzgWURsTwimoC5wGUFjqlgIuL1iHgund5OcgAYV9ioCkdSLfBO4DuFjqXQJA0HzgXuAYiIpojYUtioCq4EGCypBKgAXitwPL1uoCeIccCrGfMNDOADYiZJE4HpwJ8KG0lBfR34HNBW6ED6gEnAeuC+9JTbdyQNKXRQhRIRq4E7gFXA68DWiPivwkbV+wZ6grAsJA0FfgzcEBHbCh1PIUi6BFgXEQsKHUsfUQLMAO6KiOnATmDAttlJqiY52zAJOBoYIukDhY2q9w30BLEaOCZjvjZdNmBJKiVJDt+PiJ8UOp4COhu4VNIKklOPb5X0n4UNqaAagIaIaK9RPkSSMAaqC4G/RcT6iGgGfgK8ucAx9bqBniDmA5MlTZJURtLINK/AMRWMJJGcY34xIv6t0PEUUkTcFBG1ETGR5Hvx64jod78QcxURa4BXJU1NF10ALClgSIW2CnijpIr0/+YC+mGjfUmhAyikiGiRdC3wOEkvhHsjYnGBwyqks4EPAn+RtDBddnNEPFLAmKzvuA74fvpjajnwjwWOp2Ai4k+SHgKeI+n99zz9cNgND7VhZmZZDfRTTGZm1g0nCDMzy8oJwszMsnKCMDOzrJwgzMwsKycIs5SkHenzRElX9vK+b+4y/0xv7t8sH5wgzPY1ETigBJEO2NaTTgkiIvrdVbfW/zhBmO3rduDvJC1Mx/wvlvSvkuZLekHSRwEknSfpaUnzSK8qlvSwpAXpfQKuSZfdTjLq50JJ30+XtddWlO57kaS/SHp/xr6fyrj/wvfTK3aRdHt6z44XJN1x2D8dGzAG9JXUZt24EfhMRFwCkB7ot0bEGZIGAb+X1D5y5wzgpIj4Wzr/4YjYJGkwMF/SjyPiRknXRsRpWV7rCuA0kvsrjEzL/DZdNx04kWQY6d8DZ0t6EbgcOD4iQlJVr797s5RrEGb79zbgQ+nwI38CaoDJ6bpnM5IDwCcl/Rn4I8lAkJPp2TnAgxHRGhFrgd8AZ2TsuyEi2oCFJKe+tgJ7gHskXQHsOuR3Z9YNJwiz/RNwXUSclj4mZYz9v7NjI+k8klE+3xQRp5KMz3Mot6FszJhuBUoiooXkRlcPAZcAjx3C/s165ARhtq/tQGXG/OPAx9Oh0JE0pZub5QwHNkfELknHk9y2tV1ze/kungben7ZzjCK5a9uz3QWW3qtjeDqA4qdITk2Z5YXbIMz29QLQmp4qup/kXswTgefShuL1wN9nKfcY8LG0nWApyWmmdncDL0h6LiL+IWP5T4E3AX8GAvhcRKxJE0w2lcD/L6mcpGbzzwf3Fs32z6O5mplZVj7FZGZmWTlBmJlZVk4QZmaWlROEmZll5QRhZmZZOUGYmVlWThBmZpbV/wMaDwJxW+eQ9QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.9515831272423282\n",
            "Final Validation Accuracy: 0.5851212036779047\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# apr3 3rd\n",
        "new model add conv"
      ],
      "metadata": {
        "id": "_GeDmfRQU11M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Modified from Tutorial 3a and lecture notes\n",
        "class CNNalex2(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNNalex2, self).__init__()\n",
        "        # nn.Conv2d(256,300,5,2)\n",
        "        self.conv1 = nn.Conv2d(256,300,3, padding = 2) # Modified to 3, in_channels, out_chanels, kernel_size\n",
        "        self.conv2 = nn.Conv2d(300,300,3)\n",
        "        self.conv3 = nn.Conv2d(300,300,1)\n",
        "\n",
        "        self.pool = nn.MaxPool2d(2, 2) #kernel_size, stride \n",
        "        self.fc1 = nn.Linear(300*2*2, 32) # Modified first parameter\n",
        "        self.fc2 = nn.Linear(32, 7)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x))) # 4\n",
        "        x = (F.relu(self.conv2(x))) #2\n",
        "        x = (F.relu(self.conv3(x)))\n",
        "        x = x.view(-1, 300*2*2) # Modified size\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "5fBoVeI2U018"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "3UN-PUZvxRpV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "use_cuda = True\n",
        "model6 = CNNalex2()\n",
        "\n",
        "if use_cuda and torch.cuda.is_available():\n",
        "  model6.cuda()\n",
        "  print('CUDA is available!  Training on GPU ...')\n",
        "else:\n",
        "  print('CUDA is not available.  Training on CPU ...')\n",
        "\n",
        "train2(model6, tData, vData, batch_size = 256, lr= 0.01, num_epochs=10) #weightdecaty = 5e-3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "nrW8uFM_U9jD",
        "outputId": "4ea01535-929b-4cab-9b52-e12ebee2a3fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA is available!  Training on GPU ...\n",
            "Epoch 0\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-55-545350fd7684>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CUDA is not available.  Training on CPU ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mtrain2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#weightdecaty = 5e-3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-52-8be66b805c4b>\u001b[0m in \u001b[0;36mtrain2\u001b[0;34m(model, train_data, val_data, batch_size, num_epochs, lr, weight_decay)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Epoch\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mimgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0;31m# print(\"iteration \", n)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1201\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m                 \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1203\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1205\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1227\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1228\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExceptionWrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1229\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_utils.