FlowerClassifier_Batch32_Dropout_Acc92.ipynb

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      "name": "FlowerClassifier_Batch32_Dropout_Acc92.ipynb",
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      "metadata": {
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      "source": [
        "# Developing an AI application\n",
        "\n",
        "Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. \n",
        "\n",
        "In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below. \n",
        "\n",
        "<img src='assets/Flowers.png' width=500px>\n",
        "\n",
        "The project is broken down into multiple steps:\n",
        "\n",
        "* Load and preprocess the image dataset\n",
        "* Train the image classifier on your dataset\n",
        "* Use the trained classifier to predict image content\n",
        "\n",
        "We'll lead you through each part which you'll implement in Python.\n",
        "\n",
        "When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.\n",
        "\n",
        "First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here."
      ]
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "# !ls \"/content/drive/My Drive/\"\n",
        "\n",
        "import os\n",
        "os.chdir('/content/drive/My Drive/Colab Notebooks/pytorch_challenge')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "db3ZnI_kyTm9",
        "colab_type": "code",
        "outputId": "482b4251-288b-4ba7-9aeb-f6dc82702c5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "# Install PyTorch in Colaboratory\n",
        "# http://pytorch.org/\n",
        "# from os.path import exists\n",
        "# from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
        "# platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
        "# cuda_output = !ldconfig -p|grep cudart.so|sed -e 's/.*\\.\\([0-9]*\\)\\.\\([0-9]*\\)$/cu\\1\\2/'\n",
        "# accelerator = cuda_output[0] if exists('/dev/nvidia0') else 'cpu'\n",
        "\n",
        "# !pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.1-{platform}-linux_x86_64.whl torchvision\n",
        "!pip install torch torchvision"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.0.0)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.2.1)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (5.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6dl0fstCyTnC",
        "colab_type": "code",
        "outputId": "897016bb-41e5-45ac-844d-6a2626562005",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# Imports here\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms, models\n",
        "import numpy as np\n",
        "import time\n",
        "\n",
        "!pip install pillow\n",
        "# !pip install pillow==4.1.1\n",
        "%reload_ext autoreload\n",
        "%autoreload\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "print(torch.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (5.4.0)\n",
            "1.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0s8ZYRH5yTnE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Load the data\n",
        "\n",
        "Here you'll use `torchvision` to load the data ([documentation](http://pytorch.org/docs/0.3.0/torchvision/index.html)). You can [download the data here](https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flower_data.zip). The dataset is split into two parts, training and validation. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. If you use a pre-trained network, you'll also need to make sure the input data is resized to 224x224 pixels as required by the networks.\n",
        "\n",
        "The validation set is used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.\n",
        "\n",
        "The pre-trained networks available from `torchvision` were trained on the ImageNet dataset where each color channel was normalized separately. For both sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`, calculated from the ImageNet images.  These values will shift each color channel to be centered at 0 and range from -1 to 1."
      ]
    },
    {
      "metadata": {
        "id": "_4JuxitlyTnF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_dir = '/content/drive/My Drive/Colab Notebooks/flower_data'\n",
        "# data_dir = 'D:/flower_data'\n",
        "train_dir = data_dir + '/train'\n",
        "valid_dir = data_dir + '/valid'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lk2OVKr4yTnH",
        "colab_type": "code",
        "outputId": "dc6526f5-45f2-4c59-9188-96c973b51ee3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "cell_type": "code",
      "source": [
        "# TODO: Define your transforms for the training and validation sets\n",
        "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "\n",
        "train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
        "                                       transforms.RandomResizedCrop(224),\n",
        "                                       transforms.RandomHorizontalFlip(),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       normalize])\n",
        "\n",
        "valid_transforms = transforms.Compose([transforms.Resize(256),\n",
        "                                      transforms.CenterCrop(224),\n",
        "                                      transforms.ToTensor(),\n",
        "                                      normalize])\n",
        "\n",
        "# TODO: Load the datasets with ImageFolder\n",
        "train_data = datasets.ImageFolder(train_dir, transform=train_transforms)\n",
        "valid_data = datasets.ImageFolder(valid_dir, transform=valid_transforms)\n",
        "print('class_to_idx:')\n",
        "print(train_data.class_to_idx)\n",
        "\n",
        "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
        "trainloader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True)\n",
        "validloader = torch.utils.data.DataLoader(valid_data, batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "class_to_idx:\n",
            "{'1': 0, '10': 1, '100': 2, '101': 3, '102': 4, '11': 5, '12': 6, '13': 7, '14': 8, '15': 9, '16': 10, '17': 11, '18': 12, '19': 13, '2': 14, '20': 15, '21': 16, '22': 17, '23': 18, '24': 19, '25': 20, '26': 21, '27': 22, '28': 23, '29': 24, '3': 25, '30': 26, '31': 27, '32': 28, '33': 29, '34': 30, '35': 31, '36': 32, '37': 33, '38': 34, '39': 35, '4': 36, '40': 37, '41': 38, '42': 39, '43': 40, '44': 41, '45': 42, '46': 43, '47': 44, '48': 45, '49': 46, '5': 47, '50': 48, '51': 49, '52': 50, '53': 51, '54': 52, '55': 53, '56': 54, '57': 55, '58': 56, '59': 57, '6': 58, '60': 59, '61': 60, '62': 61, '63': 62, '64': 63, '65': 64, '66': 65, '67': 66, '68': 67, '69': 68, '7': 69, '70': 70, '71': 71, '72': 72, '73': 73, '74': 74, '75': 75, '76': 76, '77': 77, '78': 78, '79': 79, '8': 80, '80': 81, '81': 82, '82': 83, '83': 84, '84': 85, '85': 86, '86': 87, '87': 88, '88': 89, '89': 90, '9': 91, '90': 92, '91': 93, '92': 94, '93': 95, '94': 96, '95': 97, '96': 98, '97': 99, '98': 100, '99': 101}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nE-b9jVqyTnJ",
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      "cell_type": "markdown",
      "source": [
        "### Label mapping\n",
        "\n",
        "You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."
      ]
    },
    {
      "metadata": {
        "id": "MiOO7pmayTnJ",
        "colab_type": "code",
        "outputId": "64fad67d-359c-4e1d-82c2-bef076117044",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('cat_to_name.json', 'r') as f:\n",
        "    cat_to_name = json.load(f)\n",
        "    print(cat_to_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'21': 'fire lily', '3': 'canterbury bells', '45': 'bolero deep blue', '1': 'pink primrose', '34': 'mexican aster', '27': 'prince of wales feathers', '7': 'moon orchid', '16': 'globe-flower', '25': 'grape hyacinth', '26': 'corn poppy', '79': 'toad lily', '39': 'siam tulip', '24': 'red ginger', '67': 'spring crocus', '35': 'alpine sea holly', '32': 'garden phlox', '10': 'globe thistle', '6': 'tiger lily', '93': 'ball moss', '33': 'love in the mist', '9': 'monkshood', '102': 'blackberry lily', '14': 'spear thistle', '19': 'balloon flower', '100': 'blanket flower', '13': 'king protea', '49': 'oxeye daisy', '15': 'yellow iris', '61': 'cautleya spicata', '31': 'carnation', '64': 'silverbush', '68': 'bearded iris', '63': 'black-eyed susan', '69': 'windflower', '62': 'japanese anemone', '20': 'giant white arum lily', '38': 'great masterwort', '4': 'sweet pea', '86': 'tree mallow', '101': 'trumpet creeper', '42': 'daffodil', '22': 'pincushion flower', '2': 'hard-leaved pocket orchid', '54': 'sunflower', '66': 'osteospermum', '70': 'tree poppy', '85': 'desert-rose', '99': 'bromelia', '87': 'magnolia', '5': 'english marigold', '92': 'bee balm', '28': 'stemless gentian', '97': 'mallow', '57': 'gaura', '40': 'lenten rose', '47': 'marigold', '59': 'orange dahlia', '48': 'buttercup', '55': 'pelargonium', '36': 'ruby-lipped cattleya', '91': 'hippeastrum', '29': 'artichoke', '71': 'gazania', '90': 'canna lily', '18': 'peruvian lily', '98': 'mexican petunia', '8': 'bird of paradise', '30': 'sweet william', '17': 'purple coneflower', '52': 'wild pansy', '84': 'columbine', '12': \"colt's foot\", '11': 'snapdragon', '96': 'camellia', '23': 'fritillary', '50': 'common dandelion', '44': 'poinsettia', '53': 'primula', '72': 'azalea', '65': 'californian poppy', '80': 'anthurium', '76': 'morning glory', '37': 'cape flower', '56': 'bishop of llandaff', '60': 'pink-yellow dahlia', '82': 'clematis', '58': 'geranium', '75': 'thorn apple', '41': 'barbeton daisy', '95': 'bougainvillea', '43': 'sword lily', '83': 'hibiscus', '78': 'lotus lotus', '88': 'cyclamen', '94': 'foxglove', '81': 'frangipani', '74': 'rose', '89': 'watercress', '73': 'water lily', '46': 'wallflower', '77': 'passion flower', '51': 'petunia'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "# Building and training the classifier\n",
        "\n",
        "Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from `torchvision.models` to get the image features. Build and train a new feed-forward classifier using those features.\n",
        "\n",
        "We're going to leave this part up to you. If you want to talk through it with someone, chat with your fellow students! You can also ask questions on the forums or join the instructors in office hours.\n",
        "\n",
        "Refer to [the rubric](https://review.udacity.com/#!/rubrics/1663/view) for guidance on successfully completing this section. Things you'll need to do:\n",
        "\n",
        "* Load a [pre-trained network](http://pytorch.org/docs/master/torchvision/models.html) (If you need a starting point, the VGG networks work great and are straightforward to use)\n",
        "* Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout\n",
        "* Train the classifier layers using backpropagation using the pre-trained network to get the features\n",
        "* Track the loss and accuracy on the validation set to determine the best hyperparameters\n",
        "\n",
        "We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!\n",
        "\n",
        "When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project."
