Traffic_Signal_Classification.ipynb

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      "name": "Traffic Signal Recogntion.ipynb",
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HarshRangwala/NeuralNetworkProjects/blob/master/Traffic_Signal_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thxwG-w_OMwK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12TDzpoVONvv",
        "colab_type": "code",
        "outputId": "82183e5f-fe95-443b-8224-f39b80dca3ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Omug2POJK1qy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchvision.datasets import CIFAR10\n",
        "from torchvision.transforms import ToTensor\n",
        "from torchvision.utils import make_grid\n",
        "from torch.utils.data.dataloader import DataLoader\n",
        "from torch.utils.data import random_split\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIq2W9taK1ri",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Data loading \n",
        "import pickle\n",
        "train_file = r\"/content/drive/My Drive/Colab Notebooks/test.p\" \n",
        "test_file = r\"/content/drive/My Drive/Colab Notebooks/test.p\"\n",
        "val_file = r\"/content/drive/My Drive/Colab Notebooks/valid.p\"\n",
        "\n",
        "with open(train_file, mode='rb') as f:\n",
        "    train = pickle.load(f)\n",
        "with open(val_file, mode='rb') as f:\n",
        "    valid = pickle.load(f)\n",
        "with open(test_file, mode='rb') as f:\n",
        "    test = pickle.load(f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jId6WaNnBNyK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxNSDeS1K1sS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, y_train = train['features'], train['labels']\n",
        "X_valid, y_valid = valid['features'], valid['labels']\n",
        "X_test, y_test = test['features'], test['labels']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yx_hUCfuK1tA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-z5YgiJHK1ti",
        "colab_type": "code",
        "outputId": "03293188-43c8-443c-e2d6-50cea3b4dc96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "# Number of training examples\n",
        "n_train = X_train.shape[0]\n",
        "\n",
        "# Number of testing examples\n",
        "n_test = X_test.shape[0]\n",
        "\n",
        "# Number of validation examples.\n",
        "n_validation = X_valid.shape[0]\n",
        "\n",
        "# What's the shape of an traffic sign image?\n",
        "image_shape = X_train[0].shape\n",
        "\n",
        "# How many unique classes/labels there are in the dataset.\n",
        "n_classes = len(np.unique(y_train))\n",
        "\n",
        "print(\"Number of training examples: \", n_train)\n",
        "print(\"Number of testing examples: \", n_test)\n",
        "print(\"Number of validation examples: \", n_validation)\n",
        "print(\"Image data shape =\", image_shape)\n",
        "print(\"Number of classes =\", n_classes)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training examples:  12630\n",
            "Number of testing examples:  12630\n",
            "Number of validation examples:  4410\n",
            "Image data shape = (32, 32, 3)\n",
            "Number of classes = 43\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hnu0gkzRK1uV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def normalize(x):\n",
        "    return (x.astype(float) - 128) / 128"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5OfxlsWK1vF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "#converting to numpy arrays\n",
        "\n",
        "xtrain = np.array(X_train)\n",
        "ytrain = np.array(y_train)\n",
        "xvalid = np.array(X_valid)\n",
        "yvalid = np.array(y_valid)\n",
        "xtest = np.array(X_test)\n",
        "ytest = np.array(y_test)\n",
        "\n",
        "\n",
        "\n",
        "np.shape(xtrain)\n",
        "#preprocessing data\n",
        "\n",
        "xtrain_norm = normalize(xtrain)\n",
        "xtest_norm = normalize(xtest)\n",
        "xvalid_norm = normalize(xvalid)\n",
        "\n",
        "xtrain_norm = xtrain_norm.transpose((0, 3, 1, 2))\n",
        "xtest_norm = xtest_norm.transpose((0, 3, 1, 2))\n",
        "xvalid_norm = xvalid_norm.transpose((0, 3, 1, 2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8l6GxbJcK1vu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function, division\n",
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "#from skimage import io, transform\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms, utils\n",
        "from torch.autograd import Variable"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X615OHwvK1wF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xtrainTensor = torch.from_numpy(xtrain_norm)\n",
        "xtestTensor = torch.from_numpy(xtest_norm)\n",
        "xvalidTensor = torch.from_numpy(xvalid_norm)\n",
        "\n",
        "ytrainTensor = torch.from_numpy(ytrain).long()\n",
        "ytestTensor = torch.from_numpy(ytest).long()\n",
        "yvalidTensor = torch.from_numpy(yvalid).long()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ujATH3cK1we",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.utils.data as utils\n",
        "\n",
        "#creating tensor datasets\n",
        "train_dataset = utils.TensorDataset(xtrainTensor, ytrainTensor) # create your datset\n",
        "test_dataset = utils.TensorDataset(xtestTensor, ytestTensor)\n",
        "valid_dataset = utils.TensorDataset(xvalidTensor, yvalidTensor)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3IS3faCjK1w2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xh0zm1WZK1xQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating loaders\n",
        "\n",
        "batch_size = 128\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                           batch_size=batch_size,\n",
        "                                           shuffle=True)\n",
        "val_loader = torch.utils.data.DataLoader(dataset = valid_dataset,\n",
        "                                        batch_size = batch_size,\n",
        "                                        shuffle = False)\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
        "                                          batch_size=batch_size,\n",
        "                                          shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2ri0WkSK1xq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Project name used for jovian.commit\n",
        "project_name = 'german-traffic-signal-recognition-feedforward'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFDHkDt8K1yB",
        "colab_type": "text"
      },
      "source": [
        "## Base Model class & Training on GPU\n",
        "\n",
        "Let's create a base model class, which contains everything except the model architecture i.e. it wil not contain the `__init__` and `__forward__` methods. We will later extend this class to try out different architectures. In fact, you can extend this model to solve any image classification problem."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fyt-ZnCEK1yH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def accuracy(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCDdj-O7K1ya",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ImageClassificationBase(nn.Module):\n",
        "    def training_step(self, batch):\n",
        "        images, labels = batch \n",
        "        out = self(images)                  # Generate predictions\n",
        "        loss = F.cross_entropy(out, labels.long()) # Calculate loss\n",
        "        return loss\n",
        "    \n",
        "    def validation_step(self, batch):\n",
        "        images, labels = batch \n",
        "        out = self(images)                    # Generate predictions\n",
        "        loss = F.cross_entropy(out, labels.long())   # Calculate loss\n",
        "        acc = accuracy(out, labels)           # Calculate accuracy\n",
        "        return {'val_loss': loss.detach(), 'val_acc': acc.detach()}\n",
        "        \n",
        "    def validation_epoch_end(self, outputs):\n",
        "        batch_losses = [x['val_loss'] for x in outputs]\n",
        "        epoch_loss = torch.stack(batch_losses).mean()   # Combine losses\n",
        "        batch_accs = [x['val_acc'] for x in outputs]\n",
        "        epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies\n",
        "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
        "    \n",
        "    def epoch_end(self, epoch, result):\n",
        "        print(\"Epoch [{}], val_loss: {:.4f}, val_acc: {:.4f}\".format(epoch, result['val_loss'], result['val_acc']))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxUGcWL-K1yw",
        "colab_type": "text"
      },
      "source": [
        "We can also use the exact same training loop as before. I hope you're starting to see the benefits of refactoring our code into reusable functions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EX13mTklK1y2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate(model, val_loader):\n",
        "    outputs = [model.validation_step(batch) for batch in val_loader]\n",
        "    return model.validation_epoch_end(outputs)\n",
        "\n",
        "def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
        "    history = []\n",
        "    optimizer = opt_func(model.parameters(), lr)\n",
        "    for epoch in range(epochs):\n",
        "        # Training Phase \n",
        "        for batch in train_loader:\n",
        "            loss = model.training_step(batch)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "        # Validation phase\n",
        "        result = evaluate(model, val_loader)\n",
        "        model.epoch_end(epoch, result)\n",
        "        history.append(result)\n",
        "    return history"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSYoCXB7K1zX",
        "colab_type": "text"
      },
      "source": [
        "Finally, let's also define some utilities for moving out data & labels to the GPU, if one is available."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8Fl0e4AK1zd",
        "colab_type": "code",
        "outputId": "d1b57a16-0184-444d-bc58-a4bf6d1aba40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "torch.cuda.is_available()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEiroBm3K1zy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_default_device():\n",
        "    \"\"\"Pick GPU if available, else CPU\"\"\"\n",
        "    if torch.cuda.is_available():\n",
        "        return torch.device('cuda')\n",
        "    else:\n",
        "        return torch.device('cpu')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hj_ZBNTMK10J",
        "colab_type": "code",
        "outputId": "3c7f3a8c-f8c2-4fab-9569-f13329abd131",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "device = get_default_device()\n",
        "device"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpFMXS8eK10j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def to_device(data, device):\n",
        "    \"\"\"Move tensor(s) to chosen device\"\"\"\n",
        "    if isinstance(data, (list,tuple)):\n",
        "        return [to_device(x, device) for x in data]\n",
        "    return data.to(device, dtype = torch.float, non_blocking=True)\n",
        "\n",
        "class DeviceDataLoader():\n",
        "    \"\"\"Wrap a dataloader to move data to a device\"\"\"\n",
        "    def __init__(self, dl, device):\n",
        "        self.dl = dl\n",
        "        self.device = device\n",
        "        \n",
        "    def __iter__(self):\n",
        "        \"\"\"Yield a batch of data after moving it to device\"\"\"\n",
        "        for b in self.dl: \n",
        "            yield to_device(b, self.device)\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"Number of batches\"\"\"\n",
        "        return len(self.dl)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pDSfthJTK108",
        "colab_type": "text"
      },
      "source": [
        "Let us also define a couple of helper functions for plotting the losses & accuracies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsW7XohnK11A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_losses(history):\n",
        "    losses = [x['val_loss'] for x in history]\n",
        "    plt.plot(losses, '-x')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('loss')\n",