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m# instantiate since we don't know how to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: Caught FileNotFoundError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py\", line 287, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\", line 49, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\", line 49, in <listcomp>\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"/usr/local/lib/python3.7/dist-packages/torchvision/datasets/folder.py\", line 232, in __getitem__\n    sample = self.loader(path)\n  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 594, in load\n    with _open_file_like(f, 'rb') as opened_file:\n  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 230, in _open_file_like\n    return _open_file(name_or_buffer, mode)\n  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 211, in __init__\n    super(_open_file, self).__init__(open(name, mode))\nFileNotFoundError: [Errno 2] No such file or directory: '/content/drive/MyDrive/APS360 Team/aps360_dataset/FER_alexnet_big/train/happy/14616.tensor'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(model6, tData, vData, batch_size = 256, lr= 0.001, num_epochs=10,weight_decay=1e-3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qwYgSkLqZwkt",
        "outputId": "c5bfca04-cef8-4916-ed5c-80bc6af39a78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  0  Train Accuracy:  0.784910655195235\n",
            "Epoch:  0  Validation Accuracy:  0.5926441905823349\n",
            "Epoch 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  1  Train Accuracy:  0.8085966073356787\n",
            "Epoch:  1  Validation Accuracy:  0.588464753413207\n",
            "Epoch 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  2  Train Accuracy:  0.8245149604653593\n",
            "Epoch:  2  Validation Accuracy:  0.5906937865700752\n",
            "Epoch 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  3  Train Accuracy:  0.84241875370093\n",
            "Epoch:  3  Validation Accuracy:  0.5890220117024241\n",
            "Epoch 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  4  Train Accuracy:  0.8500470235814553\n",
            "Epoch:  4  Validation Accuracy:  0.5923655614377263\n",
            "Epoch 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  5  Train Accuracy:  0.8633181232366157\n",
            "Epoch:  5  Validation Accuracy:  0.5881861242685985\n",
            "Epoch 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  6  Train Accuracy:  0.8872827336375353\n",
            "Epoch:  6  Validation Accuracy:  0.5856784619671218\n",
            "Epoch 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  7  Train Accuracy:  0.9021212860078721\n",
            "Epoch:  7  Validation Accuracy:  0.5789913624965172\n",
            "Epoch 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  8  Train Accuracy:  0.9109338534954196\n",
            "Epoch:  8  Validation Accuracy:  0.5851212036779047\n",
            "Epoch 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fe5bb1ca950>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:  9  Train Accuracy:  0.924135288585461\n",
            "Epoch:  9  Validation Accuracy:  0.5834494288102535\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.924135288585461\n",
            "Final Validation Accuracy: 0.5834494288102535\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# apr 4 new model resnet-6"
      ],
      "metadata": {
        "id": "GiOMfHFkeyCI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import ssl\n",
        "ssl._create_default_https_context = ssl._create_unverified_context"
      ],