      ]
    },
    {
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        "id": "YeWVkD97yTnP",
        "colab_type": "code",
        "outputId": "ee9ba91e-486a-4876-c08f-3f60dbd785bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Use GPU if it's available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ilX2RlTzyTnQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Define our new classifier\n",
        "class Classifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.fc1 = nn.Linear(2048, 1024)\n",
        "        self.fc2 = nn.Linear(1024, 102)\n",
        "        \n",
        "        # Dropout module with 0.1 drop probability\n",
        "        self.dropout = nn.Dropout(p=0.1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Now with dropout\n",
        "        x = self.dropout(F.relu(self.fc1(x)))\n",
        "\n",
        "        # output so no dropout here\n",
        "        x = F.log_softmax(self.fc2(x), dim=1)\n",
        "\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "id": "HFMcchA7yTnU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# TODO: Build and train your network\n",
        "\n",
        "# Load Pretrained Network\n",
        "model = models.resnet152(pretrained=True)\n",
        "# Print model's state_dict\n",
        "# print(\"Model's state_dict:\")\n",
        "# for param_tensor in model.state_dict():\n",
        "#     print(param_tensor, \"\\t\", model.state_dict()[param_tensor].size())\n",
        "\n",
        "# Freeze all the network except the final layer.\n",
        "# Set requires_grad == False to freeze the parameters so that the gradients are not computed in backward().\n",
        "for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "model.fc = Classifier()\n",
        "\n",
        "# print(\"\\nClassifier's state_dict:\")\n",
        "# for param_tensor in model.fc.state_dict():\n",
        "#     print(param_tensor, \"\\t\", model.fc.state_dict()[param_tensor].size())\n",
        "\n",
        "criterion = nn.NLLLoss()\n",
        "\n",
        "# Only train the classifier parameters. Feature parameters are frozen\n",
        "optimizer = optim.Adam(model.fc.parameters(), lr=0.001)\n",
        "\n",
        "# Print optimizer's state_dict\n",
        "# print(\"\\nOptimizer's state_dict:\")\n",
        "# for var_name in optimizer.state_dict():\n",
        "#     print(var_name, \"\\t\", optimizer.state_dict()[var_name])\n",
        "\n",
        "model.to(device);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s7SpF6VvyTnX",
        "colab_type": "code",
        "outputId": "b08c7367-07c9-4c9f-a7b2-86221ee5dce7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3655
        }
      },
      "cell_type": "code",
      "source": [
        "num_epochs = 10\n",
        "steps = 0\n",
        "\n",
        "train_losses, test_losses, test_accuracies = [], [], []\n",
        "\n",
        "running_loss = 0\n",
        "print_every = 20\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    epoch_start_time = time.time()\n",
        "    for inputs, labels in trainloader:\n",
        "        steps += 1\n",
        "        # train_start_time = time.time()\n",
        "        \n",
        "        # Move input and label tensors to the default device\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        \n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        outputs = model.forward(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        # print(f\"Training time per batch: {(time.time() - train_start_time):.3f} seconds\")\n",
        "\n",
        "        running_loss += loss.item()\n",
        "        \n",
        "        if steps % print_every == 0:\n",
        "            test_loss = 0\n",
        "            accuracy = 0\n",
        "            valid_start_time = time.time()\n",
        "            model.eval()\n",
        "            with torch.no_grad():\n",
        "                for inputs, labels in validloader:\n",
        "                    inputs, labels = inputs.to(device), labels.to(device)\n",
        "                    logps = model.forward(inputs)\n",
        "                    batch_loss = criterion(logps, labels)\n",
        "                    \n",
        "                    test_loss += batch_loss.item()\n",
        "                    \n",
        "                    # Calculate accuracy\n",
        "                    ps = torch.exp(logps)\n",
        "                    top_p, top_class = ps.topk(1, dim=1)\n",
        "                    equals = top_class == labels.view(*top_class.shape)\n",
        "                    accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
        "            \n",
        "            print(f\"Validate time: {(time.time() - valid_start_time):.3f} seconds\")\n",
        "            train_losses.append(running_loss/print_every)\n",
        "            test_losses.append(test_loss/len(validloader))\n",
        "            test_accuracies.append(accuracy/len(validloader))\n",
        "            \n",
        "            print(f\"Epoch {epoch+1}/{num_epochs}.. \"\n",
        "                  f\"Step: {steps}.. \"\n",
        "                  f\"Train loss: {running_loss/print_every:.3f}.. \"\n",
        "                  f\"Test loss: {test_loss/len(validloader):.3f}.. \"\n",
        "                  f\"Test accuracy: {accuracy/len(validloader):.3f}\")\n",
        "            running_loss = 0\n",
        "            model.train()\n",
        "          \n",
        "#         if steps == 1:\n",