        "    plt.title('Loss vs. No. of epochs');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrMW6h9jK11i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def plot_accuracies(history):\n",
        "    accuracies = [x['val_acc'] for x in history]\n",
        "    plt.plot(accuracies, '-x')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.title('Accuracy vs. No. of epochs');"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-PfdhXK3K11x",
        "colab_type": "text"
      },
      "source": [
        "Let's move our data loaders to the appropriate device."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sqDc4sKK110",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loader = DeviceDataLoader(train_loader, device)\n",
        "val_loader = DeviceDataLoader(val_loader, device)\n",
        "test_loader = DeviceDataLoader(test_loader, device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umRtzqDXK12J",
        "colab_type": "text"
      },
      "source": [
        "## Training the model\n",
        "\n",
        "We will make several attempts at training the model. Each time, try a different architecture and a different set of learning rates. Here are some ideas to try:\n",
        "- Increase or decrease the number of hidden layers\n",
        "- Increase of decrease the size of each hidden layer\n",
        "- Try different activation functions\n",
        "- Try training for different number of epochs\n",
        "- Try different learning rates in every epoch\n",
        "\n",
        "What's the highest validation accuracy you can get to? **Can you get to 50% accuracy? What about 60%?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "589_nt_xK12L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "input_size = 3*32*32\n",
        "output_size = 43"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4GAOnXiK12c",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "**Q: Extend the `ImageClassificationBase` class to complete the model definition.**\n",
        "\n",
        "Hint: Define the `__init__` and `forward` methods."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztm03uSNK12f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "55cd23a7-ef69-4536-c997-228bcff7f1d8"
      },
      "source": [
        "class trafficsignalModel(ImageClassificationBase):\n",
        "\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.linear1 = nn.Linear(input_size, 1324)\n",
        "    self.linear2 = nn.Linear(1324, 500)\n",
        "    self.linear3 = nn.Linear(500, 256)\n",
        "    self.linear4 = nn.Linear(256, 128)\n",
        "    self.linear5 = nn.Linear(128, 64)\n",
        "    self.linear6 = nn.Linear(64, output_size)\n",
        "\n",
        "  def forward(self, xb):\n",
        "    #flatten the images\n",
        "    # Flatten images into vectors\n",
        "    out = xb.view(xb.size(0), -1)\n",
        "    # Apply layers & activation functions\n",
        "    out = self.linear1(out)\n",
        "    out = F.relu(out)\n",
        "    out = self.linear2(out)\n",
        "    out = F.relu(out)\n",
        "    out = self.linear3(out)\n",
        "    out = F.relu(out)\n",
        "    out = self.linear4(out)\n",
        "    out = F.relu(out)\n",
        "    out = self.linear5(out)\n",
        "    out = F.relu(out)\n",
        "    out = self.linear6(out)\n",
        "    out = F.relu(out)\n",
        "    return out\n",
        "'''\n",
        "  def __init__(self):\n",
        "    #super(trafficsignalModel, self).__init__()\n",
        "    super().__init__()\n",
        "    self.conv1 = nn.Conv2d(3, 10, kernel_size = 5)\n",
        "    self.conv2 = nn.Conv2d(10, 20, kernel_size = 5)\n",
        "    self.conv2_drop = nn.Dropout2d()\n",
        "    self.fc1 = nn.Linear(500, 50)\n",
        "    self.fc2 = nn.Linear(50, output_size)\n",
        "  def forward(self, x):\n",
        "    x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
        "    x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
        "    x = x.view(-1, 500)\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.dropout(x, training=self.training)\n",
        "    x = self.fc2(x)\n",
        "    return F.log_softmax(x)\n",
        "\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv1 = nn.Conv2d(3, 16, 3, 1, padding=1)\n",
        "    self.conv2 = nn.Conv2d(16, 32, 3, 1, padding=1)\n",
        "    self.conv3 = nn.Conv2d(32, 64, 3, 1, padding=1)\n",
        "    self.fc1 = nn.Linear(4*4*64, 500)\n",
        "    self.dropout1 = nn.Dropout(0.5)\n",
        "    self.fc2 = nn.Linear(500, output_size)\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.conv1(x))\n",
        "    x = F.max_pool2d(x, 2, 2)\n",
        "    x = F.relu(self.conv2(x))\n",
        "    x = F.max_pool2d(x, 2, 2)\n",
        "    x = F.relu(self.conv3(x))\n",
        "    x = F.max_pool2d(x, 2, 2)\n",
        "    x = x.view(-1, 4*4*64)\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = self.dropout1(x)\n",
        "    x = self.fc2(x)\n",
        "    return x\n",
        "'''"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n  def __init__(self):\\n    #super(trafficsignalModel, self).__init__()\\n    super().__init__()\\n    self.conv1 = nn.Conv2d(3, 10, kernel_size = 5)\\n    self.conv2 = nn.Conv2d(10, 20, kernel_size = 5)\\n    self.conv2_drop = nn.Dropout2d()\\n    self.fc1 = nn.Linear(500, 50)\\n    self.fc2 = nn.Linear(50, output_size)\\n  def forward(self, x):\\n    x = F.relu(F.max_pool2d(self.conv1(x), 2))\\n    x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\\n    x = x.view(-1, 500)\\n    x = F.relu(self.fc1(x))\\n    x = F.dropout(x, training=self.training)\\n    x = self.fc2(x)\\n    return F.log_softmax(x)\\n\\n  def __init__(self):\\n    super().__init__()\\n    self.conv1 = nn.Conv2d(3, 16, 3, 1, padding=1)\\n    self.conv2 = nn.Conv2d(16, 32, 3, 1, padding=1)\\n    self.conv3 = nn.Conv2d(32, 64, 3, 1, padding=1)\\n    self.fc1 = nn.Linear(4*4*64, 500)\\n    self.dropout1 = nn.Dropout(0.5)\\n    self.fc2 = nn.Linear(500, output_size)\\n  def forward(self, x):\\n    x = F.relu(self.conv1(x))\\n    x = F.max_pool2d(x, 2, 2)\\n    x = F.relu(self.conv2(x))\\n    x = F.max_pool2d(x, 2, 2)\\n    x = F.relu(self.conv3(x))\\n    x = F.max_pool2d(x, 2, 2)\\n    x = x.view(-1, 4*4*64)\\n    x = F.relu(self.fc1(x))\\n    x = self.dropout1(x)\\n    x = self.fc2(x)\\n    return x\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hz5IrjVqK123",
        "colab_type": "text"
      },
      "source": [
        "You can now instantiate the model, and move it the appropriate device."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jg-I0zRDK126",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = to_device(trafficsignalModel(), device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nB9-o-8QK13J",
        "colab_type": "text"
      },
      "source": [
        "Before you train the model, it's a good idea to check the validation loss & accuracy with the initial set of weights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "HREtkMHzK13L",
        "colab_type": "code",
        "outputId": "3905e643-490d-4d02-973d-4f43a96dc846",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "history = [evaluate(model, val_loader)]\n",
        "history"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'val_acc': 0.01495535671710968, 'val_loss': 3.7624144554138184}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2WlM2_xK13e",
        "colab_type": "text"
      },
      "source": [
        "**Q: Train the model using the `fit` function to reduce the validation loss & improve accuracy.**\n",
        "\n",
        "Leverage the interactive nature of Jupyter to train the model in multiple phases, adjusting the no. of epochs & learning rate each time based on the result of the previous training phase."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BrMqu9XK13h",
        "colab_type": "code",
        "outputId": "36c60e2e-7670-4fdd-e9c0-99d5d31febda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history += fit(100, 0.01, model, train_loader, val_loader)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0], val_loss: 3.7546, val_acc: 0.0469\n",
            "Epoch [1], val_loss: 3.7478, val_acc: 0.0469\n",
            "Epoch [2], val_loss: 3.7417, val_acc: 0.0469\n",
            "Epoch [3], val_loss: 3.7361, val_acc: 0.0469\n",
            "Epoch [4], val_loss: 3.7311, val_acc: 0.0469\n",
            "Epoch [5], val_loss: 3.7265, val_acc: 0.0469\n",
            "Epoch [6], val_loss: 3.7223, val_acc: 0.0469\n",
            "Epoch [7], val_loss: 3.7185, val_acc: 0.0469\n",
            "Epoch [8], val_loss: 3.7148, val_acc: 0.0469\n",
            "Epoch [9], val_loss: 3.7112, val_acc: 0.0469\n",
            "Epoch [10], val_loss: 3.7074, val_acc: 0.0469\n",
            "Epoch [11], val_loss: 3.7030, val_acc: 0.0480\n",
            "Epoch [12], val_loss: 3.6979, val_acc: 0.0518\n",
            "Epoch [13], val_loss: 3.6927, val_acc: 0.0580\n",
            "Epoch [14], val_loss: 3.6854, val_acc: 0.0632\n",
            "Epoch [15], val_loss: 3.6767, val_acc: 0.0712\n",
            "Epoch [16], val_loss: 3.6661, val_acc: 0.0708\n",
            "Epoch [17], val_loss: 3.6537, val_acc: 0.0679\n",
            "Epoch [18], val_loss: 3.6406, val_acc: 0.0632\n",
            "Epoch [19], val_loss: 3.6290, val_acc: 0.0846\n",
            "Epoch [20], val_loss: 3.6211, val_acc: 0.0748\n",
            "Epoch [21], val_loss: 3.6155, val_acc: 0.0743\n",
            "Epoch [22], val_loss: 3.6115, val_acc: 0.0721\n",
            "Epoch [23], val_loss: 3.6068, val_acc: 0.0750\n",
            "Epoch [24], val_loss: 3.6023, val_acc: 0.0808\n",
            "Epoch [25], val_loss: 3.5970, val_acc: 0.0835\n",
            "Epoch [26], val_loss: 3.5912, val_acc: 0.0795\n",
            "Epoch [27], val_loss: 3.5842, val_acc: 0.0714\n",
            "Epoch [28], val_loss: 3.5774, val_acc: 0.0761\n",
            "Epoch [29], val_loss: 3.5714, val_acc: 0.0806\n",
            "Epoch [30], val_loss: 3.5660, val_acc: 0.0795\n",
            "Epoch [31], val_loss: 3.5604, val_acc: 0.0786\n",
            "Epoch [32], val_loss: 3.5439, val_acc: 0.0842\n",
            "Epoch [33], val_loss: 3.5334, val_acc: 0.0855\n",
            "Epoch [34], val_loss: 3.5199, val_acc: 0.0833\n",
            "Epoch [35], val_loss: 3.4996, val_acc: 0.0804\n",
            "Epoch [36], val_loss: 3.4778, val_acc: 0.0848\n",
            "Epoch [37], val_loss: 3.4514, val_acc: 0.0949\n",
            "Epoch [38], val_loss: 3.4290, val_acc: 0.0953\n",
            "Epoch [39], val_loss: 3.4028, val_acc: 0.1045\n",
            "Epoch [40], val_loss: 3.3715, val_acc: 0.1098\n",
            "Epoch [41], val_loss: 3.3535, val_acc: 0.1118\n",
            "Epoch [42], val_loss: 3.3941, val_acc: 0.1306\n",
            "Epoch [43], val_loss: 3.2515, val_acc: 0.1549\n",
            "Epoch [44], val_loss: 3.2698, val_acc: 0.1513\n",
            "Epoch [45], val_loss: 3.2601, val_acc: 0.1569\n",
            "Epoch [46], val_loss: 3.1150, val_acc: 0.1705\n",
            "Epoch [47], val_loss: 2.9998, val_acc: 0.1942\n",
            "Epoch [48], val_loss: 2.8690, val_acc: 0.2297\n",
            "Epoch [49], val_loss: 2.8646, val_acc: 0.2268\n",
            "Epoch [50], val_loss: 2.9955, val_acc: 0.2033\n",
            "Epoch [51], val_loss: 2.8641, val_acc: 0.2486\n",
            "Epoch [52], val_loss: 2.6739, val_acc: 0.2622\n",
            "Epoch [53], val_loss: 2.5722, val_acc: 0.3285\n",
            "Epoch [54], val_loss: 2.7448, val_acc: 0.2837\n",
            "Epoch [55], val_loss: 2.5517, val_acc: 0.3432\n",
            "Epoch [56], val_loss: 2.5746, val_acc: 0.2964\n",
            "Epoch [57], val_loss: 2.4378, val_acc: 0.3258\n",
            "Epoch [58], val_loss: 2.5357, val_acc: 0.3138\n",
            "Epoch [59], val_loss: 2.4948, val_acc: 0.3199\n",
            "Epoch [60], val_loss: 2.5146, val_acc: 0.3090\n",
            "Epoch [61], val_loss: 2.4398, val_acc: 0.3385\n",
            "Epoch [62], val_loss: 2.3947, val_acc: 0.3308\n",
            "Epoch [63], val_loss: 2.7304, val_acc: 0.2983\n",
            "Epoch [64], val_loss: 2.3528, val_acc: 0.3438\n",
            "Epoch [65], val_loss: 2.3218, val_acc: 0.3605\n",
            "Epoch [66], val_loss: 2.4146, val_acc: 0.3628\n",
            "Epoch [67], val_loss: 2.4731, val_acc: 0.3411\n",
            "Epoch [68], val_loss: 2.2896, val_acc: 0.3925\n",
            "Epoch [69], val_loss: 2.2937, val_acc: 0.3804\n",