      "metadata": {
        "id": "5Fjis_Crl8js"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resnet18new2 = torchvision.models.resnet.resnet18(pretrained=False)"
      ],
      "metadata": {
        "id": "_RCpOTeYe-9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_feature = resnet18new2.fc.in_features\n",
        "resnet18new2.fc=nn.Linear(num_feature, 6)"
      ],
      "metadata": {
        "id": "QUbzZ56roTTd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parent_path = '/content/drive/MyDrive/APS360 Team/Alex_Uploads/FER-2013_Images/FER-2013/'\n",
        "\n",
        "data_transform = transforms.Compose(\n",
        "    [\n",
        "        transforms.Resize((224,224)), \n",
        "        #convert image to grayscale\n",
        "        # transforms.Grayscale(num_output_channels=3),\n",
        "        #randomly adding brightness \n",
        "        # transforms.ColorJitter(brightness=(1,1.2)),\n",
        "        #apply ramdom rotation from -30 to 30 degrees\n",
        "        # transforms.RandomRotation(30),\n",
        "        #apply random horizontal flip with probability of 50% \n",
        "        transforms.RandomHorizontalFlip(p=0.5),\n",
        "        transforms.ToTensor(),\n",
        "        #normalize the data with 0.5 mean and variance.\n",
        "        transforms.Normalize((0.5073955,), (0.25,), inplace=True)\n",
        "       \n",
        "    ])\n",
        "training_set = torchvision.datasets.ImageFolder(parent_path+'train',data_transform)\n",
        "valid_set = torchvision.datasets.ImageFolder(parent_path+'valid',data_transform)\n",
        "\n",
        "# train_data = datasets.DatasetFolder(parent_path+'train', loader=torch.load, extensions=('.tensor'))\n",
        "# val_data = datasets.DatasetFolder(parent_path+'val', loader=torch.load, extensions=('.tensor'))\n",
        "# train_data = train_data.view(3,224,224)\n",
        "# val_data = val_data.view(3,224,224)"
      ],
      "metadata": {
        "id": "KODmtc4ipGLt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# torch.cuda.empty_cache()\n",
        "\n"
      ],
      "metadata": {
        "id": "SUGDigt2xupi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "use_cuda = True\n",
        "\n",
        "\n",
        "if use_cuda and torch.cuda.is_available():\n",
        "  resnet18new2.cuda()\n",
        "  print('CUDA is available!  Training on GPU ...')\n",
        "else:\n",
        "  print('CUDA is not available.  Training on CPU ...')\n",
        "\n",
        "train2(resnet18new2, training_set, valid_set, batch_size = 128, lr= 0.01, num_epochs=5,weight_decay=5e-4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 886
        },
        "id": "SglZ-IcaomiF",
        "outputId": "aaca56e7-8555-4431-dd5d-a5838ed1be9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA is available!  Training on GPU ...\n",
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.3560422589589695\n",
            "Epoch:  0  Validation Accuracy:  0.34521788341822296\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.4743252255098101\n",
            "Epoch:  1  Validation Accuracy:  0.4592529711375212\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.5351514513355095\n",
            "Epoch:  2  Validation Accuracy:  0.5036785512167515\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.5678986346565582\n",
            "Epoch:  3  Validation Accuracy:  0.5220713073005093\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.6276718963883332\n",
            "Epoch:  4  Validation Accuracy:  0.5636672325976231\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.6276718963883332\n",
            "Final Validation Accuracy: 0.5636672325976231\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/APS360 Team/model checkpoint/res-6.pth'\n",
        "torch.save(resnet18new2.state_dict(), path)"
      ],
      "metadata": {
        "id": "dCFhRLwNDzcB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train2(resnet18new2, training_set, valid_set, batch_size = 128, lr= 0.001, num_epochs=5,weight_decay=5e-4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 868
        },