        "#             break\n",
        "  \n",
        "    print(f\"Time per epoch: {(time.time() - epoch_start_time):.3f} seconds\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validate time: 30.318 seconds\n",
            "Epoch 1/10.. Step: 20.. Train loss: 4.549.. Test loss: 4.207.. Test accuracy: 0.069\n",
            "Validate time: 30.556 seconds\n",
            "Epoch 1/10.. Step: 40.. Train loss: 4.102.. Test loss: 3.717.. Test accuracy: 0.219\n",
            "Validate time: 30.722 seconds\n",
            "Epoch 1/10.. Step: 60.. Train loss: 3.632.. Test loss: 3.070.. Test accuracy: 0.352\n",
            "Validate time: 30.672 seconds\n",
            "Epoch 1/10.. Step: 80.. Train loss: 3.064.. Test loss: 2.472.. Test accuracy: 0.482\n",
            "Validate time: 30.648 seconds\n",
            "Epoch 1/10.. Step: 100.. Train loss: 2.614.. Test loss: 2.145.. Test accuracy: 0.521\n",
            "Validate time: 30.655 seconds\n",
            "Epoch 1/10.. Step: 120.. Train loss: 2.384.. Test loss: 1.701.. Test accuracy: 0.623\n",
            "Validate time: 30.642 seconds\n",
            "Epoch 1/10.. Step: 140.. Train loss: 1.894.. Test loss: 1.425.. Test accuracy: 0.656\n",
            "Validate time: 30.478 seconds\n",
            "Epoch 1/10.. Step: 160.. Train loss: 1.827.. Test loss: 1.307.. Test accuracy: 0.663\n",
            "Validate time: 30.721 seconds\n",
            "Epoch 1/10.. Step: 180.. Train loss: 1.632.. Test loss: 1.085.. Test accuracy: 0.751\n",
            "Validate time: 30.613 seconds\n",
            "Epoch 1/10.. Step: 200.. Train loss: 1.495.. Test loss: 1.035.. Test accuracy: 0.743\n",
            "Time per epoch: 551.773 seconds\n",
            "Validate time: 30.690 seconds\n",
            "Epoch 2/10.. Step: 220.. Train loss: 1.348.. Test loss: 0.976.. Test accuracy: 0.743\n",
            "Validate time: 30.688 seconds\n",
            "Epoch 2/10.. Step: 240.. Train loss: 1.216.. Test loss: 0.870.. Test accuracy: 0.779\n",
            "Validate time: 30.886 seconds\n",
            "Epoch 2/10.. Step: 260.. Train loss: 1.249.. Test loss: 0.801.. Test accuracy: 0.790\n",
            "Validate time: 30.884 seconds\n",
            "Epoch 2/10.. Step: 280.. Train loss: 1.227.. Test loss: 0.715.. Test accuracy: 0.815\n",
            "Validate time: 30.777 seconds\n",
            "Epoch 2/10.. Step: 300.. Train loss: 1.107.. Test loss: 0.669.. Test accuracy: 0.844\n",
            "Validate time: 30.719 seconds\n",
            "Epoch 2/10.. Step: 320.. Train loss: 1.036.. Test loss: 0.663.. Test accuracy: 0.824\n",
            "Validate time: 30.794 seconds\n",
            "Epoch 2/10.. Step: 340.. Train loss: 0.980.. Test loss: 0.663.. Test accuracy: 0.822\n",
            "Validate time: 30.742 seconds\n",
            "Epoch 2/10.. Step: 360.. Train loss: 0.989.. Test loss: 0.580.. Test accuracy: 0.857\n",
            "Validate time: 30.731 seconds\n",
            "Epoch 2/10.. Step: 380.. Train loss: 1.055.. Test loss: 0.563.. Test accuracy: 0.859\n",
            "Validate time: 30.792 seconds\n",
            "Epoch 2/10.. Step: 400.. Train loss: 1.033.. Test loss: 0.543.. Test accuracy: 0.854\n",
            "Time per epoch: 554.741 seconds\n",
            "Validate time: 30.746 seconds\n",
            "Epoch 3/10.. Step: 420.. Train loss: 0.864.. Test loss: 0.529.. Test accuracy: 0.865\n",
            "Validate time: 30.819 seconds\n",
            "Epoch 3/10.. Step: 440.. Train loss: 0.761.. Test loss: 0.553.. Test accuracy: 0.860\n",
            "Validate time: 30.775 seconds\n",
            "Epoch 3/10.. Step: 460.. Train loss: 0.830.. Test loss: 0.481.. Test accuracy: 0.879\n",
            "Validate time: 30.826 seconds\n",
            "Epoch 3/10.. Step: 480.. Train loss: 0.811.. Test loss: 0.451.. Test accuracy: 0.886\n",
            "Validate time: 30.858 seconds\n",
            "Epoch 3/10.. Step: 500.. Train loss: 0.814.. Test loss: 0.446.. Test accuracy: 0.885\n",
            "Validate time: 30.864 seconds\n",
            "Epoch 3/10.. Step: 520.. Train loss: 0.769.. Test loss: 0.431.. Test accuracy: 0.886\n",
            "Validate time: 30.796 seconds\n",
            "Epoch 3/10.. Step: 540.. Train loss: 0.747.. Test loss: 0.437.. Test accuracy: 0.885\n",
            "Validate time: 30.799 seconds\n",
            "Epoch 3/10.. Step: 560.. Train loss: 0.815.. Test loss: 0.502.. Test accuracy: 0.854\n",
            "Validate time: 30.774 seconds\n",
            "Epoch 3/10.. Step: 580.. Train loss: 0.857.. Test loss: 0.418.. Test accuracy: 0.894\n",
            "Validate time: 30.753 seconds\n",
            "Epoch 3/10.. Step: 600.. Train loss: 0.807.. Test loss: 0.412.. Test accuracy: 0.894\n",
            "Time per epoch: 554.790 seconds\n",
            "Validate time: 30.775 seconds\n",
            "Epoch 4/10.. Step: 620.. Train loss: 0.725.. Test loss: 0.357.. Test accuracy: 0.911\n",
            "Validate time: 30.655 seconds\n",
            "Epoch 4/10.. Step: 640.. Train loss: 0.697.. Test loss: 0.371.. Test accuracy: 0.907\n",
            "Validate time: 30.597 seconds\n",
            "Epoch 4/10.. Step: 660.. Train loss: 0.732.. Test loss: 0.369.. Test accuracy: 0.901\n",
            "Validate time: 30.665 seconds\n",
            "Epoch 4/10.. Step: 680.. Train loss: 0.712.. Test loss: 0.360.. Test accuracy: 0.902\n",
            "Validate time: 30.846 seconds\n",
            "Epoch 4/10.. Step: 700.. Train loss: 0.653.. Test loss: 0.366.. Test accuracy: 0.914\n",
            "Validate time: 30.793 seconds\n",