            "Epoch [70], val_loss: 2.2437, val_acc: 0.4089\n",
            "Epoch [71], val_loss: 2.1712, val_acc: 0.4282\n",
            "Epoch [72], val_loss: 2.1446, val_acc: 0.4382\n",
            "Epoch [73], val_loss: 2.2205, val_acc: 0.4195\n",
            "Epoch [74], val_loss: 2.1879, val_acc: 0.4146\n",
            "Epoch [75], val_loss: 2.4368, val_acc: 0.3588\n",
            "Epoch [76], val_loss: 2.1281, val_acc: 0.4389\n",
            "Epoch [77], val_loss: 2.2840, val_acc: 0.3904\n",
            "Epoch [78], val_loss: 2.3378, val_acc: 0.3938\n",
            "Epoch [79], val_loss: 2.3684, val_acc: 0.3746\n",
            "Epoch [80], val_loss: 2.3174, val_acc: 0.3967\n",
            "Epoch [81], val_loss: 2.5613, val_acc: 0.3422\n",
            "Epoch [82], val_loss: 2.4022, val_acc: 0.3686\n",
            "Epoch [83], val_loss: 2.0769, val_acc: 0.4552\n",
            "Epoch [84], val_loss: 2.1434, val_acc: 0.4601\n",
            "Epoch [85], val_loss: 2.2202, val_acc: 0.4552\n",
            "Epoch [86], val_loss: 2.1101, val_acc: 0.4400\n",
            "Epoch [87], val_loss: 2.0851, val_acc: 0.4525\n",
            "Epoch [88], val_loss: 2.1009, val_acc: 0.4494\n",
            "Epoch [89], val_loss: 2.0233, val_acc: 0.5030\n",
            "Epoch [90], val_loss: 2.2788, val_acc: 0.4432\n",
            "Epoch [91], val_loss: 2.2049, val_acc: 0.4405\n",
            "Epoch [92], val_loss: 2.3790, val_acc: 0.4029\n",
            "Epoch [93], val_loss: 2.0583, val_acc: 0.4985\n",
            "Epoch [94], val_loss: 2.2144, val_acc: 0.4522\n",
            "Epoch [95], val_loss: 2.2655, val_acc: 0.4411\n",
            "Epoch [96], val_loss: 2.4093, val_acc: 0.4271\n",
            "Epoch [97], val_loss: 2.1839, val_acc: 0.4474\n",
            "Epoch [98], val_loss: 2.2174, val_acc: 0.4566\n",
            "Epoch [99], val_loss: 2.2009, val_acc: 0.4561\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQ-vg7IFCuC4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 925
        },
        "outputId": "60e5d98f-c522-4f16-fd41-009a7da93400"
      },
      "source": [
        "history += fit(50, 0.03, model, train_loader, val_loader)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0], val_loss: 3.2928, val_acc: 0.6190\n",
            "Epoch [1], val_loss: 3.3246, val_acc: 0.6185\n",
            "Epoch [2], val_loss: 3.3106, val_acc: 0.6239\n",
            "Epoch [3], val_loss: 3.3611, val_acc: 0.6254\n",
            "Epoch [4], val_loss: 3.4165, val_acc: 0.6289\n",
            "Epoch [5], val_loss: 3.4376, val_acc: 0.6132\n",
            "Epoch [6], val_loss: 3.4102, val_acc: 0.6288\n",
            "Epoch [7], val_loss: 3.3336, val_acc: 0.6223\n",
            "Epoch [8], val_loss: 3.2738, val_acc: 0.6194\n",
            "Epoch [9], val_loss: 3.3281, val_acc: 0.6239\n",
            "Epoch [10], val_loss: 3.3847, val_acc: 0.6027\n",
            "Epoch [11], val_loss: 3.3474, val_acc: 0.6272\n",
            "Epoch [12], val_loss: 3.3905, val_acc: 0.6241\n",
            "Epoch [13], val_loss: 3.4171, val_acc: 0.6226\n",
            "Epoch [14], val_loss: 3.4966, val_acc: 0.6190\n",
            "Epoch [15], val_loss: 3.4176, val_acc: 0.6301\n",
            "Epoch [16], val_loss: 3.4965, val_acc: 0.6202\n",
            "Epoch [17], val_loss: 3.5002, val_acc: 0.6308\n",
            "Epoch [18], val_loss: 4.4388, val_acc: 0.5425\n",
            "Epoch [19], val_loss: 3.0969, val_acc: 0.6201\n",
            "Epoch [20], val_loss: 3.3750, val_acc: 0.5869\n",
            "Epoch [21], val_loss: 3.1310, val_acc: 0.6318\n",
            "Epoch [22], val_loss: 3.1623, val_acc: 0.6306\n",
            "Epoch [23], val_loss: 3.2366, val_acc: 0.6308\n",
            "Epoch [24], val_loss: 3.2201, val_acc: 0.6295\n",
            "Epoch [25], val_loss: 3.1861, val_acc: 0.6318\n",
            "Epoch [26], val_loss: 3.2725, val_acc: 0.6279\n",
            "Epoch [27], val_loss: 3.3515, val_acc: 0.6146\n",
            "Epoch [28], val_loss: 3.3873, val_acc: 0.6263\n",
            "Epoch [29], val_loss: 3.4768, val_acc: 0.6127\n",
            "Epoch [30], val_loss: 3.5382, val_acc: 0.5977\n",
            "Epoch [31], val_loss: 3.4352, val_acc: 0.6223\n",
            "Epoch [32], val_loss: 3.4396, val_acc: 0.6321\n",
            "Epoch [33], val_loss: 3.3818, val_acc: 0.6306\n",
            "Epoch [34], val_loss: 3.2698, val_acc: 0.6284\n",
            "Epoch [35], val_loss: 3.3111, val_acc: 0.6343\n",
            "Epoch [36], val_loss: 3.3434, val_acc: 0.6314\n",
            "Epoch [37], val_loss: 3.4023, val_acc: 0.6327\n",
            "Epoch [38], val_loss: 3.4828, val_acc: 0.6281\n",
            "Epoch [39], val_loss: 3.4738, val_acc: 0.6281\n",
            "Epoch [40], val_loss: 3.5103, val_acc: 0.6339\n",
            "Epoch [41], val_loss: 4.1358, val_acc: 0.5647\n",
            "Epoch [42], val_loss: 3.2955, val_acc: 0.6379\n",
            "Epoch [43], val_loss: 3.4398, val_acc: 0.6317\n",
            "Epoch [44], val_loss: 3.4168, val_acc: 0.6274\n",
            "Epoch [45], val_loss: 3.4384, val_acc: 0.6409\n",
            "Epoch [46], val_loss: 3.5390, val_acc: 0.6315\n",
            "Epoch [47], val_loss: 3.5654, val_acc: 0.6324\n",
            "Epoch [48], val_loss: 3.3670, val_acc: 0.6377\n",
            "Epoch [49], val_loss: 3.5628, val_acc: 0.5873\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vm7b6UItEGjO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "1fac29c9-7c26-4c48-9dd6-56f7df8e08e6"
      },
      "source": [
        "history += fit(10, 0.03, model, train_loader, val_loader)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0], val_loss: 3.4281, val_acc: 0.6310\n",
            "Epoch [1], val_loss: 3.4691, val_acc: 0.6339\n",
            "Epoch [2], val_loss: 3.4998, val_acc: 0.6322\n",
            "Epoch [3], val_loss: 3.5282, val_acc: 0.6317\n",
            "Epoch [4], val_loss: 3.6121, val_acc: 0.6259\n",
            "Epoch [5], val_loss: 3.5813, val_acc: 0.6348\n",
            "Epoch [6], val_loss: 3.5320, val_acc: 0.6378\n",
            "Epoch [7], val_loss: 3.5688, val_acc: 0.6362\n",
            "Epoch [8], val_loss: 3.5694, val_acc: 0.6394\n",
            "Epoch [9], val_loss: 3.6163, val_acc: 0.6362\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WcJDjQjK14A",
        "colab_type": "code",
        "outputId": "27b8e0ac-559a-49f6-b55d-35b36f6ca281",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import csv\n",
        "def im_convert(tensor):\n",
        "  image = tensor.cpu().clone().detach().numpy()\n",
        "  image = image.transpose(1, 2, 0)\n",
        "  image = image * np.array((0.5, 0.5, 0.5)) + np.array((0.5, 0.5, 0.5))\n",
        "  image = image.clip(0, 1)\n",
        "  return image\n",
        "\n",
        "'''\n",
        "classes = (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42)\n",
        "'''\n",
        "\n",
        "'''classes = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14',\n",
        "           '15', '16', '17', '18', '19','20', '21', '22', '23', '24', '25', '26', '27', '28', '29',\n",
        "           '30', '31', '32', '33', '34', '35', '36', '37', '38', '39','41','42')'''\n",
        "\n",
        "with open('/content/signnames.csv', mode='r') as f:\n",
        "    fread = csv.reader(f)\n",
        "    next(fread, f)\n",
        "    classes = [rows[1] for rows in fread]\n",
        "\n",
        "dataiter = iter(test_loader)\n",
        "\n",
        "images, labels = next(dataiter)\n",
        "images = to_device(images, device)\n",
        "labels = to_device(labels, device)\n",
        "\n",
        "output = model(images)\n",
        "_, preds = torch.max(output, 1)\n",
        "\n",
        "fig = plt.figure(figsize=(30, 30))\n",
        "\n",
        "for idx in np.arange(20):\n",
        "  ax = fig.add_subplot(10, 2, idx+1, xticks=[], yticks=[] )\n",
        "  plt.imshow(im_convert(images[idx]))\n",
        "  ax.set_title(\"{} ({})\".format(str(classes[int(preds[idx].item())]), str(classes[int(labels[idx].item())])), color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x2160 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O87WnaZhgh0n",
        "colab_type": "code",
        "outputId": "a6c26a82-64d0-4b84-dc51-d243f86fd736",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "model.eval()\n",
        "correct = 0\n",
        "total = 0\n",
        "for images, labels in test_loader:\n",
        "    images = Variable(images.cuda())\n",
        "    outputs = model(images)\n",
        "    _, predicted = torch.max(outputs.data, 1)\n",
        "    total += labels.size(0)\n",
        "    correct += (predicted.cpu() == labels.cpu()).sum()\n",
        "print('Test Accuracy of the model on the 10000 test images: %.4f %%' % (100 * correct / total))"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy of the model on the 10000 test images: 97.0000 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/pytorch/aten/src/ATen/native/BinaryOps.cpp:81: UserWarning: Integer division of tensors using div or / is deprecated, and in a future release div will perform true division as in Python 3. Use true_divide or floor_divide (// in Python) instead.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tROW3ENcgkst",
        "colab_type": "code",
        "outputId": "5f7d3c6a-1ddd-4792-bf62-3577a6b94a2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        }
      },
      "source": [
        "def predict_image(img, model):\n",
        "    xb = img.unsqueeze(0)\n",
        "    yb = model(xb)\n",
        "    _, preds  = torch.max(yb, dim=1)\n",
        "    return preds[0].item()\n",
        "\n",
        "def im_convert(tensor):\n",
        "  image = tensor.cpu().clone().detach().numpy()\n",
        "  #image = image.transpose(1, 2, 0)\n",
        "  #image = image * np.array((0.5, 0.5, 0.5)) + np.array((0.5, 0.5, 0.5))\n",
        "  #image = image.clip(0, 1)\n",
        "  return image\n",
        "\n",
        "img, label = test_dataset[1533]\n",
        "img = to_device(img, device)\n",
        "label = to_device(label, device)\n",
        "plt.imshow(im_convert(img[0]), cmap='gray')\n",
        "print('Label:', label, ', Predicted:', predict_image(img, model))"
      ],
      "execution_count": 85,
      "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-85-2e36496f7f87>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\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[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Label:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m', Predicted:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredict_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\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[0;32m<ipython-input-85-2e36496f7f87>\u001b[0m in \u001b[0;36mpredict_image\u001b[0;34m(img, model)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpredict_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\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      2\u001b[0m     \u001b[0mxb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\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      4\u001b[0m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\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/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    548\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\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[1;32m    549\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[0;32m--> 550\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\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    551\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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    552\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\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[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-65-04f98b25d32e>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, xb)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;31m#flatten the images\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;31m# Flatten images into vectors\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[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\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# Apply layers & activation functions\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[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\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: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead."