        "id": "OktB_t86ERJ2",
        "outputId": "c3dc94a4-5587-4583-e286-ff844c51edfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.7012389877505177\n",
            "Epoch:  0  Validation Accuracy:  0.5987549518958687\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.720157242638026\n",
            "Epoch:  1  Validation Accuracy:  0.6024335031126202\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.7433224527043628\n",
            "Epoch:  2  Validation Accuracy:  0.6032823995472553\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.7625565968200484\n",
            "Epoch:  3  Validation Accuracy:  0.5945104697226938\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.7875469446491875\n",
            "Epoch:  4  Validation Accuracy:  0.6038483305036786\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.7875469446491875\n",
            "Final Validation Accuracy: 0.6038483305036786\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(resnet18new2, training_set, valid_set, batch_size = 128, lr= 0.0001, num_epochs=10,weight_decay=5e-4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pQjA-q17ZgxL",
        "outputId": "55494235-f0fb-45df-e5fc-724b75b4ee4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.8013056754764663\n",
            "Epoch:  0  Validation Accuracy:  0.6058290888511602\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.8063950019304341\n",
            "Epoch:  1  Validation Accuracy:  0.6097906055461234\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.809448597802815\n",
            "Epoch:  2  Validation Accuracy:  0.6061120543293718\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.8129935769190271\n",
            "Epoch:  3  Validation Accuracy:  0.6049801924165252\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.8155557895475765\n",
            "Epoch:  4  Validation Accuracy:  0.6055461233729486\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.8199782387420589\n",
            "Epoch:  5  Validation Accuracy:  0.601018675721562\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.8220841669299077\n",
            "Epoch:  6  Validation Accuracy:  0.6013016411997736\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.8269278017619599\n",
            "Epoch:  7  Validation Accuracy:  0.5979060554612338\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.8265417149275209\n",
            "Epoch:  8  Validation Accuracy:  0.6058290888511602\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.8334210803411604\n",
            "Epoch:  9  Validation Accuracy:  0.6097906055461234\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.8334210803411604\n",
            "Final Validation Accuracy: 0.6097906055461234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train2(resnet18new2, training_set, valid_set, batch_size = 128, lr= 0.0001, num_epochs=10,weight_decay=5e-4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QSk3kjfCkhHo",
        "outputId": "13a03da0-07d7-4fff-ba42-13e248ae9467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Epoch:  0  Train Accuracy:  0.9815380295531922\n",
            "Epoch:  0  Validation Accuracy:  0.6969439728353141\n",
            "Epoch 1\n",
            "Epoch:  1  Train Accuracy:  0.9818188199782387\n",
            "Epoch:  1  Validation Accuracy:  0.6949632144878325\n",
            "Epoch 2\n",
            "Epoch:  2  Train Accuracy:  0.9828717840721631\n",
            "Epoch:  2  Validation Accuracy:  0.6963780418788907\n",
            "Epoch 3\n",
            "Epoch:  3  Train Accuracy:  0.9833982661191253\n",
            "Epoch:  3  Validation Accuracy:  0.6901528013582343\n",
            "Epoch 4\n",
            "Epoch:  4  Train Accuracy:  0.9844863290161805\n",
            "Epoch:  4  Validation Accuracy:  0.6943972835314092\n",
            "Epoch 5\n",
            "Epoch:  5  Train Accuracy:  0.985714787125759\n",
            "Epoch:  5  Validation Accuracy:  0.6876061120543294\n",
            "Epoch 6\n",
            "Epoch:  6  Train Accuracy:  0.9867326524165526\n",
            "Epoch:  6  Validation Accuracy:  0.6958121109224674\n",
            "Epoch 7\n",