            "Epoch 4/10.. Step: 720.. Train loss: 0.781.. Test loss: 0.386.. Test accuracy: 0.904\n",
            "Validate time: 30.826 seconds\n",
            "Epoch 4/10.. Step: 740.. Train loss: 0.753.. Test loss: 0.404.. Test accuracy: 0.894\n",
            "Validate time: 30.819 seconds\n",
            "Epoch 4/10.. Step: 760.. Train loss: 0.634.. Test loss: 0.407.. Test accuracy: 0.893\n",
            "Validate time: 30.713 seconds\n",
            "Epoch 4/10.. Step: 780.. Train loss: 0.701.. Test loss: 0.369.. Test accuracy: 0.901\n",
            "Validate time: 30.786 seconds\n",
            "Epoch 4/10.. Step: 800.. Train loss: 0.723.. Test loss: 0.380.. Test accuracy: 0.898\n",
            "Validate time: 30.785 seconds\n",
            "Epoch 4/10.. Step: 820.. Train loss: 0.776.. Test loss: 0.352.. Test accuracy: 0.905\n",
            "Time per epoch: 584.915 seconds\n",
            "Validate time: 30.755 seconds\n",
            "Epoch 5/10.. Step: 840.. Train loss: 0.705.. Test loss: 0.365.. Test accuracy: 0.908\n",
            "Validate time: 30.802 seconds\n",
            "Epoch 5/10.. Step: 860.. Train loss: 0.512.. Test loss: 0.325.. Test accuracy: 0.912\n",
            "Validate time: 30.732 seconds\n",
            "Epoch 5/10.. Step: 880.. Train loss: 0.619.. Test loss: 0.363.. Test accuracy: 0.908\n",
            "Validate time: 30.729 seconds\n",
            "Epoch 5/10.. Step: 900.. Train loss: 0.617.. Test loss: 0.349.. Test accuracy: 0.907\n",
            "Validate time: 30.796 seconds\n",
            "Epoch 5/10.. Step: 920.. Train loss: 0.680.. Test loss: 0.338.. Test accuracy: 0.914\n",
            "Validate time: 30.784 seconds\n",
            "Epoch 5/10.. Step: 940.. Train loss: 0.638.. Test loss: 0.302.. Test accuracy: 0.921\n",
            "Validate time: 30.731 seconds\n",
            "Epoch 5/10.. Step: 960.. Train loss: 0.663.. Test loss: 0.317.. Test accuracy: 0.916\n",
            "Validate time: 30.727 seconds\n",
            "Epoch 5/10.. Step: 980.. Train loss: 0.650.. Test loss: 0.311.. Test accuracy: 0.920\n",
            "Validate time: 30.765 seconds\n",
            "Epoch 5/10.. Step: 1000.. Train loss: 0.622.. Test loss: 0.291.. Test accuracy: 0.931\n",
            "Validate time: 30.819 seconds\n",
            "Epoch 5/10.. Step: 1020.. Train loss: 0.733.. Test loss: 0.312.. Test accuracy: 0.928\n",
            "Time per epoch: 554.619 seconds\n",
            "Validate time: 30.835 seconds\n",
            "Epoch 6/10.. Step: 1040.. Train loss: 0.583.. Test loss: 0.314.. Test accuracy: 0.913\n",
            "Validate time: 30.768 seconds\n",
            "Epoch 6/10.. Step: 1060.. Train loss: 0.564.. Test loss: 0.339.. Test accuracy: 0.915\n",
            "Validate time: 30.762 seconds\n",
            "Epoch 6/10.. Step: 1080.. Train loss: 0.580.. Test loss: 0.295.. Test accuracy: 0.933\n",
            "Validate time: 30.658 seconds\n",
            "Epoch 6/10.. Step: 1100.. Train loss: 0.534.. Test loss: 0.305.. Test accuracy: 0.921\n",
            "Validate time: 30.830 seconds\n",
            "Epoch 6/10.. Step: 1120.. Train loss: 0.562.. Test loss: 0.281.. Test accuracy: 0.932\n",
            "Validate time: 30.729 seconds\n",
            "Epoch 6/10.. Step: 1140.. Train loss: 0.567.. Test loss: 0.293.. Test accuracy: 0.928\n",
            "Validate time: 30.766 seconds\n",
            "Epoch 6/10.. Step: 1160.. Train loss: 0.490.. Test loss: 0.275.. Test accuracy: 0.937\n",
            "Validate time: 30.813 seconds\n",
            "Epoch 6/10.. Step: 1180.. Train loss: 0.608.. Test loss: 0.294.. Test accuracy: 0.926\n",
            "Validate time: 30.844 seconds\n",
            "Epoch 6/10.. Step: 1200.. Train loss: 0.610.. Test loss: 0.278.. Test accuracy: 0.931\n",
            "Validate time: 30.870 seconds\n",
            "Epoch 6/10.. Step: 1220.. Train loss: 0.563.. Test loss: 0.317.. Test accuracy: 0.910\n",
            "Time per epoch: 554.856 seconds\n",
            "Validate time: 30.832 seconds\n",
            "Epoch 7/10.. Step: 1240.. Train loss: 0.667.. Test loss: 0.324.. Test accuracy: 0.913\n",
            "Validate time: 30.844 seconds\n",
            "Epoch 7/10.. Step: 1260.. Train loss: 0.608.. Test loss: 0.315.. Test accuracy: 0.913\n",
            "Validate time: 30.794 seconds\n",
            "Epoch 7/10.. Step: 1280.. Train loss: 0.534.. Test loss: 0.298.. Test accuracy: 0.924\n",
            "Validate time: 30.724 seconds\n",
            "Epoch 7/10.. Step: 1300.. Train loss: 0.493.. Test loss: 0.303.. Test accuracy: 0.915\n",
            "Validate time: 30.674 seconds\n",
            "Epoch 7/10.. Step: 1320.. Train loss: 0.595.. Test loss: 0.334.. Test accuracy: 0.916\n",
            "Validate time: 30.798 seconds\n",
            "Epoch 7/10.. Step: 1340.. Train loss: 0.549.. Test loss: 0.290.. Test accuracy: 0.919\n",
            "Validate time: 30.820 seconds\n",
            "Epoch 7/10.. Step: 1360.. Train loss: 0.498.. Test loss: 0.262.. Test accuracy: 0.925\n",
            "Validate time: 30.760 seconds\n",
            "Epoch 7/10.. Step: 1380.. Train loss: 0.501.. Test loss: 0.307.. Test accuracy: 0.914\n",
            "Validate time: 30.737 seconds\n",
            "Epoch 7/10.. Step: 1400.. Train loss: 0.685.. Test loss: 0.319.. Test accuracy: 0.914\n",
            "Validate time: 30.874 seconds\n",
            "Epoch 7/10.. Step: 1420.. Train loss: 0.613.. Test loss: 0.326.. Test accuracy: 0.905\n",
            "Time per epoch: 554.980 seconds\n",
            "Validate time: 30.772 seconds\n",