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ob11o2Bog0vD",
        "colab_type": "code",
        "outputId": "3ec6fdf8-0be4-4819-bc06-b78b8d58cfae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plot_losses(history)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D4kqykmP5oLiSMcPjWLW+hALHcFatL+HtL45w54rNbNhfxZ0rNpOXkyLCIAjtMIPF0VEWoqwq4pvOdbAIRAgGL0WltSxbkI/PD8sW5DM5KwWA88YNJ9Zm5eJJ6Xy6z3AL2ayKFo+fa6ZmEW1VAKQnRlPX4sWioKyuBYDi8gYU8EFxJSOSY9hWUkuz20ehswqAJ9ft484Vm7FaYOmqrW2EQRBOV0zXULTVgtWiIt4iaHYb402OC8kaGqIMeSFYMsdBgcMe/F1UWstjC2dwztg0Hls4g9tnjwPaCsOHuyqIClgMrgY3F463ozWckzsMBYxIisEPxNoslNe1AkYqXIvHjwJWF1fS7Pbx4Nu7uGvuON7YXiaWgnDac9w1ZCEq4JKNZFq8PqKtFmIDdQ/iGhpC9FYYpo1K4ZO9LuZNSmfjgWrmTUqnsr6VKVnJtHj8TMlKRgHnj0vDaiFoPXj9GrdP8z//KOblzaWAWArC6Y2njWvIEvl1BG4fsTYLMVHGNCkWwRDmRMJw2ZRM7p8/CZ+GBeeOotB5jAXnjsJZ2cglk9L5oqyOBeeO4ouyWmKirNisCq1h9hl2rIpgvMHj9bO6uJJWj5+HP9jLsgX5wHFBEIShjjvgCoqyKKIsCm+Ep4+2en3E2qxEm0IQ4eM9FU6L9NHeYKaCFjjsbX4vnu1g+VonTy2aSVFpbfD3eY409lU2AoYVsfFANbPOGMbGA9XE2Kz4/dpwGwVWE26fH6/fz4sbSli318WyBfltqp0lFVUYqri9fqKtFpRSgWBx5FsEcdFWoq1D3yIQIegFXYnE8rVOHls4g6LS2qBraeOBzVw7LYtx6Qn89p3dKGVYBiOSoqmod/P37WWMH5HAtkM17Cir5dE1+7hr7rg2LTFEFIShhMfnD66uoyyWQdF9NDYqxCIQIRC6o71AmMIAhuvnXy+bwMMf7GW2aSlEWWj1+tlb0cjv39+Nx6eZNiqFhz/Y26GQTQrYhKGCx+fHFsjGM4LFkT2xNnt8xEYfF4LWPnYNmYs+c94AOvUM9PS4U+G0jxGEg9B4g5m6GhqIvn56NqtuP5f0xOhgCt22kloaA4VsWhMsZJMaBWGo4Pb6sQXcLIMhfbTF4yM2ykKM1cga6muLwOyDVuh0AcdrntrHDM3jnv1kPy0eX5fHnQpiEYSRriwFMybg0zAxI5Fd5Q0kxVipb/Xh92k8Pi/Pry8hNc7GtpJaorppgSG9kYTBgjvUNTQIYgQtHh/DEqLD5hoy+6AtemYjw+KNzW8euWl6h5hhgcPOA9dNYcnKLbyypZTDNS3B2GJfIRZBP2L+U0OrnSsDdQr1rT4ucKQFC9nsidHUNHsAgsHmZrfXaIHh9vHJHmMV8eS6fSx6ZiMlxxrbpKTe92qRWAtCROHx6WDgNcoS+emj/REjKHDYSUuIpryulbhoK+ePS+O+V4u4c8VmDlY1Bq2Ff3x+FIDPD9cxaWRSn4oAiBAMCEWltdw1d1wwQPzlkTpuOncUGw9UBwvZqgICoYCLJ6Zjsyi8foiNsuDza1p9mqaAMHh9fp5fX8K0USnsKKtl0TMbeW3rYXEjCRGF2+sLuoYGQ/pos8fIGrJaFFaL6rbpnPkdC/37iXXONt+5zr6D6/ZUcrS2hZgoC2U1LXztkU94s+gIAOPSE1i6aisPf7CH17eXYbUoYm0WPj9c2+a1+gIRggFgyRxHsOWF+Xt+XhYxNkuHQrYF545i08HqYAuMVq+fK88eic2q8Pkh2qowF1ariyv51VvF+P0aq8UiBWxCROHx6Tauoci3CIyCMjDaYnRnEQT9/XuNCdpqCTSrDMywnfn1C50u7n5+Cxp46IZpZCTHsL20ltln2Hls4QweWe3kjPQEHnpvN2AsAp9eNIvHFs5oE1voCyRGMEB0FT8wU1CLSmu5Mi+z0xqFdXtcgZWVH49P85UzR7BmV2Xwi+X1a2PvheJKLAoefHsX/3b5RMAQhGUL8qVuQeh3jGCxmTVkocnt7bNzhyOzptnjC7aXiLFZOm0xEfq6v/1mHt95egOj0+I5UtPC/VdO4sF3drPjcD0f7ans4NcvKq3lK2eO4O/byoi2WqhtMlzB7+4o5/rpOXh8ftYfqA4ef9uFY4PPX7Ygn6LS2j5zEYlFECG0r3BeMsfB4tkOxqQlnLA30vr9x7BajC9YtNWo2gRjiz2/Nio6H3xnN4ue2chdc43ni4Ug9Ddu3/Gsob4OFvc0A6c3tHr8xAWEoCuLwHzddXsqebbwIF6/Zl9lI5dPGckNM0fj9vp5bdthbj53dJtFX6HTxZI5Dva5mnCkJ3DvX7dRMD4Nm1VhVbBkxWYa3b6gRXHttCxWrj8UfH/mHNFXiBBEOD3pjWT6Wn88fxIXnmHH59dEWaC+xcucCemAsRrzeP08+PZu7lyxWVpcCP1O24Kyvk0fNVfktzy9gZ/87fOg5XsyK+bla518vMeF2+cPWgR+rfnicG2nr7tsQT6Ln9vER7srAWMxtnZ3JX9cvQeAjKSYNpO4KR7v7jhKUWkNMVHGa9w+exw/veosWn1GNwIFxEdH8eP5k1i3x8Vdc8f1uUvIJGxCoJSKVUptUEptV0rtUEr9vJNjFimlKpVS2wI/t4drPEOFzoTha/nZPHPrLCZnpbCtpJYF547CarFw8aR0thyqJs5mJcqi0BirsoYWL898fKDNl0UsA+FU6SpguuiZDRQ6Xbi9xrW3fK2T2mYPFfUtffr6R2tb8fg0K9cfarMC7y15OSn8ywtbAIizWSl0ujjW6CYhtnNPeoHDHpxIz8pMxu3TLL5wLE9+vB8wGlH+8dv53PbsJp5Y5wyKx/97cRtaw56KhmBa+cLzc0mNN/Y/OGfscB5bOIPFsx1t4olFpR0F6VQJp0XQCszTWk8FpgGXK6XO6+S4F7XW0wI/T4ZxPEOSJXMc/Pr6vDYFbKOGJ/DMrbOCbqSv5Wfx71dMJCbKEhSE93aWMzkruU06q1gGwqnQ3j1jBkyzU2NZumor5XXNfFFWi9UC20tqg6mkfcXTn+wDIGdYXJsV+IloL2AFDjuLCnIB+GSv8d3ITo0L7lTWnn8UldEU6ER88JgR0zvW5OayyRmAEWvIGR7HvZeewUPv7qHQacT4mt1GFtKVUzKDolXodKGAJXPGsaeioc2YQheBfU3YgsVaaw2Y78QW+InsNIFBzolaXTxz6yx2lNXy4Nu7cfv8rNvj4vI/fERFfas0vxNOGXOle/fzW7j0rAze31nB/fMnsWz1XrJS4th5tJ6cYXE8umYfM3OHcbim+ZRf0wzWllU3s7eikTibBVd9K9fmZ/fYPWQK2GWTM7h6ahYAj31kiMqa3ZUUONJobPV2GiModLr40SufA/CLr01h66FqfvnmTuyJMViUCh6362g9i2c7mJyVwt0rt9Dk9qKBq87OZO2eyqAQLV21NVhUdtGE9FNycfWGsMYIlFJWpdQ2oAJ4T2u9vpPDvq6UKlJKvayUGtXFee5QSm1SSm2qrKwM55CHFO1bXQA8umYfz37XSEGzWRXFR+sZFm8Ty0DoEwocdkYkxfDSplK+NTOHxbMdWJRi59F6oq2K0upmbj53NJkpcXj7IEZgTuJPfrwfi4KvT8+hxetnWk5qj90opoC9vq2M7z6zkcV/3oTH62+Tt9/i8XUqBEWltcwebycmysKUrBQuPWskANtKaiitaWZEUgwAKz87SKHTxazc4ShlJHBMH53KlJwUli3IZ+mqrbyxvazNpG+OKxyuoPaEVQi01j6t9TQgBzhHKTWl3SFvALla6zzgPeDPXZznca31TK31zPT09HAOeUjSXhAKHHaSYqNIjIliWLwNZ2UjF//2Q4kZCKfMuj2V7Ck3HAErPjvE/75dTHWTh7Ozk/H4NNflZ7Ny/SGqGlrx9kHTuQKHnYe/nc+u8nrGpCXwRqAYy6t1r9wos3KH49NGBX+j24fHr9vk7TsrG6lqbO3wvCVzHJTVNjN1VCrRURZGpsSiFJyRkURZTTMTMpLIGRaHx69ZumorNz/5GdVNHiZmJLL1UA1Wy/EJf0xaQoeVf7hcQe3pl6whrXUN8CFwebv7q7TW5qf7JDCjP8ZzutK+xcUjN01nw4+/gj3Bxn5XE+mJ0WIZnKa095ND55Ww3RFaIAVwxohE/rTGeP6hY83cP38Sa3dXctfccXy6ryroIz9VkuOi0Br2uxqZnJVMbJSFd7442mZcJ2q58tynB2jx+Blrjw/eZ+btFzjspCfFUFl/XAjMQPgfP9jDjrI6Zo4ZRqHTxVMf7ycjKZaymmbKaprJSo1lYkYSxxrczD97JOv3VzPWnkBlg5v750/i0TX7KHS6+m3C74pwZg2lK6VSA7fjgK8Cxe2OyQz58xpgZ7jGIxwn1DLYeOAYPm3UHOwqb2D+w+vEMjgN6Ys8/KLSWi6eOAKrRXHB+DT2VjYw1h5PnM3CozdPb5P9MmdCep/17nlhwyEAbrswl88P1+L2+fnE6aIw8HPnis28WXSky