            "Epoch:  7  Train Accuracy:  0.9851883050787968\n",
            "Epoch:  7  Validation Accuracy:  0.6975099037917374\n",
            "Epoch 8\n",
            "Epoch:  8  Train Accuracy:  0.9865571584008985\n",
            "Epoch:  8  Validation Accuracy:  0.6910016977928692\n",
            "Epoch 9\n",
            "Epoch:  9  Train Accuracy:  0.9878558141167386\n",
            "Epoch:  9  Validation Accuracy:  0.6893039049235993\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final Training Accuracy: 0.9878558141167386\n",
            "Final Validation Accuracy: 0.6893039049235993\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path2 = '/content/drive/MyDrive/APS360 Team/model checkpoint/res-6_2.pth'\n",
        "torch.save(resnet18new2.state_dict(), path2)"
      ],
      "metadata": {
        "id": "AFALxsuSGAVz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "val_loader = torch.utils.data.DataLoader(valid_set, batch_size=256, \n",
        "                                          num_workers=0, shuffle=True)\n",
        "\n",
        "# constant for classes\n",
        "emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']\n",
        "y_pred = []\n",
        "y_true = []\n",
        "# iterate over test data\n",
        "resnet18new2.load_state_dict(torch.load('/content/drive/MyDrive/APS360 Team/model checkpoint/res-6_2.pth'))\n",
        "for inputs, labels in val_loader:\n",
        "        output = resnet18new2(inputs)\n",
        "       # output = modelA(inputs) # Feed Network\n",
        "\n",
        "        output = (torch.max(torch.exp(output), 1)[1]).data.cpu().numpy()\n",
        "        #print(output)\n",
        "        y_pred.extend(output) # Save Prediction\n",
        "        \n",
        "        labels = labels.data.cpu().numpy()\n",
        "        y_true.extend(labels) # Save Truth\n",
        "        #print(labels)\n",
        "# Compute confusion matrix:\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "cm_normalised = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "# Plot confusion matrix:\n",
        "sns.set(font_scale=1.5) \n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "ax = sns.heatmap(cm_normalised, annot=True, linewidths=0, square=False, \n",
        "                 cmap='Blues', yticklabels=emotion_labels,\n",
        "                 xticklabels=emotion_labels, vmin=0,\n",
        "                 vmax=np.max(cm_normalised), fmt=\".2f\",\n",
        "                 annot_kws={\"size\": 20})\n",
        "ax.set(xlabel='Predicted label', ylabel='True label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_2VZ8e2rgAlW",
        "outputId": "93fc7c8f-63a8-461f-993a-e97dfc5db503"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-9cf28efa9a44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mresnet18new2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/APS360 Team/model checkpoint/res-6_2.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresnet18new2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m        \u001b[0;31m# output = modelA(inputs) # Feed Network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 249\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    230\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    441\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[1;32m    442\u001b[0m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[0;32m--> 443\u001b[0;31m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "use_cuda = True\n",
        "\n",
        "resnet18newbest = torchvision.models.resnet.resnet18(pretrained=True)\n",
        "num_feature = resnet18newbest.fc.in_features\n",
        "resnet18newbest.fc=nn.Linear(num_feature, 6)\n",
        "resnet18newbest.cuda()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4Il0BxKV9wf",
        "outputId": "1f5c9b03-8084-4df7-c61b-43fba4e66cf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=512, out_features=6, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "val_loader = torch.utils.data.DataLoader(valid_set, batch_size=256, \n",
        "                                          num_workers=0, shuffle=True)\n",
        "\n",