            "Epoch 8/10.. Step: 1440.. Train loss: 0.707.. Test loss: 0.268.. Test accuracy: 0.931\n",
            "Validate time: 30.867 seconds\n",
            "Epoch 8/10.. Step: 1460.. Train loss: 0.496.. Test loss: 0.288.. Test accuracy: 0.921\n",
            "Validate time: 30.397 seconds\n",
            "Epoch 8/10.. Step: 1480.. Train loss: 0.565.. Test loss: 0.268.. Test accuracy: 0.924\n",
            "Validate time: 30.031 seconds\n",
            "Epoch 8/10.. Step: 1500.. Train loss: 0.544.. Test loss: 0.288.. Test accuracy: 0.925\n",
            "Validate time: 29.951 seconds\n",
            "Epoch 8/10.. Step: 1520.. Train loss: 0.479.. Test loss: 0.289.. Test accuracy: 0.924\n",
            "Validate time: 30.283 seconds\n",
            "Epoch 8/10.. Step: 1540.. Train loss: 0.527.. Test loss: 0.265.. Test accuracy: 0.931\n",
            "Validate time: 29.964 seconds\n",
            "Epoch 8/10.. Step: 1560.. Train loss: 0.520.. Test loss: 0.305.. Test accuracy: 0.919\n",
            "Validate time: 29.963 seconds\n",
            "Epoch 8/10.. Step: 1580.. Train loss: 0.463.. Test loss: 0.292.. Test accuracy: 0.925\n",
            "Validate time: 30.161 seconds\n",
            "Epoch 8/10.. Step: 1600.. Train loss: 0.560.. Test loss: 0.330.. Test accuracy: 0.913\n",
            "Validate time: 29.870 seconds\n",
            "Epoch 8/10.. Step: 1620.. Train loss: 0.518.. Test loss: 0.269.. Test accuracy: 0.926\n",
            "Validate time: 29.801 seconds\n",
            "Epoch 8/10.. Step: 1640.. Train loss: 0.599.. Test loss: 0.285.. Test accuracy: 0.920\n",
            "Time per epoch: 574.017 seconds\n",
            "Validate time: 29.938 seconds\n",
            "Epoch 9/10.. Step: 1660.. Train loss: 0.498.. Test loss: 0.276.. Test accuracy: 0.916\n",
            "Validate time: 29.785 seconds\n",
            "Epoch 9/10.. Step: 1680.. Train loss: 0.480.. Test loss: 0.300.. Test accuracy: 0.922\n",
            "Validate time: 29.891 seconds\n",
            "Epoch 9/10.. Step: 1700.. Train loss: 0.483.. Test loss: 0.304.. Test accuracy: 0.923\n",
            "Validate time: 29.944 seconds\n",
            "Epoch 9/10.. Step: 1720.. Train loss: 0.473.. Test loss: 0.247.. Test accuracy: 0.943\n",
            "Validate time: 29.835 seconds\n",
            "Epoch 9/10.. Step: 1740.. Train loss: 0.505.. Test loss: 0.255.. Test accuracy: 0.932\n",
            "Validate time: 29.915 seconds\n",
            "Epoch 9/10.. Step: 1760.. Train loss: 0.466.. Test loss: 0.281.. Test accuracy: 0.929\n",
            "Validate time: 29.841 seconds\n",
            "Epoch 9/10.. Step: 1780.. Train loss: 0.458.. Test loss: 0.272.. Test accuracy: 0.939\n",
            "Validate time: 29.901 seconds\n",
            "Epoch 9/10.. Step: 1800.. Train loss: 0.505.. Test loss: 0.280.. Test accuracy: 0.922\n",
            "Validate time: 30.101 seconds\n",
            "Epoch 9/10.. Step: 1820.. Train loss: 0.525.. Test loss: 0.281.. Test accuracy: 0.927\n",
            "Validate time: 29.738 seconds\n",
            "Epoch 9/10.. Step: 1840.. Train loss: 0.547.. Test loss: 0.228.. Test accuracy: 0.937\n",
            "Time per epoch: 539.678 seconds\n",
            "Validate time: 29.771 seconds\n",
            "Epoch 10/10.. Step: 1860.. Train loss: 0.488.. Test loss: 0.285.. Test accuracy: 0.921\n",
            "Validate time: 30.099 seconds\n",
            "Epoch 10/10.. Step: 1880.. Train loss: 0.527.. Test loss: 0.293.. Test accuracy: 0.917\n",
            "Validate time: 29.895 seconds\n",
            "Epoch 10/10.. Step: 1900.. Train loss: 0.502.. Test loss: 0.272.. Test accuracy: 0.932\n",
            "Validate time: 29.917 seconds\n",
            "Epoch 10/10.. Step: 1920.. Train loss: 0.487.. Test loss: 0.273.. Test accuracy: 0.931\n",
            "Validate time: 30.090 seconds\n",
            "Epoch 10/10.. Step: 1940.. Train loss: 0.476.. Test loss: 0.290.. Test accuracy: 0.922\n",
            "Validate time: 29.822 seconds\n",
            "Epoch 10/10.. Step: 1960.. Train loss: 0.486.. Test loss: 0.289.. Test accuracy: 0.919\n",
            "Validate time: 29.993 seconds\n",
            "Epoch 10/10.. Step: 1980.. Train loss: 0.468.. Test loss: 0.302.. Test accuracy: 0.921\n",
            "Validate time: 30.387 seconds\n",
            "Epoch 10/10.. Step: 2000.. Train loss: 0.512.. Test loss: 0.299.. Test accuracy: 0.915\n",
            "Validate time: 29.858 seconds\n",
            "Epoch 10/10.. Step: 2020.. Train loss: 0.563.. Test loss: 0.313.. Test accuracy: 0.925\n",
            "Validate time: 30.003 seconds\n",
            "Epoch 10/10.. Step: 2040.. Train loss: 0.506.. Test loss: 0.268.. Test accuracy: 0.938\n",
            "Time per epoch: 540.279 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wYGk3CDuMh74",
        "colab_type": "code",
        "outputId": "6bfae3b0-1dab-4a7d-e30d-aa1455e99cab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(train_losses, label='Training loss')\n",
        "plt.plot(test_losses, label='Validation loss')\n",