/dS6HTx4Du7sCj4yfyzSIqNItZm4ZnCA8HPI3tYHHXN3g6B8PK6Frx+TUKMNfh5ZaXGcrCqkYr6VspqmkmIicJZ2cA7O8qJtVnY72pk0sik4OfRH66fExHOyuJM4M9KKSuG4LyktX5TKfULYJPW+nXgHqXUNYAXOAYsCuN4hADmyiPUMpicmcL5v36fHWV1nJXZNpvIjC8IQ4/QythlC/JZ9PRGxtkTOHisiacWzexRAoF5jiVzHFz+h4/ISollWFw09S1eGlq93H7h2OBxplX6yze/JKoPsoYKnS5e3lxKdmos/3nVZC45M4NFz2zE7fWz8KkNaK2Jj47i8e/M6DLgWlRaS3piDEmxNv7t5aJggsUb28uC139WShylSc1895mNpCdFU9/q4/75k/jdu0b7h2WrncHPKyv1EO99WY7WMDkrhZXrjUKzivpWYm2WNv2CTItjoAmbRaC1LtJa52ut87TWU7TWvwjc/9OACKC1vk9rPVlrPVVrfbHWurj7swp9SahlsONILdE2KwnRVr48UseVYhmcFoRaAruP1uP2+Skur2fM8PgeuwnNc7yxvYzio/VMyEjkrc8NX73WkJEc2+EcRkHZqVsEWw/VoFB8NRCkLXDY+fnVkwHw+TV+DTPGDOt0sjXdYddMzaKkupmM5Bjumjsu2Lrh19fnBVfs0VEWrBZFrj2BkuoWfD4/CkWLx3gPl08ZGXyN7NS4YDuKORPS+cmVZwGgFNis4esXdCpIZfFpTPuYwZ9umk7hfZeQEG3hy7I6Rg2PCz5+27ObaL+AE3EY/JiWwO1/3sTP3vgSgKQYK8Xl9Sx6ekO36YvmRGqe40cvFwHwyd4q7p8/CatFEW1VPPLh3g7nsFpOrcWE+dqzcofj9vk535EWvB7H2ONJjLESa7OgMALYZjO4UEwBeypQ+HXl2Zk8umZfG8EyfffRURbqWzzsq2w0qvZbffz3W4Yn+5qpmazdfTwFNCs1Lvj8rNQ4vjkzh5HJsWgNtxbkBq2ASHELgQiBQDvLoKwWW5SVaKtie0ktd67YzNJVW7n30jOCgS3om14uQmQwK3c4/sCk/LVpWaz+14uJtVlYs7uSORPS20zgoUFlcyJ9Yp2TVzaX0uwxgr9XnJ3J4tkOrsvPxu3TLDxvTAchibIa+xG0n6B7urgwX/svGw5hUWBRiqWrtmK1wJ0rNqOU4rr8bK44eyR+Dbc/tykYMzDPb07Gz316gKSYKH79z+IuRa+qoZX6Fi8ZyTFcNGFE8LovcKTx8I3TgymghU5XGyHITI1lw4FjuH1+7pk3Pqz9gk4FEQKhU8vgloJcLAre2XGUi86ws3i2g7vmjuOWpzcw87/f67dCFyE8hE7oy1bvpcXr58zMJN7ZUc5r20pRGMVQ//ziSBv3hTkB/21LKeePS+OuueN44K1iXt1yGDi+On5inZPVxRUdJj8TszHi91ZtOanFRTD3f3sZ9sQY/v2VomAg+qq8TB5bOIOrp2bxqbMKm1UxIikm6PMPPf+kkcn4/Jr6Vm+3bSlqmjxYLYry+lbio63sKW/gwvF2th6qaWMVPf7RPioDrTPSk2LYfLCaO1ds5rLJGdx76cQ2ghFJSBtqIUh7y33jO5gAACAASURBVMC03F/fXsbk7GSWrXbi9WlcDW5mdeF3FQYH5oR+19xxPPLhXpJjoyivbeX66Vk88FYx370wl6c+PsAt5+e2Ef0Ch5175o3nBy9t55Uth/nySB3JsVHUtni5YkoGD984nSfWOXngrWLunz+JxbMdnOdI67BwiAq0o/79t6Zxx3Ob+c75Y/jLxpITLi5Cg9vRAauior6VAkdap4HXR26azq3PbOTQsSZqmz3Bql2Th97dhV/DgnNHs3L9Ic4LnKc954wdzvr9x8Cv+bC4ok0gvf17+97zRp+irNQ43theBhCsWA51CUXS90csAiFIqGXw6Jp9/Hj+JGwWY+ObX71VTHKsNZgj/sWRuohb1Qg9x5yQfvfObrx+jcenWXaT0afq/vmTsCfGMDI5lgNVjR182X/ZWALAx3tdjBkeT22Ll7H2BNbsMlwvPj/cP38SZiy4M3+4aRGkxkfT0OrlT2ucPWoUZwrYml0V3PPCVhTGhi1d7dpV4LBz2WTDPXTxpBEUOOxBa6hwr4tVGw4xfkQCV+VlctnkjC5X66F9kX50xaQuq38LHHYeWTAdgMZWL+/sKA82lAsdU6S4hExECIQOmJbB4tkOFl80Lnh/SXULtsAX+IYZORFp4gqd01nBGEBKnNFI7ebzRrfZB0MpxeSsZD7a7WL66GEAPLHOyW/f2cWuo/XB528tqSHeZqGmyc29l54RdL0snu1oM9m1n/yiLMbU82FxReDxtB41igtt+1xW20KszcLTt3adhVPodPHRHqMtzT+/OEqh0xUUk6c/2Y9fw7yJhgBcPTWrywCu2T4b4Lr87A5javNex9u5cLydvRUNp9QFtT8RIRA6EGoZ/GVjCffMG09g/uf22WNJjo3Cp3VEZT0I3dNZwdjiP2+ivL6VKdnJvLLlcIdYwPr9x2j2+Ph0X1WwgGrt7go0cG3A1QHQ5PFz98WOXhVIma6hJ9YZzd3OzEzusf88NLi9ePa4LrNwQmNe2alx5GUbnwEY7Zw/2u3CZlX8dXNJG9dXZ6t1UwhGJMV02YU09HW/PFLXZXwkEhEhEDol1Pd5niONhBij2nLl+kOMTIllT3lDRJq4QueYE+Vtz25i4dPrueO5TcGNYW67YGwHt4h5PMBv39nFo2v28R9XTOLzw3VkJMewbq8ruDK+Lj+7jRuoJ9eEuaPeJZOMVs2Gn79nKZV/LjyATxvpnt1l4YTGvKaOSqH4aF2wTuBYoxu3z9jq9cxAAWV3mEJwRkZit8eFfm8iOTjcHhECoVNCO5YuXbWVxxbO4OlFs7gqL5ODVU3sPFI3wCMUekpovn/OsFjW7XbR6vHj15pzcofxy7d2duoWmTtxBJNGJrGjrI5vzcwhe5iRFlle18qcCXbW7q7knnnjWbu7stdpxLaAa8gWZQiCmWlzIiEpdLr47bu7iLIo/vcbed1OtKZlC5CXk0pts5dHPnSSlRLLv/11OxbVfXwh9LMzYwTj0xO7TXENFR/z/QwGy1myhoROMb+My9c6O1zYVqVYuf4QxxrdDE+IHshhCj0gtN/+nopGUuNs1DQbG6UXH61neUgwM3RlXOh0BfcMeO7TgwxPiMai4JqpWby2tazbrKATYVoEFXVGI7fQhm7dUVRay/D4aBwjEkmMiepxFs7UnFQA7rhoHD94aRs+PyREW3nilpkAXY7f/OwWnDsaOF6v0FXblc5ELFLaSHRHjywCpdT3lVLJyuAppdQWpdSl4R6cMPCErqpMvnKWYc6/tvVwm/ul0jgyKXDY+e4FubywoYSkWCtKwfnjhgNwyZkZnU5SpovjqrMzGZkcS5RVUVLdzMUT04mLtnLxpPQ2nTN7u+o1YwTldYYlUHECITBX5ldOyaSstoWLJ44IXm8nsiKWr3XS7PGiFKzZVRl0Y00dlXrCKl/zsWc/OQDAy1tKh2T9TE9dQ9/VWtcBlwLDgIXA/4RtVELEsnytk7oWLwC/fXcX971axBPrnNz3alEwY0QEIfIwJ9r6Fh9zJ6azq7yB6/KzeTuQSdMe08Vx9bQsaps91DZ7sSpIiYvmnR3l3D57XIe0yd7Ei8ysIVMI6lu8tHi6bkt9vB2EEVweFm/rcfFZXk4K//rXIjJTYtmw/xiqE5dQd+MvcNhZeN4YAG45P3fIiQD0XAjMPdeuBFZorXeE3CecRuTlpPCz13cQG2Xh3LHDeWlTCb96q5hXNh/mj98+HlOQ1hORRW5aAgAXOOy8trWMu+aO4/ffmsZTi2Z26mM3LcECh53f3ZAHwMSRyazZXXnCDJueYFoE5qICuncPmSvzVRsOkRpn45dv7ezxytx8rumGionqPuW0PYVOFy9uMrLnVm0YHFlAvaWnQrBZKfUuhhC8o5RKAvqmmbgwqDC/VB6/5sMQM9vt8/O793ZJ64kIpNDp4vfvG+2Sx6TFd9gQ5URunSvPzuLGc0bx5ZG6PsuLNwvKAEYPNzaDqahv7XaDnOmjh+H3a2qaPb0eR4HDzllZyUD3KaftGaxZQL2lp0JwG/AfwCytdRPGRvS3hm1UQkRT4LBzdpax4o+2WvjeXAcK2HKoZtAU0JxOFJXWcvdcY+X+9Rk5HfL9e5Kp886O8j7Niw/di2DSyCTAsAhMF9B9rxSxdndFm/5Dv3xzBz4NV+dl9nochU4XpdXN3DNvPM/3ovHbYM0C6i09zRo6H9imtW5USt0MTAf+L3zDEiKZQqeLvZX1xNos2KwWUhNs2KwKt0/zTOGBLvu1CAPDkjkO3v+yHABbwCXT00yW9r10TiZDqDNCLYJJmcm8+2U5lfUtXD4ll2UL8vnuMxt5YWOJ4ca5ZRY7ymp5fn0JCvj11/MoKq3p8ThO5T0M1iyg3tJTi+BRoEkpNRX4IeAEngvbqISIxfxSXT01i6cXzeKeS8bzwFvFfDWQSTR3QvqQNJ0HO+YmMLZe7goWrhWxNUQIJmQkYlHHYwQFDnswLbnV6+fHr33OQ+/uYWRyLLNyh3dIGx2o9zCU6KlF4NVaa6XUtcAyrfVTSqnbwjkwITJp/6UqKq3l/vmTOOhqAuD66TnceO7oiOuueLrjCbRkMC2CnhKuFXHoONITYxieEBPMbHr7iyOU1bZwTu5wNh08xoGqJqaPTmVrSQ0Lzh3dZuvMnozjdFnVnwo9FYJ6pdR9GGmjs5VSFow4gXCa0f5LZf5d0+Rm5fpD7K1oYPFF4+RLFmF4vCdnEYQLq+X4ONISYxiRFENlfSuFThc/fGk7YLSu+PxwDc0eP1sO1QCQEkgblX20+5aeXhXfAlox6gmOAjnAg2EblTDoSI2Pxp4Yzd6KhoEeitAJXr8hBH2xYXxfEBojSEuIptnjY7+rkaLSWiZkJGFPjOaBf+7ka/nZ3PvVCcFjf/vOLslKCwM9uioCk//zQIpS6iqgRWstMQKhDY70RPZWihBEImaDud66hsKFWUdgtShS4myMGh7H/qpGJmQksuNIHWmBGMHVU7O455Iz+PasUQCcnZ0iIhAGetpi4gZgA/BN4AZgvVLqG+EcmDD4GD8ikb0VDWh98puSC+EhGCy2RIpFYIwjJsrCZ/urmJKVgsIoRnR7/Ryoagpu6FLodPHul0b6avHReklECAM9jRH8GKOGoAJAKZUOvA+8HK6BCYOP8SMSqW324Gpwk54UM9DDEULwBiyCqEixCAKuobSEaKOnUV4mfg3NbqPNxJVTMjvdCrKv0leFtvR0eWAxRSBAVS+eK5wGLF/rpDUQkDTjBNJzKHJwn2T6aLgw00dHDY9n2YJ8Xt1iNDDUwBVTRrJ2T2UwO0hSP8NPTy2Ct5VS7wAvBP7+FvCP8AxJGIzk5aRw90pj0+69lQ1otGR3RBDeYIxg4IVg+VonowJ7GwxPiKbAYWdiRiKbD9VwgcPOozfP6HRTeBNJ/ex7ehos/jfgcSAv8PO41vrfwzkwYXBR4LDzp5uMTbv/urFEzPcIw+PzY1FtC7kGirycFH782hcA2BNjeGKdk82HarhwvJ2dR+tOurW1cPL0eGMarfUrwCthHIswyCkYbycrJZaiw7XcM2+8iEAE4fH7IyZ1tMBh58Gv57F4xWY+21fFrqP1/DiwyU17S0Cuof6h2ytDKVWvlKrr5KdeKSV7FQptKHS6ONboxmqBFZ8dlOyOCMLj1cHtFiOBS87MYNLIJIqP1vO1/CwWzzYKE8USGBi6vTK01kla6+ROfpK01sn9NUgh8jFXcrfPHovPD/9xxSTpORRBeP3+iMkYAvhsfxUV9a2BPY9dba6TU9nnQDg5ImeJIAxqzOyO66bnAKCUkpVdBOHx6YgIFMPp0+N/MCGb1wt9grmC8/s1iTFRfF5ayw0zR4mPN0Lw+PzYIiBQDN13A5XrZWAQIRD6FItFMSU7mc8PiyUQSXh9kRMslm6gkUfYrgylVKxSaoNSartSaodS6uedHBOjlHpRKbVXKbVeKZUbrvEI/cPytU7SEqL58khdsK3Bfa8Wcd+rRW2Ok2Kz/sVwDUWGRSBEHuFcIrQC87TWU4FpwOVKqfPaHXMbUK21Hg/8HvhNGMcj9AN5OSms2e3C7fWzu9zoC/Nm0RHeLDoS9AGHbj8o9A8enz9iYgRC5BE215A2Oo+ZrShtgZ/23ciuBX4WuP0ysEwppbR0LRu0FDjs/Pe1k/nBS9v53bu72VZSw2MLZwDwvee3cNGEdNbtcUmxWT8jQiB0R1ivDKWUVSm1DagA3tNar293SDZQAqC19gK1QFon57lDKbVJKbWpsrIynEMW+oCv5WdjsypWF1cEN7MvcNg5Z2waf99WxlfOHCEi0M94/Tqi0keFyCKsQqC19mmtp2FsZHOOUmrKSZ7nca31TK31zPT09L4dpNDnfLqvCq0hKzWWlesPUeg08sTX7jb6Foa6iYT+we0Vi0Domn65MrTWNcCHwOXtHjoMjAJQSkUBKRidTYVBiun/nzMhncZWH8tuzOfOFZu5c8VmJo5MAuDC8XbJG+9nvH4JFgtdE86soXSlVGrgdhzwVaC43WGvA7cEbn8DWC3xgcGNmSM+Z2I6tc0exqYncFVeJlflZVJea2xOXtvskWKzfsYrMQKhG8JZR5AJ/FkpZcUQnJe01m8qpX4BbNJavw48BaxQSu0FjgHfDuN4hH7AzBE3d6AqPlrPr6/Po6bJzQsbSoiyKIqP1nP+uDSJE/Qjbp8O/k8EoT3hzBoqAjo0o9da/zTkdgvG9pfCEGNihuEGKj5Sz8UTR1B8tB6Aiyaks7q4gvK6VkamxA7kEE8rvD4/0VHiGhI6R5YIQlhIibeRlRLLrqNGk9pdASG4dloWAMVHpXltf+Lx+cUiELpErgwhbEwMtBkGw0WUGm9jzgQj68sUBqF/8PgkfVToGhECISwsX+skKdbG7vJ6PtpdSfHROmwWxW/eLmZYvI3Xt5cB0mqiv/D4/BG1H4EQWciVIYSFvJwUPigux69h6Qtb+KK0lpomD3/fVkZdi4f6Fq+0muhHpKBM6A4RAiEsFDjs/NfVkwFocfvw+DVTslOwWhRWZaG0uonvPb9FWk30Ex4pKBO6Qa4MIWx8c0YOF09Mx+3TJMZY2VpSw60FuVw0wY5fw9V5WSIC/YTHL0IgdI1cGULY+HRfFdtLa7kuP4vGVh/X5WfzTOEBCp1G8fjfth2W6uJ+wittqIVuECEQwoLp/79r7jjW7nZx//xJvL+zHJ9fY05HN84aLa0m+gGttREjkPRRoQvkyhDCgtlqwueHZQvyWTzbwVV5mVw7LYvlC2eggNhoq7Sa6Ac8PqNrS3SUfN2FzpGtKoWwYLaaCI0B/Pr6vODtkSmxlFY3UeCYIHGCMGPuFBcVIXsWC5GHLBGEASFnWByHq5sHehinBd6ARSDBYqEr5MoQBoTs1DgO14gQ9AfugEUgwWKhK0QIhAEhe1gcR2tb8Pr8LF/r7BAwlorjvsPrD7iGxCIQukCuDGFAyBkWj9evKa9vJS8npU320MlUHIuYdI3HK64hoXvkyhAGhOzUOAAOVzdT4LCzbEE+t/95E998tJClq7b2uuK4L8RkqOLxi2tI6B7JGhIGhOxhASGoaQKGU+CwkxBjZePBau6ZN77XmUSmmNy5YjPTRqWyo6xO2lcE8ARjBLLuEzpHrgxhQAi1CMBYwbvq3ShgxWcHT6rIrMBhJ2dYHOv2uLj53NEiAgHMrCFJHxW6QoRAGBBibVbsiTGUVjdT6HTxvee3oAEN/PzaKSdVcVzodLG3ogGAlZ8dkorlAEGLQArKhC6QK0Pod8zAbvYwI4W0qLSW6/Kzg49npsT2uuLYjAmMsycA8Juv50n7igBmZbFNWkwIXSBXhtDvmIHd2CgLh6ubyctJ4aVNpcHHzQCyWZ3cE8yWFn5jzmNSZpK0rwjglToC4QSIEAj9jhnY3V5aw4GqRr73/Bbmn50ZfPxkCs2WzHFQ4LBT2+wBoK7F02sxGaqYBWVSRyB0hVwZwoBQ4LAzZ0I6fg2XTR4JgD0xhuEJ0ZSeZOsJrTU1phA0e/tsrIMdM1gsW1UKXSHpo8KAUOh0sX7fMQBe315GbloCY9LiafX6Trr1RIvHj9trrH7rWjx9NtbBTrDpnLiGhC6QJYLQ75iB3T/dPJ3MlFimjkql+GgdcTYr2alxlJ2kEJhuIYC6ZhECE4/frCwWIRA6R4RA6HfMwG6Bw870McNwVjTg16DRZKfGc7i6Ga11r89b0+wO3q5vEdeQiccrBWVC98iVIfQ7ZmAXYOaYYVTUtwJwfX4O2cPiaPb4qG7q/Yq+JuQ54ho6jjSdE06EXBnCgLF8rbONu2JMWjwNgZX8Pz8/0ubYnjSQayMEEiwOEqwjENeQ0AUiBMKAkZeTwu/e3R3MZqmoa+Gpj/cB8Ot/Fve6gZwZF7CogbMIIrELarCyWArKhC6QK0MYMAocdh65aTp+rbFa4Id/LeL22eMAmDdpBN95agPXLvuY257d1KMGcmaMIDMljvoBEoJI7IIa3KFMWkwIXSDpo8KAUuCwc8WUkbxRdITLp2bwbOEBoq0WXt9eBsD20lomZyV3eF6h00VRaW2bgrGaJg9WiyIzJXbAXENmsdxdK7eQHBtFQ6uXR26aPqAN8NyyZ7FwAmSJIAwohU4XnziruGfeeNbudnHX3HH4/BpzzoqNsnDoWBN3rth8wlV2bbOH1DgbKXG2AQ0WFzjs5I9OpaS6mbkTRwx4F1TZs1g4EWIRCAOGOaGbbp/zHGksXbWVKdnJQUtgR1kdf7xxOj94aSvfeWoDZ2Uls9/VyGMLZwQnWNM6qGn2kBJvIznOxu6K+gF9X5/tqwLgnR1HKXS6BlQMPD4/FgVWsQiELgjbEkEpNUop9aFS6kul1A6l1Pc7OWauUqpWKbUt8PPTcI1HiDxC6wnAWEnfNXccO8rquC4/i4NVTQCkJtj4wVcn4PVrikprg8FPrTVPrHNy27ObyMtJobbJsAiSYqMGrI7AFLcCRxoAF09MH/AuqB6/X6wBoVvCaRF4gR9qrbcopZKAzUqp97TWX7Y7bp3W+qowjkOIUNo3hCt0unh0zT6eu+0cChx2Xt9exj0vbOWfnx9pM7H7fJpbn9mI1sZq9/75kyhw2HngHztJT4whOdZGXbMHrTVK9e8q2BS3pz/eD0CrVwe7oA6UVeDxahECoVvCdnVorY9orbcEbtcDO4Hs7p8lnM60txCuzssk1mbh4z2VvLbtMDnDYnGkJ+Dxa1q9ftw+P5Myk1g82xCU2mYPqfHRJMdF4dfQ6PYFz91faZ1msZxZJHegqnHAu6B6/X7pMyR0S78sE5RSuUA+sL6Th89XSm1XSv1TKTW5i+ffoZTapJTaVFlZGcaRCgNJaMUxgFKKMzOTafb4sVoUcyeO4LffnBoMJNusit3lDcEJvqbJQ0qcjeRYG9C231B/p3VWBoTgUFUTPn/v22X0JR6fWARC94T96lBKJQKvAP9Pa13X