        "# constant for classes\n",
        "emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']\n",
        "y_pred = []\n",
        "y_true = []\n",
        "# iterate over test data\n",
        "resnet18newbest.load_state_dict(torch.load('/content/drive/MyDrive/APS360 Team/model checkpoint/res_with_learning_rate_schedualer_resample.pth'))\n",
        "for inputs, labels in val_loader:\n",
        "        inputs, labels = inputs.cuda(), labels.cuda()\n",
        "        output = resnet18newbest(inputs)\n",
        "       # output = modelA(inputs) # Feed Network\n",
        "\n",
        "\n",
        "        output = (torch.max(torch.exp(output), 1)[1]).data.cpu().numpy()\n",
        "        #print(output)\n",
        "        y_pred.extend(output) # Save Prediction\n",
        "        \n",
        "        labels = labels.data.cpu().numpy()\n",
        "        y_true.extend(labels) # Save Truth\n",
        "        #print(labels)\n",
        "# Compute confusion matrix:\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "cm_normalised = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "# Plot confusion matrix:\n",
        "sns.set(font_scale=1.5) \n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "ax = sns.heatmap(cm_normalised, annot=True, linewidths=0, square=False, \n",
        "                 cmap='Blues', yticklabels=emotion_labels,\n",
        "                 xticklabels=emotion_labels, vmin=0,\n",
        "                 vmax=np.max(cm_normalised), fmt=\".2f\",\n",
        "                 annot_kws={\"size\": 20})\n",
        "ax.set(xlabel='Predicted label', ylabel='True label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "uK4whTZtVBNB",
        "outputId": "08988eef-0552-4d62-dc92-951ebd15df55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(58.5, 0.5, 'True label'), Text(0.5, 58.5, 'Predicted label')]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# resnet test acc"
      ],
      "metadata": {
        "id": "OVUhZ2h35MQR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data.dataset import T\n",
        "use_cuda = True\n",
        "\n",
        "resnet18newbest = torchvision.models.resnet.resnet18(pretrained=True)\n",
        "num_feature = resnet18newbest.fc.in_features\n",
        "resnet18newbest.fc=nn.Linear(num_feature, 6)\n",
        "resnet18newbest.cuda()\n",
        "\n",
        "resnet18newbest.load_state_dict(torch.load('/content/drive/MyDrive/APS360 Team/model checkpoint/res_with_learning_rate_schedualer.pth'))\n",
        "\n",
        "\n",
        "\n",
        "data_transform = transforms.Compose(\n",
        "    [\n",
        "        transforms.Resize((224,224)), \n",
        "        #convert image to grayscale\n",
        "        # transforms.Grayscale(num_output_channels=3),\n",
        "        #randomly adding brightness \n",
        "        # transforms.ColorJitter(brightness=(1,1.2)),\n",
        "        #apply ramdom rotation from -30 to 30 degrees\n",
        "        # transforms.RandomRotation(30),\n",
        "        #apply random horizontal flip with probability of 50% \n",
        "        transforms.RandomHorizontalFlip(p=0.5),\n",
        "        transforms.ToTensor(),\n",
        "        #normalize the data with 0.5 mean and variance.\n",
        "        transforms.Normalize((0.5073955,), (0.25,), inplace=True)\n",
        "       \n",
        "    ])\n",
        "\n",
        "\n",
        "test_data_path = '/content/drive/MyDrive/APS360 Team/Alex_Uploads/FER-2013_Images/FER-2013/test'\n",
        "test_data = torchvision.datasets.ImageFolder(test_data_path,data_transform)\n",
        "test_loader = torch.utils.data.DataLoader(test_data, batch_size=50, \n",
        "                                          num_workers=0, shuffle=True)\n",
        "\n",
        "test_acc = get_accuracy(resnet18newbest, test_loader)\n",
        "print(\"test_acc = \", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tq-zVRwl5L1I",
        "outputId": "db422dd9-c2da-4134-a4a8-93d6d0eef99a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test_acc =  0.6756297763939995\n"
          ]
        }
      ]
    }
  ]
}