        "plt.legend(frameon=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff168d4d048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A+rxwr0z/WNJUSbeE+boR59vRo9ndrJqmA8rzKfWgzAMAAABhC9M2m22gpD9J\n+q/dbu/57WhCdLj2eOU1jXK63JGeFgAAAKJIOPtMPyypWdLNYbym0Tol0nx3QZSkprh6DR/UT/p4\nhyTJ45HcFovyaI/Xq/XUny9EBs839vGMYxvPN/bFwjMOy8q0zWa7UNLZkm612+2l4bhmT/Pt5iFJ\n+w+Uqn9uit+xfWV1kZwSAAAAokzIK9M2my1T0j8lfSDpqZBnFKAnmnnn5aUpwRqv9Pg01TS13H9n\n+T5NGOR/3qbt5RqSm9zOFRDtorFZPMKH5xv7eMaxjecb+6LxGQe7Sh6Olem/ScqWdK3dbo/u7RS7\nKDcpx/i6rKFCmanxird6f2Vs3AIAANC3hbQybbPZTpJ0laQHJB04+BKir+SDx+rsdntlKPfqCblJ\n2dpavV1SywuIJpNJ+VlJ2l3aUt5BmAYAAOjbQl2ZniHJJOmnknYF/CdJcw5+fV+I9+kRvivTNU21\nanI1+W0rTq9pAACAvi3UmukXJK3u4HvzJS2TdL+84bpXyU3M9hsHbiteVt0ol9sti7lbdmUHAABA\nlAspTNvt9k2SNrX3PZvNJkm77Xb7glDu0ZN8V6alllKP/KxMY+xye1Re41B+ZlLgRwEAANAHsKR6\nCG3CdGOFCgKCM6UeAAAAfVc4N23xY7fbTd117UhJj09VvDlOTe5mSS0r0+ML/FvhlVQ2SEN7YnYA\nAADoaaxMH4LJZGrTHi8rPUFWC+3xAAAAQJg+rJwk70uIZQ0VMptMystMNI4RpgEAAPouwvRh5PqE\n6fLGCrk9bhX4tMcrpmYaAACgzyJMH4ZvmYfT7VS1o8avPV5pVYPc7pja+BEAAACdRJg+jLa9psv9\nwrTT5VFlrSPS0wIAAEAUIEwfRtte0xVt+kqXVFE3DQAA0BcRpg8jJzFLJnm7/JU1+u+CKNFrGgAA\noK8iTB9GnCVOGQnpxrisoVw5GYmymL0Bm44eAAAAfRNhuhPyAnpNW8xm5WTQHg8AAKCvI0x3gn+v\n6XJJ8iv1KCZMAwAA9EmE6U7ITfSuTB9orlODs1EFmd5e0yVV9fJ4aI8HAADQ1xCmOyEvyb89XnmD\n/0uITc1uVdc1RXpaAAAA6GGE6U7IadMer7ydjh6UegAAAPQ1hOlOyAsM0+20x2NbcQAAgL6HMN0J\nKXHJSrQkGOPShnLlZiTJ5O2Ox8o0AABAH0SY7gSTyeTX0aO8oUJxVrNy0mmPBwAA0JcRpjvJv9d0\n2/Z4hGkAAIC+hzDdSVkJmcbX1Y4aeTwe5WfRHg8AAKAvI0x3ku+W4k3uZjW6GpWf6V2ZbnC4VNvQ\n3BNTAwAAQA8hTHeSb5iWWlbMfs3ZAAAgAElEQVSnC2iPBwAA0KcRpjspI94/TFc5atrpNU17PAAA\ngL6EMN1J7a1M52WyMg0AANCXEaY7qU2YbqpRfJxFWWne/tOEaQAAgL6FMN1JiZYExVvijXG1o0aS\n/F5CLKkiTAMAAPQlhOlOMplMyvSpmzbCNL2mAQAA+izCdBf4lnpUN7WEad9dEA80NKup2RXxeQEA\nAKBnEKa7wC9MH1yZzvYJ05JUUeuI6JwAAADQcwjTXRAYpj0ej3LSE/zOqahpjPS0AAAA0EMI013g\nWzPt9LhU56xXdob/ynQ5YRoAAKDPIEx3QXu9prPTAlemKfMAAADoKwjTXZCRkOE3rnLUKM5qUXpy\nnHGMMg8AAIC+gzDdBYFbirf3EiJhGgAAoO8gTHdBRkKa37i9MF1OmQcAAECfQZjugnhLvJKs3k1a\nWntNZ/t09KiobZTH44n43AAAABB5hOkuaq/XtO/GLU3NbtU1OiM+LwAAAEQeYbqL2ttSPHDjlvJq\n6qYBAAD6AsJ0F7W3pXg2G7cAAAD0SYTpLvIN0zVNtXJ73H5lHhJbigMAAPQVhOku8m2P5/a4VdtU\np/SUeFnMJuM4uyACAAD0DYTpLmqzC2JTtcwmk7J8dkKkzAMAAKBvIEx3UXtbikv+HT3YUhwAAKBv\nIEx3Uce7IHpXpinzAAAA6BsI013UmV0Qqw445HK7IzovAAAARB5huousZqtS41KMcWt7PN8yD49H\nqqptivjcAAAAEFmE6SC0twtiYK9pSj0AAABiH2E6CO2H6YBe04RpAACAmEeYDoLvluJV7XTzkNi4\nBQAAoC8gTAfBd2W6tvmAXG6XkhKsSkqwGMcp8wAAAIh9hOkgBPaarmmqleRf6lFRTZgGAACIdYTp\nIAT2mm6v1IMyDwAAgNhHmA5C2y3FD76EyJbiAAAAfQphOggdbSnuW+ZR1+hUY5MzovMCAABAZBGm\ng5AWlyqTTMa4uqOOHjWUegAAAMQywnQQLGaL0uNTjXFHG7dQ6gEAABDbCNNB8tu4pan9jVtojwcA\nABDbCNNBam8XxKy0BJ/iD6mcMg8AAICYRpgOkm97vNYwbbWYlZEabxyvZGUaAAAgphGmg+S7Ml3n\nrFezq1mSf6kHZR4AAACxjTAdpMyEDL9xdXu7IFLmAQAAENMI00HqqNd0jk9Hj4pah9weT0TnBQAA\ngMghTAcpcEtx7y6I3pVpp8ut2vrmiM4LAAAAkUOYDlJndkGU6DUNAAAQywjTQUqJS5bFZDHGRplH\nBhu3AAAA9BWE6SCZTWalx6cZ4ypHtST/Mg+JXtMAAACxjDAdgsx2Nm5JS46T1eL9tbIyDQAAELsI\n0yFob0txk8nk39GDMA0AABCzCNMhaG9LcSlw4xbKPAAAAGIVYToEvu3xGl0ONTpbVqGzWZkGAADo\nEwjTIWjTHu/gLog5PivT1XVNana6IzovAAAARAZhOgSd7TVdeYBSDwAAgFhEmA5Bm10QjTAd0Gu6\nmlIPAACAWESYDkFmmzKPgxu3BO6CWEuYBgAAiEWE6RAkWZMUZ7YaY2Nlmo1bAAAA+gTCdAhMJpNf\nqUdrmE6Itygl0Ruy6egBAAAQmwjTIfJ9CbHKp9d0jl+vacI0AABALCJMhygzIcP4utpRbXzt29Gj\nkjIPAACAmGQ9/CmHZ7PZxkr6haRpkgZIqpG0UtIf7Xb7p+G4R7TyW5luqpHH4zm4pbg3TJdWNxjH\nAQAAEDtCXpm22WxTJX0iaYakxyT96OD/p0tabrPZjg/1HtEsy2dl2ul2qq65XpKUn5VkHG9qdqvq\nQFPE5wYAAIDuFY6V6X9LMkk6wW63b289aLPZPpM0T9Jtks4Jw32iUmZipt+40lGl1PgUFWQn+R0v\nrqhXVpp//2kAAAD0biGtTNtsNrOkZyTd5BukD1p68P+DQ7lHtPOtmZakqoN10wXZyX7HiyvrIzYn\nAAAAREZIK9N2u90t6R8dfHvEwf9/Fco9ol1WQJiubGwJ07kZibKYTXK5PZKk4oqGiM8NAAAA3Sss\nLyC2stlsmZJS1fIi4r2Stkm6M5Rr5uWlhT6xbrx3tjtZJpNJHk9LaG6yNBif65eTrD2ldZKkyrqm\nHv1Z0D6eSWzj+cY+nnFs4/nGvlh4xuFujVcpaZekFyQtkTTFbrdvC/M9oorFbFFWond1ury+0vi6\nf26q8fXesgMRnRcAAAC6X1hXptXSwSNF0tGSrpM0w2azzbHb7auDvWBpaW245tZprX9L6uy90+PS\nVdFQJUnaX11mfC47Nd44Z19ZnYqLa2Q20x4vGnT1GaN34fnGPp5xbOP5xr5ofMbBrpKHdWXabre/\nb7fbF9rt9nskHS8pQ9ILB19UjFm+ddNVPhu3FPi0x3O6POyECAAAEGO6LeQe7O6xTNKRkoZ3132i\nQaZPmUelo9qon86nowcAAEBMC7U13kibzbbLZrM92cEprU2Yw11OElV82+M1u5tV52wJzf2yAsI0\nHT0AAABiSqgr099KSpQ0x2azDfX9hs1mGy7pBEmlkjaFeJ+oFtger+pge7ys9ATFWb2/4uIKVqYB\nAABiSah9pp02m+0GSc9L+tRmsz0saaukoZKul5Qk6Sd2u90V8kyjWGaC/y6IVY5qDUwbILPJpPys\nJKM93n7KPAAAAGJKyDXTdrv9JbX0lV6plgD9pKSbJK2RdLrdbn8q1HtEO9/WeFJL3XSrAp9SjxLK\nPAAAAGJKWGqZ7Xb7x5LODce1eqOM+HSZZJJHLS8eVjVWGd8ryPZ29CitbpDT5ZbVEtPNTQAAAPoM\nUl0YWMwWpcd7N2jpaGXa45FKq1idBgAAiBWE6TDxrZv27TXdL7A9HqUeAAAAMYMwHSa+ddMdbdwi\n0WsaAAAglhCmw8S313RlY5WxcUt6SrwS4y3G92iPBwAAEDsI02HiG6ab3M1qcLaUc5hMJr+66eJK\nyjwAAABiBWE6TAI3bvF7CdGno8d+VqYBAABiBmE6TDIT227c0sp3Zbqy1iFHc0zvYQMAANBnEKbD\npM3KdAe9piWphFIPAACAmECYDpOMhHS/sd/KdJv2eJR6AAAAxALCdJhYzValdWLjFon2eAAAALGC\nMB1GWb4btzR6w3RqUpxSk+KMMRu3AAAAxAbCdBj51k37lnlI/pu3sDINAAAQGwjTYZTpswtipcO7\ncYvkXzdNzTQAAEBsIEyHke/GLQ5XkxpdjcbYd2W6pr5Z9Y3OiM4NAAAA4UeYDiPfmmlJqmw8REcP\nSj0AAAB6PcJ0GGUG9JruaOMWiTANAAAQCwjTYZSVGLiluHfjlvws/41b6OgBAADQ+xGmwygjcGXa\np8wjKcGqjNR4Y8zKNAAAQO9HmA6jOLNVaXHejVvatsejowcAAEAsIUyHmX97PP8w3S/bp9d0RYNf\n6zwAAAD0PoTpMPN9CTEwTPuuTNc7nKptaI7YvAAAABB+hOkw89sFsTEgTAe0xyvhJUQAAIBejTAd\nZr69phtdjWpwtr9xiyTtp24aAACgVyNMh1lmQHu8ap9Sj/ysJJl8vkdHDwAAgN6NMB1mgRu3+O6C\nGGe1KDs90RjT0QMAAKB3I0yHWZswHfgSom9Hj0pqpgEAAHozwnSYZbXZUrzKb+z7EuK+8jo5mlwR\nmRcAAADCjzAdZnGWOKXGpRjjwI1bjiz0hm2ny6NvdlZGbG4AAAAIL8J0N/DrNR3QHm/MsBy/lxC/\n2loeoVkBAAAg3AjT3cA3TAeuTKcmxWnYgHRjvG5LOTshAgAA9FKE6W6QlejtNR34AqIkjR2eY3xd\nVt1Iv2kAAIBeijDdDXxXphucDWp0Ovy+P3ZYjt943RZKPQAAAHojwnQ3aNvRw391eki/NKUnxxlj\n6qYBAAB6J8J0NwjsNR0Yps0mk8b4rE5v2lWlxiZnROYGAACA8CFMd4OsxENv3CJJ43zqpp0uj77Z\nUdXmHAAAAEQ3wnQ3aLMy3dg2TI8qypbJp0cepR4AAAC9D2G6G8Rb4pVi9e50WOlou+qcmhSn4T4b\nuNAiDwAAoPchTHeTzMSOe0238u3qUV7TqL3ltMgDAADoTQjT3STrEBu3tBpHizwAAIBejTDdTTJ9\nNm4payiX2+Nuc87gglRlpMQb43XUTQMAAPQqhOluMiCln/G1w9Wk0vqyNueYTCa/Uo9Nu6rU4KBF\nHgAAQG9BmO4mg9MG+o131O5u9zzfrcVdbo++2VHZrfMCAABA+BCmu0lhan+ZTd5f767aPe2eN7oo\nS2afHnm0yAMAAOg9CNPdJN4Sp/4pBcZ4R037K9PJiXE6ojDdGK/bSos8AACA3oIw3Y18Sz12HdjT\n7kuIkn+pR0WNQ3vK6rp9bgAAAAgdYbob+YbpJleTSupL2z1vbGCLPEo9AAAAegXCdDcakh7wEmIH\npR6D8lOVmerTIo9+0wAAAL0CYbobDUjp16mXEE0mk8b4rE