7uEtwBit9VTgj8BrnZ1Da/241nqm1npmenp6eAcsRBQTRiRRWtNMi8fPrNzhbDp4DK3huvwsoiwWfH7N3Su38PEeF/UtXlLjbSQFhCDUnWSmdd72503c92pRmyB1X+P3a1wNraQlROP2+U+6iV5f4fH5sUmgWOiGsAqBUsqGIQLPa61fbf+41rpOa90QuP0PwKaUkh3HBcBw50RHWTD7z1mU4sF3dnPxpHQmjkzmloIxAJw7bjgbDxgtrb84XEtynBH6ap9Cmhxro9nt44UNJb3a3L63bqWaZg8en2ZW7nAA9rsae/aGw4TX55diMqFbwpk1pICngJ1a64e6OGZk4DiUUucExlMVrjEJg4u8nBRe23YYgOzUOAqdLmKiLNw+e1xwe8u0BBu7jtYzJi0egAkZSZ26hgB+/95uAHLT4lm5vueb2/fWrWS6hc4ZGxlC4PFpKSYTuiWcy4QLgIXAvJD00CuVUkuUUksCx3wD+EIptR14GPi2Ppn+w8KQpMBh5zfXnw1AYmwU7+woD9YPmK6eRrePg1VN/PTvOwCYlTucpNjjFoG5ml9dXM4HxRWAETy9bHJGj9M6zde647nNfO/5LSd0K5lCcFZWMgnR1rALwYksFo9P0keF7gln1tDHWmultc7TWk8L/PxDa71ca708cMwyrfVkrfVUrfV5WuvCcI1HGJxccXYm00alsutofQd3ToHDzjem56CBmIDrwywoAyNGYK7ml68xmtk50hMorW7h6rysXm1uX+CwExdt4a3Pj5zQrVRR3wLA4eomcu0JQSEIV/O5E1ksIgTCiZCrQ4hoPt1XxaFjTdwzb3wHd06h08UrWw4zalgcVY1Gw7nUOBs7yozJva7ZE1zNbzlUzbB4G0dqjUl64sikXqV1FjpduBrcpMbZTuhWMi2C/35rJ4mxURyoagxrltLx/kab+enfv+hgsXj9WlpQC90iQiBELKEtKO69dCLLFuQHV77mY/deegZ1LV7Mae6N7WX84MXt2KyKukDW0OTMFLx+TXWTh3mTRgBQUt3zTJ5Cp4ulz281XkPRZhydUVnfSpzNyiMLprO9pIZDVU187/ktJ52l1JNgdYHDjkUpnvv0YAeLxe31y6Y0QrfI1SFELJ21oDDdOeZji2c7ePTm6URHWbAqWL52H8sW5JMaHx0MFj/32QEAvj49m3V7jDqU0uqmXo3jgeun4NdG0dq5Y9PauJXaT9QV9a0kx0VRdLiW88eloYHr8rNPOlW1J8HqtbsrqG7yYE+M7mCxeP06uOeDIHSGXB1CxNK+wAwIunNCHytw2Ln9wrH4NEwfnQpAcqDfUKHTxcMf7MGi4IHrz+b335oGwMd7et77Z8kcB7n2BAC0Pu5yMt1K5kRtnnNvRQNVDW6sFth4oBqAV7YcPul+Q6YA3rliM+c+8H4H66LQ6eKeF7YGx9feYvH6pLJY6B4RAmHQU+h08cLGEu6ZN56iw7XcuWIzKrBL2Rvby/D5NWdmJhMTZWXepAwSY6wUH+1dd1LT7w9Q3WTEI0xLoMBh555541n41Hq+/fin7DxSx/j0BB5ds497LhkPwD2XjD+l5nMFDjsTM5Ior2vlmqlZbQSyqLSWWwpyAahqdDNzzPA2Fovbp4mS3cmEbpCrQxjUtI8jPLZwBgDOyka2l9Tw9hdH8Wu4bPLIoF99rD0xmFnUUzoTAtMSePuLIyxfuw8NfLbvGFYLJMREtVm1Z6fG9ypLqbP3aT73b1vbWhdL5jiwhDTXq6hvaWOxeH1+oqPEIhC6RoRAGNR0Fkd4bOEMRiTFUNfiDaaVJsZEBf3qOcPiehUjgHZC0OgJvtayG/P5lxe2Ul7XEnzc6wfHiEQKHHb2BVJHa5vdJ918zhS7UcPiALj74o7WxZ6KhuDt8rrWNs/3+PxiEQjdIleHMKjpLI4ARuvljOQYjta1YgH+uHpPUDBGDY+ntLoZfy+awXVmEQBojMpdDRQ40oLZS28VHeGJdU7+6+9fAEZDvJPFFLtjTeaezLEdrAtnRQNZKbEAbUQJpOmccGLk6hCGFEFX0U35/PP7FxFrs+CH4JaRy9c6cXv9uL1+3v7iSPA5pr+/q4IvV0MrwxOigbaT+u/eNdpWXDs1i+Kj9Vw0wRClvJxUHnp3D48smI7Nqqg+BSFYMsfBlOyU4DlqmjwdXD/7Khu5YLzx2h2FwC91BEK3iBAIQ4pQV1Hx0TpibVauOjuTt78wtozMy0nh5c2lAPzHq5/zxDonS1dtxWqh+/5BDa3kpsUTZVF8UFxOodPF37aWsuVQNV+blsW3zhnFZZMzKCqt5cyRSXy6r4rFs8dSMN5Oanw0tc3uTs/bFe1TUg9VHXdltbcuSqqbcfv8zBo7HJtVdXANGQVl8lUXukauDmFIYbqKTMvgTzdNZ9lN03lq0UyWrjJSLP/r6rMAY6X8q7eKmZyVzKNr9p2wf9CIpFhS46OJj7YG2lY4sSj46llG36Krp2Zx98UODlS1rYROjbP12jXUvnbg/S/Lg4+1z1raG4gPnDEikZRYW7BWwsTjlfRRoXtECIQhSXfFaNdOyyY7NS6w4Q2s2+M6Yf+gyvpW0pNiGJ5gI9pqZdmN+ewub8CRnsh//n0HyxbkA/Domn08tWhmm0poi+p9jKDAYefBb+Sx6JmN/PLNHTz2kdErKTXeRm2gUM4Ui/d3GiJR1eimprnj63j8fikoE7pFrg5hSNJdMdqmg8do9viYnJWEz2889mzhgWDrivZxArfXT3WTh/SkGFLjo6lucuMYkYjGyNYxRaQr8fHptgHmnhJlteD2+nnq4wM4RiSQGm9j9PD44LnM8/9t62ESoq386OUiZoxJpdnja3Mer0+LRSB0iwiBcFphuozumjuOQ8eaibNZAchKjeXOFZu5c8XmDnGCqkbD556eFMOweBvVTW7e2F4GwNemZQddQF2JT/6o1OAqvjd85jS25shIjqH4aD3D46NJaedmmpqTisLYn/nmhHADQwAAD6tJREFUc0dzVlYKFSExAq21xAiEEyJXh3BaYa7afX54bOEMnlo0k2irheKjDfi15qq8TIpKa9sEas3U0U0HjjEsPpryulYeCmxy82+XTzxhE7rU+OOTd093Oyt0uni28ABg1AXERFkorW7G59ccDHQzBfjN28W0ev1cNCGdJ9btp8Xjo6HVy+piw13k8RkpskdqWsLSAlsYGogQCKcV5qrd/F3gsLPg3NGAsbr+9fV5HQK163Ybwddpo1IZlhBNfYuHORPSiYmykJkc2yb+0Bmp8dE0e3y0eHw93u2sqLSW0cPjgn83tvq4YspIKutbaGj1snTVVh5b62TlZwfJGRbLF4druffSM3g9YKn84MXt3PdqER/vNcb++vYy8nJSwrYngjC4iRroAQjCQFLodPH69jIykmJYv/9YsHfQsgX53PL0BhKio3AHAgnzzszgkQ/34NdwrNHNWHsClsAWkKaodEZKoJ1Fbcj+CN99ZiMTRyZRUt3cabbSkjkO1u2uZE9FAxlJsRypa2FEcgzvfNmCx6d5+Nv5fPfZjfg11DR5efw7M4Ln+NVbxdw918Fv39kVTJX9xowcgGA7DkEIRSwC4bQltE/RLRfk4vNr7l65JSgGcTYrNc0eWr1G8HVvRT1/32qsuLeX1jA20JH0RAyLb1uINn30MLx+zfbSWm7qJltJKcW49ASqAzUIf9lYwjcDE/rkrGRGB/ZpvrUgN1gsZ77Wa9vK8Ph10DVUcqzphFtsCqcvIgTCaYsZLygqrSUjKQaAa6dlUVRay4NvF1PX4iUh2orPDzaL4gcvbmfJXKOat8Xj77EQpMYbFkFNINvn6U/24w20t3ju04PdbnAz1p7IDTNHAXDTuaOZPsZos/3hrgr2uxoZkxbP8xsOBYvlfvXWTgB2HqkjyqKCm9av2V15whRZ4fRFhEA4bTHjBHk5KfzqH8XkDItlV3k9Vgs8ssbwo7/+LxcyLN6Gx6+ZMyGdAkda8Pnj0hN79Dqma6i6yUOh08X/vb8n+NjC80Z3GWh2NbTi82veLDrCPfPG89KmUo7UGO0jfvb6DqxKccmkjGCwGuCRm6YHnx9rs/DvV0wkMSaKWJuFZwIpsoLQHhEC4bTH9NsfrW3ls33H+N07uxkzPJ4JGYmsLi6nxePnqjyjTYW5ET3Qc9dQoEfRloPH2F5SQ0KMla+cOQKbVfHZvmOdBpq9Pj9VjW4KnVVttuo0A73fnDkKt8/P2PSENsHqAoediSOTAKPi+dE1+3j8OzN4etEsrsrLPKU9EYShiwiBIGCIwVfPNPYzRkFpTTP2xBgeeKuYey89g2ULpvO1/Cz+6/UdweeMsyf0KAsnNWARrPjsEClxNo41enCkJxq7nbW0bSBncqzRcCPdOGtUmwK1n18zGSBYlzAuIEbmOQqdLirrW7ln3nje/qKcu+aOCwayf3193intiSAMXUQIBAEjcLz+QDUXT0ynxePH59dsPljN/fMnsXi2MUlfPTUruAFMaryNnUfrum1UZxIfbcVmVXzlzAx++abhw39xUwmXnpXBwaomPGZ5cwiVDUbtwjljh7e5/+JJhlhtPWRsgRlqlbTfpOepRTN5dM2+NhbAye6JIAxtRAiE057QCfSZW8/hwvFGHODK/9/enQdJVV1xHP/+BhhWYcABHZawjGAFFUHHCBgSdw1lXFKauIQoWStlmYipJBKzVPJPyqyaihUwiYkLUdSgsagkLsSiSgngQAARHRYBHYptXFgVWU7+uLdnmp6eBZju1/rOp2rKfks/D2f69Zl33