5v3lMtt5u6aQAAgGhHmO5GcZY4Ffr0\nm+6oPZ4kDSlIM752ujyqrHV069wAAAAQOsJ0NxvkUze9u7bjlxDzs5L8xqVVDd06LwAAAISOMN3N\nBvvUTTe5m7W/rqTd8/Iy/cN0CWEaAAAg6hGmu9mQgJ0Qd3ZQ6pGbkSiTz5iVaQAAgOhHmO5m/VP7\nyWKyGOOdHbyEaLWYlZ2eYIwJ0wAAANGPMN3N4sxWFaZ6X0Lc2UF7PMm/1IMwDQAAEP0I0xHg9xLi\ngb1yuV3tnucfphu7fV4AAAAIDWE6Anzrppvdzdpff/iXEA80NKu+0dntcwMAAEDwCNMRMCi90G/c\nUakH7fEAAAB6F8J0BAxI6SdrJ15CDGyPR5gGAACIboTpCLCarSpMHWCMO2qPR5gGAADoXQjTEeJb\n6tHRS4gpiVYlJViNMWEaAAAguhGmI8T3JUSn26l9dcVtzjGZTMrLTDTG7IIIAAAQ3QjTETKokzsh\n0msaAACg9yBMR8iAlAJZzd4Sjo5eQsz3CdPl1Q653O5unxsAAACCQ5iOEIvZooG+LyF20B7Pd2Xa\n7fGovMbR7XMDAABAcAjTETQ4zfsS4p66fXK6227KkkevaQAAgF6DMB1BgzvxEmKb9niVhGkAAIBo\nRZiOoMHpAS8htlPqkZ2WILPJZIxZmQYAAIhehOkI6pecrzi/lxDbhmmrxaycjARjTJgGAACIXoTp\nCGrzEmIn2uPRaxoAACB6EaYjzLff9N66Yrk9bVvf5Qf0mvZ4PBGZGwAAALqGMB1h/VPyja+dbqcq\nG6vanOO7Mt3gcKmusW3XDwAAAPQ8wnSE5Sfn+Y1L6svanNOmowelHgAAAFGJMB1h+cm5fuPihtI2\n5xCmAQAAegfCdIRlJmT4dfQo7cTKdAm9pgEAAKISYTrCzCaz8pK8q9PtlXkkJ1qVmhRnjFmZBgAA\niE6E6R7gW+pRUt+2zEOS8jITja8J0wAAANGJMN0DfF9CLG+sVLO7bbeOvID2eAAAAIg+1sOfcng2\nmy1P0h2SzpNUIKlK0gpJd9vt9jXhuEcsyfcp8/DIo/KGcvVLKfA7xzdMV9Q45HS5ZbXwdx8AAIBo\nEnI6s9ls+ZLWSLpK0ssH//8fSadKWmGz2Y4O9R6xJrA9XvFhXkL0SCqrbuzuaQEAAKCLwrEyfY+k\ngZIusNvtr7cetNlsqyS9IelXkr4XhvvEjMD2eO3VTee30x6vX3Zyt84LAAAAXROOuoG9kl6UNC/g\n+GK1LKqOC8M9YkpqXIqSrN6wzMYtAAAAvVPIK9N2u/3ODr6VJskkqSbUe8Qak8mk/ORc7ajZJUkq\naWfjlqy0BFnMJrncnpZz6DUNAAAQdcLyAmIHrj34/+dDuUheXloYphJ99x6c1d8I02WN5e3eq19O\nsvaU1kmSahqae/R3Eav4ncY2nm/s4xnHNp5v7IuFZ9wt7SFsNtsZaunu8bmkf3XHPXq7/mne7h1V\njTWqb2678lyQk2J8vb+8PiLzAgAAQOeFfWXaZrNdIelxSdslzbbb7U2hXK+0tDYc0+qS1r8ldee9\nUzz+fxPbuGu7BqcN9DuWkezdBXFfWZ1KSmpkMpm6bU59SSSeMXoOzzf28YxjG8839kXjMw52lTys\nK9M2m+23kp6R9KWkaXa7fV84rx9L2nb0aPsSom9HD0ezS7X1zd0+LwAAAHRe2MK0zWa7X9Jdkt6U\ndLLdbi8J17Vjke/GLVL77fECO3qU0NEDAAAgqoQlTB9ckb5J0lOSzrfb7RT4HkaiNVEZ8d5/Tjjc\nyrREezwAAIBoE44dEKdL+r1a+kz/yG63u0KeVR/huxNie2E6NzPRb0yYBgAAiC7heAHx3oP/f0fS\n+Tabrb1z3mK1uq385Fx9W7VVUkuvaY/H4/eCYWK8VenJcao5WCtdSq9pAACAqBKOMD3x4P8fPsQ5\nQ9XS3QM+fFemG5yNOtBcp7T4VL9z8rKSvGGalWkAAICoEo4dEOnVFqTAlxCL60vbhunMJG3Z07KJ\n5N7yejU4nEpK6M69dgAAANBZ3bJpCzrHd2Vaar9uur/Pxi0HGpr1wNyv5GiiLB0AACAaEKZ7UG5S\ntkzyLuy31x7vxHH9leyzEr1pV5UefO0rOZoJ1AAAAD2NMN2DrGarcpKyjXFJQ9uV6czUBP384glK\nSrAYxzbuqNRDr32lZieBGgAAoCcRpnuYb910eyvTkjS0f7p+9r0JSoj3BuoN2yv10Ovr1ex0d/sc\nAQAA0D7CdA/z3Va8tKFcbk/74fiIwgz9bM54xcd5H9m6reX61xvr5XQRqAEAAHoCYbqH+b6E6HQ7\nVdlY3eG5Rw3K1E8vHK94q/exrd1cpmeX2Lt1jgAAAGgfYbqH+a5MSy2btxzKiCFZuuHCcYrzCdQr\nvtqniprGbpkfAAAAOkaY7mH5SYdvjxdodFG2fnTWKL9jq74pCeu8AAAAcHiE6R6WlZghq9nb+q6j\nlxADTToqT+kp8caYMA0AABB5hOkeZjaZAzp6HH5lWpLMZpMm27yr2lv31qiM7cYBAAAiijAdBXzr\npju7Mi1Jx4ws8BuvsrM6DQAAEEmE6Sjg29GjvLFSzW5npz53xMAMZaZ6Sz0+20iYBgAAiCTCdBTw\nLfPwyKPyhvJOfc5sMmnyiHxjvGN/rYor68M+PwAAALSPMB0F8gLa4xV3sm5aalvqsZoXEQEAACKG\nMB0FCpID2+N1vm562IB0ZacnGGNKPQAAACKHMB0FUuNSlGRNNMalDZ1fmTabTJriU+qxq+SA9pXX\nhXV+AAAAaB9hOgqYTCa/zVs62x6v1ZQRAV09KPUAAACICMJ0lPBtj7e/vmtheGj/NOVmeFe2V1Hq\nAQAAEBGE6SjRL8VbqlHbdEAVjZWd/qzJZNKUkd7P7ymr057SA2GdHwAAANoiTEeJYRlFfuPNVdu6\n9PljKPUAAACIOMJ0lChKHyyLyWKMt3QxTA8uSFV+VpIxXvVNiTweT9jmBwAAgLYI01Ei3hKnIekD\njfHm6u1d+rwpoKvHvvJ67S6lqwcAAEB3IkxHkeEZQ42v99cV60Bz18Jw4AYun20sDsu8AAAA0D7C\ndBQ5InOo33hL1fYufX5gXor65yQb41UbS+Sm1AMAAKDbEKajyLCMIplkMsabq7Z26fOBpR4lVQ36\n8tuu9awGAABA5xGmo0hyXJIGpPYzxl1dmZakE8cNkMXsDeTzV27nRUQAAIBuQpiOMr6lHrsO7FGj\n09Glz+dkJGrqGG8g376/Vhu2V4RtfgAAAPAiTEcZ35cQ3R63ttfs7PI1zjxuiEzexWkt+Gh7GGYG\nAACAQITpKBP4EmJXN2+RpILsZL/OHpt2V2vTrqqQ5wYAAAB/hOkok5GQrtykHGPc1ZcQW505dYjf\neMHK7aFMCwAAAO0gTEch39Xp7TU75XQ7u3yNgXmpOvrIXGO8fluFtu2rCcv8AAAA0IIwHYWO8Kmb\nbnY7tbN2T1DXOev4Ir8xq9MAAADhRZiOQsPbbN7S9bppSRraP11jhmYb4y++LdPukgMhzQ0AAABe\nhOkolJeUo/T4NGMczEuIrQJXpxd+siPoawEAAMAfYToKmUwmv9XpLdXb5fa4g7rWUYMyddTADGP8\n2cZiFVfUhzxHAAAAEKajlu9LiA3OBu2rKw76WmedUGR87fGwOg0AABAuhOko5fsSohRaqcfoomwV\n9fOWjXy8fr8qa7u2syIAAADaIkxHqQGp/ZRkTTTGwb6EKLWUjfjWTrvcHi37fHco0wMAAIAI01HL\nbDJrWEaRMd5ctU0ejyfo6004Mlf5WUnG+P0v9qixqev9qwEAAOBFmI5ivqUe1U01Km+sCPpaZpNJ\np00ZZIzrHU59tG5/SPMDAADo6wjTUeyILP+66W9DKPWQpBPG9FdKotUYL121S2538KvdAAAAfR1h\nOooNThuoOLM3/IZSNy1JCfEWnXJ0oTEuqWrQF9+WhXRNAACAvowwHcWsZquK0gcb428rtwTdb7rV\nqZMGymI2GeO3V+0M6XoAAAB9GWE6yh2ROcz4uqyxQp8XfxnS9TJTE3TcqAJj/O3uam3dWxPSNQEA\nAPoqwnSUm9p/sqwmizH+35ZFanY1h3TN7/i8iCixOg0AABAswnSUy0nK1smDTjDGlY4qvbd7RUjX\nHFyQplFFWcZ49TelKqtuCOmaAAAAfRFhuheYNWSGUqzJxnjJ9vdU23QgpGueNsVbi+32ePTOajZx\nAQAA6CrCdC+QHJesM4bONMaNrka9te2dkK45Zli2+ud4A/qHX+5Vg4NNXAAAALqCMN1LnFh4nPKS\ncozxir2fqLiuJOjrmU0mnX6Md3W6scml5V/uDWmOAAAAfQ1hupewmq06d/h3jbHb49a8LW+FdM2p\nowuUlhxnjJeu3iWnK7TWewAAAH0JYboXGZ83RsMziozxurKvtalyS9DXi7NaNGPiQGNcXuPQx+vZ\nYhwAAKCzCNO9iMlk0vlHnuV37PXNC0LayGXGxEIlxHlb781fuZ3VaQAAgE4iTPcyRemDNblggjHe\nVbtHq4vXBn29tOR4zZjk3WK8rLpRH29gdRoAAKAzCNO90NnDZslqthrjBVuXhLQ6ffoxgxUf5/2j\nsIDVaQAAgE4hTPdCOUnZOmWgdyOX8sZK7ardE/T10pPjdapP7XRpFavTAAAAnUGY7qWO7z/Fb7yu\nbGNI1zv9WP/V6YUrd8jlZnUaAADgUAjTvVR+cp5f3+n15aGF6fTkeL/OHiVVDfp4fXFI1wQAAIh1\nhOleymQyaWzuKGO8q3aPqhzVIV1zVju10+2tTpdXN2rNplJVHXCEdD8AAIDeznr4UxCtxuSM1Lu7\nlhvjDWXf6ITCY4O+XnpKy+r04k93SmpZnf5kQ7FOGNtfklRW1aD5K7dr5fr9crk9Mkk6alCmjhlV\noEm2PKUnx4f08wAAAPQ2hOlebHhmkRItiWp0NUqS1pVvDClMSy2r0++u2a2m5pYV6fkfbddRgzK1\n6JMdWv7VPrncHuNcjyT7rirZd1Xp+bc3aeSQTE0ZWaBjRxYoId7SwR0AAABiB2UevZjVbNXInKOM\nsb3iWzW5mkO6ZnpKvGYc7V87/ct/f6z31+71C9KB3B6PNmyv1NOLvtE9z65WU7MrpHkAAAD0BoTp\nXm5szkjj6yZ3s76tCn578VanHztY8VbvH43ACD04P1Xfn2XTieP6Kzmh7T9u7Cmr0zuf7w55HgAA\nANGOMo9eblSOTSaZ5DkYedeVbdTonBEhXTMjJV7TJxZqyWe7/I4PzEvROdOG6eijcmU2mXTyhEJd\nfrpNG7ZV6LONJfrcXqImZ0t5yMKPt+vEcf2VRh01AACIYaxM93Jp8akamjHYGK8v2yiPp+NyjM46\nc2qRCrKTJUmFuSm67twxuvOHx2iSLU9mk8k4z2oxa/wRubp69ihdcPJw43iDw6X5K7eHPA8AAIBo\nxsp0DBiTM1Jbq