333nlrVWASgadIbAd06l7W7F44k+nQvp2fXTnTvEk65U6p6Uz2gF/sOHKIuTpuZfXXRsDtcEVTGm9gZvbt1QQozsHXrUsaJvbs1bmttfCXnWuOFwKVe9hfognUNrNq8i6vGDeLfr2xp1p4+sbqSgRXd2LJz3xH1wunbowvz67bz9t79nDuqPys27eC+F9cDoSvq9DkrDhveoiE+zZw96ihAWZkabz4Pr+zV+BwDtD6+knOt8QfKXOplviizrwwmVldyTU1Ds7/6F6xr4L39h7glDjM9vvr4dhWDMsHmne9TJvj150+nbusuvvHgEgDufm4N731wEAR73j/Ai2saeGDhBgDOHtGv2bH69ijn3b37G+8POHesvBA4F7XWtJI9x0FmnwnVx7fZPDRj/jrGDO5DfK6LSSP7U7d1FyvqdzBzyplMm72MrTv3MbyyJ2OH9OFrDy6hc5koE3TpJM47eUCzYzZdEXghcB3Dm4aci9pqWjmaNvjM2EKDKkJb/uiBvQ+7wbz/oDFyQE/WN+zhifjU8oFDxvDKnpzQuxtSU9NPZsC6vj2aCoGPHeQ6ghcC59rpaNrgM8Vi0fp36FImHln8RuNYP5mriWemfZrTBvUGoFNs81+7LdwMzpYpKplhI3Z/cKBdvZaca4sXAucKbGJ1JVMnDmX/IWPK+KHNJrH57+tvseGtvZR3Lms8IQ+asfGtvc26fv7++nG8tOFtAO56drWPHeQ6hBcC5wpswboGZtfWHzaPce7cypeNqeKvU8/i6jMHk2kMmjSyf+PcCJnmn4nVlUw+rQqgsag4d6y8EDhXQLkPeeVOYpO5Mvj558YwsbqSz44d2Dit5KmD+jQ2B2Wafxasa2D+6u2HFRXnjlXBCoGkIZKel7RK0iuSvp1nH0n6naS1klZIOiPfsZz7sGrrBnPufYeJ1ZVMu2hUeO+b7x7WK6mtouLc0ZKZFebAUhVQZWZLJR0HLAGuNLNVWftMBm4BJgNnA3eb2dmtHbempsZqa2sLErNzpeCDA4e47o8LWbLxHb51/kncdvHJQFNX1OzCsWBdAyvqd/hDY65NkpaYWU2+bQV7jsDMNgOb4+tdkl4FBgGrsna7AnjAQjVaKKlCUlV8r3OpVLvxbdY37Gls/sk8tJbvy761mdGca6+i3COQNAwYByzK2TQIeDNruT6uy33/1yXVSqrdvn17ocJ0LnHe/OOSUPBCIKkX8HfgVjPbeTTHMLN7zazGzGr69+/fsQE6V0J84DiXhIIOMSGpC6EIzDKzOXl22QQMyVoeHNc5l0re/OOSUMheQwL+DLxqZr9pYbengC/F3kPjgR1+f8A554qrkFcE5wBTgJclLYvrfgB8DMDMZgD/JPQYWgvsBaYWMB7nnHN5FLLX0AuA2tjHgJsLFYNzzrm2+ZPFzjmXcl4InHMu5Qr2ZHGhSNoObDzKt1cCpdghu1TjgtKNzeM6Mh7XkfkoxjXUzPL2v//QFYJjIam2pUesk1SqcUHpxuZxHRmP68ikLS5vGnLOuZTzQuCccymXtkJwb9IBtKBU44LSjc3jOjIe15FJVVypukfgnHOuubRdETjnnMvhhcA551IuNYVA0qWS6uK0mLcnGEfeKTwl9ZP0rKQ18b99E4qvk6T/SZobl4dLWhTzNltSeQIxVUh6XNJrkl6VNKEU8iVpWvwdrpT0sKRuSeRL0n2StklambUub36KOT1sC3H9Mv4eV0h6QlJF1rbpMa46SZcUM66sbd+RZJIq43Ki+Yrrb4k5e0XSL7LWd1y+zOwj/wN0AtYBI4ByYDkwOqFYqoAz4uvjgNXAaOAXwO1x/e3AnQnFdxvwN2BuXH4UuDa+ngF8M4GY7ge+Gl+XAxVJ54swgdJ6oHtWnm5KIl/Ap4AzgJVZ6/LmhzDI478I44CNBxYVOa6Lgc7x9Z1ZcY2O52VXYHg8XzsVK664fgjwNOGB1coSydd5wHNA17g8oBD5KspJk/QPMAF4Omt5OjA96bhiLP8ALgLqCHM8Z4pFXQKxDAbmAecDc+OHvyHrxD0sj0WKqU/8wlXO+kTzRdPsev0IgzfOBS5JKl/AsJwvkLz5AWYC1+Xbrxhx5Wy7ijBXSbNzMn4hTyhmXMDjwOnAhqxCkGi+CH9YXJhnvw7NV1qahto1JWax5UzheYI1zcWwBTghgZDuAr4HHIrLxwPvmtmBuJxE3oYD24G/xCarP0nqScL5MrNNwK+ANwhzc+8AlpB8vjJayk8pnQtfJvy1DQnHJekKYJOZLc/ZlHS+RgGTYnPjfElnFSKutBSCktPaFJ4WSnxR+/VKugzYZmZLivn/bYfOhMvlP5jZOGAPoamjUUL56gtcQShUA4GewKXFjKG9kshPWyTdARwAZpVALD0Ic6X8OOlY8uhMuOocD3wXeFRSq8P7H420FIKSmhJT+afw3CqpKm6vArYVOaxzgMslbQAeITQP3Q1USMrMW5FE3uqBejNbFJcfJxSGpPN1IbDezLab2X5gDiGHSecro6X8JH4uSLoJuAy4IRappOOqJhT05fHzPxhYKunEhOOC8PmfY8FiwtV6ZUfHlZZC8BIwMvboKAeuJUyTWXSxmuebwvMp4Mb4+kbCvYOiMbPpZjbYzIYR8vMfM7sBeB64OsG4tgBvSjo5rroAWEXC+SI0CY2X1CP+TjNxJZqvLC3lJ9HpYSVdSmh+vNzM9ubEe62krpKGAyOBxcWIycxeNrMBZjYsfv7rCR06tpD8dLpPEm4YI2kUobNEAx2dr0Ld9Ci1H8Ld/9WEu+t3JBjHJwmX6SuAZfFnMqE9fh6whtBLoF+CMZ5LU6+hEfEDthZ4jNh7ocjxjAVqY86eBPqWQr6AnwKvASuBBwk9OIqeL+Bhwn2K/YQvsa+0lB9CB4B74nnwMlBT5LjWEtq2M5/9GVn73xHjqgM+U8y4crZvoOlmcdL5Kgceip+xpcD5hciXDzHhnHMpl5amIeeccy3wQuCccynnhcA551LOC4FzzqWcFwLnnEs5LwTOFZGkcxVHdnWuVHghcM65lPNC4Fwekr4oabGkZZJmKszTsFvSb+O48PMk9Y/7jpW0MGuM/czY/ydJek7ScklLJVXHw/dS0/wKswoxdoxzR8ILgXM5JH0c+AJwjpmNBQ4CNxAGlqs1s1OA+cBP4lseAL5vZmMIT59m1s8C7jGz04GJhKdGIYw4eythTPkRhDGKnEtM57Z3cS51LgDOBF6Kf6x3JwzadgiYHfd5CJgjqQ9QYWbz4/r7gcckHQcMMrMnAMzsfYB4vMVmVh+XlxHGoH+h8P8s5/LzQuBccwLuN7Pph62UfpSz39GOz7Iv6/VB/Dx0CfOmIeeamwdcLWkANM7/O5RwvmRGFr0eeMHMdgDvSJoU108B5pvZLqBe0pXxGF3juPfOlRz/S8S5HGa2StIPgWcklRFGg7yZMCnOJ+K2bYT7CBCGeZ4Rv+hfB6bG9VOAmZJ+Fo9xTRH/Gc61m48+6lw7SdptZr2SjsO5juZNQ845l3J+ReCccynnVwTOOZdyXgiccy7lvBA451zKeSFwzrmU80LgnHMp93/knKoBucBROQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Hbwo4Lib0DS",
        "colab_type": "code",
        "outputId": "5ba88442-e80c-438d-84ca-75293cd3a320",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plot_accuracies(history)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dt5LjaLab6dc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_architecture = repr(model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJCLU5OCcrMC",
        "colab_type": "code",
        "outputId": "046266ec-550f-4d9e-a16b-4e14248d40c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "model_architecture"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'trafficsignalModel(\\n  (linear1): Linear(in_features=3072, out_features=1324, bias=True)\\n  (linear2): Linear(in_features=1324, out_features=500, bias=True)\\n  (linear3): Linear(in_features=500, out_features=256, bias=True)\\n  (linear4): Linear(in_features=256, out_features=128, bias=True)\\n  (linear5): Linear(in_features=128, out_features=64, bias=True)\\n  (linear6): Linear(in_features=64, out_features=43, bias=True)\\n)'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcNvLAL9ct21",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}