3dIkiodVdpbt1+Fqf1DumZqUpzu+dExqqx1KCc9USafAN2R6RML9c7nu1Ra1fJC\n5Htr9mjmpIHKz0oOaS4AAADRipXpGDAmd6TfONTdEFtZzGblZiR1KkhLLavUvqvTLrdHr32wNSxz\nAQAAiEaE6RgwIKWfshIyjfH6MIXpYEwZka+h/dON8apvSrRlb2ibyQAAAEQrwnQMaNkN0bs6vb1m\np2qbDvTYXC6acYTfsVff3RyWOm4AAIBoQ5iOEb6lHh55tKH8mx6by1GDMnX0kbnGeNPuaq3dXNZj\n8wEAAOguhOkYcVTmcMWb44xxT5Z6SNKFpwz36/ox9/0tcrndPTgjAACA8CNMx4g4S5xGZHt3Q9xY\nsUlOt7PH5tM/J0UnTRhgjPeV1+vDL/f12HwAAAC6A2E6hozJ9W7W0uhyaHPVth6cjXTOCUVKiLMY\n4zeWb1V5dWMPzggAACC8CNMxJHDnw/XlPVvqkZGaoDOO9W4oU1vfrHtfXquauqYenBUAAED4EKZj\nSGZChganFRrjz4u/1N4D+3twRtLpxwxW/xzvpi3FFfX6xytrVd/YcyUoAAAA4UKYjjFjckcZX9c0\n1epvq/+pT/d93mPzSYi36ObvTVB2eoJxbGfxAT0490s1Nbt6bF4AAADhQJiOMacMPEGZCRnGuMnd\nrGc3vqznN85Vk6u5R+aUk5Gon180QWnJ3m4jm3ZX65E31svpCr3Dx+7SA3r9w61auX4fK94AACCi\nTOHaTMNms8VLukfSLZI+tNvtp4TjuqWltRHf7SMvL6313pG+dVhUO2r11Ibn9W2V/1behan99aMx\n/6f85LwemdeO/bX664tr1ODwrkgfO6pAV88e5ddGrys2bK/Qg3O/UrOzJZRbLSaNGZqjKSPzNeGI\nXCUlWNv9XG9/xjg0nm/s4xnHNp5v7IvGZ5yXlxZUGAnLyrTNZrNJ+ljSjyUFl4oQNhkJabphwtWa\nNWSG3/E9B/bpL6se1NrS9T0yryH90nTjBeMUZ/X+sfv062I9t8Qut7vrf2f6OiBIS5LT5dHazWV6\nbP7XuunBFfrna19p48iYF5oAACAASURBVI7KsMwfAAAgUMhh2mazZUlaI8kiaXLIM0JYWMwWzR4+\nS9eN/6FS4rwvADa6HHp83XP6YPfKHpmXbXCWrjt3jCxm79+5Pli7Vw+9vk6NTZ0v0djYTpAO5HS5\n9cW3Zbr3pS+0q6RntlcHAACxLRwr0/GSnpV0nN1ut4fhegij0Tkj9KspP9XQ9CHGMY88emXTG5q/\nZbHCVebTFeOPyNVVZ430+yeMtZvL9Kf/rlFFzeH7UH+zo1IPzP1KTT5BetzwHP343DGaPCJf8Vb/\nP9Yej/Temt3hmj4AAICh/YLSLrDb7cVqKe9AlMpKzNTPJl6rlze9oY/2fmocX7zjXdU01epi2/my\nmC2HuEL4HTeqnyTpyYXfGC8h7io5oLufWa0bLxynof3T2/2cfWel7p/7ZZsg/ZPzxirOataUEfly\nNLn05ZYyzftwq4orGyRJn3xdrItmHKmE+Mj+nAAAILaF7QXEVjabzSPpg3C9gCgp8kunMcrj8Wju\nhoV6dcNCv+OTBozVT6f+SAnW+IjPaeO2Cv3h6U9VfcC7kUt8nEU3XzJRJ4wfoKZml0qrGlRcUa/d\nJbV69q2NcjR5X2CcNCJfv/7BMYqPaxuSl366Qw++stYY33TR0Zp5zOA25wEAACjI9/4I033Q0s3L\n9fiaF/1KPGw5w3TbidcpNSEl4vPZX16nu574VLuK/d/ozUpLUGWto8PPTbTl6/Yr2w/SktTgcOr7\nv19sdA8ZNTRbf7n+xPBNHAAAxJLYDNO0xuseX5au15MbXpDT7X3pb3BaoX4+6SeymkOu/umy+kan\n/v3meq3fWtGp80cPzdaNF4xVnPXQZRtPL9qoD7/cZ4z/cPWx6p+T0ieecV/G8419POPYxvONfdH4\njHu0NR56n/F5Y3TDhKuVZE0yju2s3aN3dy7vkfkkJ1p104XjdOrEgYc8z2I26aTx/XXD+YcP0pJ0\n4vgBfuPlPsEaAAAgVJFfgkTUOCJzqG6e+GP9Y80janC2dNF4a/tSTSwYp9yknIjPx2I267LTjlJR\n/zSt+qZEqUlxys1IVE5GonLTE5WTmaTstARZLZ3/O+Cw/ukqzE3RnrI6SdLK9ft0/snDuutHAAAA\nfQxhuo8bkNpP5ww/Qy/Z50mSmt1OvWSfp5+Mv0qmIHclDNUJY/vrhLH9w3Itk8mkE8cP0EvLvpUk\n1dQ368vNZfr/7N13eFzlmTf+7znTu6ap9zZqlmy5YWOMwWAg9GZKIBCSvAnZd1PeTbZnN5vNb7PZ\n8tv0vAFCICH0YAIYA7axMQbjKqv33kaa3vs57x8jjzSeURuNpfH4+VyXL9CZdmZundF9nnM/95OT\nrVjkkQRBEARBEIsjZR4Ers7dihL5bJeLTnMPzk43r+EeJde22ixwObMnBsdIqQdBEARBEEmy4pFp\nnU5XA6Dmos1anU5335yf3+3u7nav9LWIS4OmaDxUdS/+/fRPwbDh/s2v976FGlUlxHNWT7xcycR8\nbKjQ4nTXNACgbdAEg8UDrVK0yCMJgiAIgiAWloyR6b0AXpvzDwgn13O3ZSbhdYhLKE+ag90FOyM/\nO/xO/Ln/wBruUXLtnDMRkWWBw2dG1nBvCIIgCIJIF8lYAfH7AL6/4j0h1twtJTfg3HQzTF4LAOD4\nxElszdmIUkXx2u5YElQXK6GWC2GaWa784KkR7N1ducZ7RRAEQRDE5Y7UTBMRAg4fD+jujtr2Utcb\nCDGheR5x+aApCtfUz05qnDa70dJnWMM9IgiCIAgiHZBkmohSq65CY2Z95OcJlx6HRj5awz1Knh31\nOVFLG31wkpR6EARBEASxMiSZJmLcV3EHhBxh5Of9gwfRZuxcwz1KDpVciLrS2f7ZJ1on4XD713CP\nEuPyBnCseQIt/SYkewVTgiAIgiCWhyTTRAyFQI47y26J/BxiQ3i67Q/oMveu4V4lx9xSj2CIwb6P\nB9dwb5aHZVmc6pzCPzz1GZ470IWfvNaMX7zRCrc3uPiDCYIgCIK4JEgyTcS1I28rNmY2RH4OMkH8\npuU59FuH1m6nkmB9hQZq+eyo+9GmcfSMWtdwj5bGZPPip6+34P/+uR12dyCyvanXiB88dxojU441\n3DuCIAiCuHKRZJqIi6ZoPFbzIOo1tZFtfiaAXzU/i2H76Bru2cpwOTQevUkXte25A10IBONPsmRZ\nFu+dHMG3f34c//zsKbx2tA/dIxYEQ8xq7C4YhsXB06P4x2dOoqXfFPc+01YP/r8/nMXHLROrsk8E\nQRAEQcyiUr3m0mBwrPoOarWyC6+92i+dcgIzI9Kd5p7INglXjG82fhV50uQs+b0Wnn+/Bx81jUV+\nvm17Me7ZWRpzv7c/GYxbCiIScFBTpMK6MjUUEj78QQb+QAj+QAi+AIMQw6A8T4HKgoyEl2XXm914\n+u0ODE7ao7ZTFHDt+jw09xlhcfiibrumPgefv7ESfB4noddMF+QYTn8kxumNxDf9pWKMtVpZQn+w\nSTIdRyoGeC35Q378svm36LPOJpUyvhTfbnwSWWLtGu5Z4vgiPp788YeRCYgcmsI/P74Z+ZnSyH0+\nODWClz/sW9HrbNJp8ehNOsjE/GU9Tm9249//eA52V/QEyaIsGR6/pQpF2TLY3X48/VY72ocsUfcp\nzJTiW3sbkCEVrGjfL2fkGE5/JMbpjcQ3/aVijBNNpkmZB7EoPoePJ+u/iCJ5QWSbw+/Ez5uehjPg\nWsM9S5xCKsCX76yL/BxiWPzuQBcYJnzudvT8eEwinZmx/OXHz3Qb8L3fnsL5XuOSHzNt9eA/X2qK\nSqT5PBp7ryvHPz62EUXZ4S8guZiPb+9djzuuLo5q+Tcy7cTP/9Qyb+kKQRAEQRDJw/n+97+/1vuw\nILfb//3Vfk2JRHDhtVf7pVMWl+Zig3YdOszdcPidAABvyIsJlx6bstYnXMqwViQSAYpz5GjpNcBg\n9QAArE4fJEIepi0ePPduV9T979pRgr+4Zx12rMtBjkYMmqJgcfoQCi1+4cQXCOFk5xSMNg+qCpXg\ncec/hzXbvfiPF5tgts+Wb+RrpfjbzzeioVwD+qLPmaIoVBUpUZYrR+uAGf4gM/Ne/LDYfdhQoVmz\n2DAMi5MdUxjU26GWC1e19EQo5CHEMPB6A4vfmbgske/p9Ebim/5SMcYSieBfEnkcKfOIIxUvPaQK\nh9+J/zrzCxi95si2O0pvxk3F16/hXi3fhRh39E7je789CX8gnITyuTSCIRbMnOPi5q2FuH9XWUxS\nGgwxGJ12gmXDI8d8HgcCHgd8Lo0xgxO/3d+JaYsn6jFquQCP7NFhXakaNB39fFanDz/+4zlMzXlM\njlqMv/l8I+RLKBOZMrvxr8+fgds32yrvwd0V2LO5YIFHXTpvHBvAO58OAQB4XBpbq7NwXWMeSnLk\nl/R12wfNeO69LlgdPty/qwx7thRe0tcj1gb5nk5vJL7pLxVjnGiZBxmZjiMVz5ZShYDDR1lGMU5O\nngGDcMLZY+lHRUYp1CLVGu/d0l2IMRgGfC6NtsHwyUGIYTH37O26xjw8tLsi7uguTVNQygRQygSQ\nS/iQCHkQ8jngcWmo5UJcU58Lry8UNYHQ4wvhZMcUPjo/AYPNAz6XA5VcAKcngP986Tz0ZnfkvplK\nEf7m4UYoJEurfZaKeCjMluKzjqnIto4hC8ryFQmVqKyExeHDb95qj5TNMAyLkWknjjVPoLnPCJqm\nkK0Sg8tJXqUZy7J4/9Qofru/A25vECwLtA2aIZfwL3kCT6w+8j2d3kh8018qxjjRkWmSTMeRigFO\nJQqBHFK+BG2m2VKIDnMPtmQ3QsC5PCa9zY1xSY4crQMmWJ3R8b66LhtfuLkqprRiqbgcGvVlapTn\nK9A9YoHHN1vD7AuEMDTpwKdtehxtGseJNj2mzLMj0mq5EH/zcCNUc3piL0WmUgwBj4P2ofDJAQug\npc+ITVWZkAh5Cb2PRPzpo370j9vj3mZ1+nG+14hjzROozM9Y9nuMxx8I4XfvduL9U7FtG1v7TcjV\nSJCnkaz4dYjUQb6n0xuJb/pLxRgnmkyTCYhEQnbkXoVNWesjP9v9Dvyu/SUw7Or0X04mmqbw2M1V\n4Mwpu9hUlYnHP5d4Ij1XbbEKP3hiK65elx33doc7AIPVG/k5Q8rHdx9aD7UisSTzpi0F2FabFfnZ\n5Q3i539qgde/9JUSV1L+ZbR58NH52Z7X2gwhKgsyYu7ncAfwqzfb4A+sbKKk2e7Fj/54Difap+Le\nzgJ46q32yAlGOmJZliwtTxAEsUbIyHQcqXi2lGooikK1qhLNhna4Zjp6mLxmACwqleVru3NLcHGM\nFVIBCjKlsLv82FKdhc/fWJnUEgQel0ZjpRZX12VDJRfC6w/G9IgGALmYh79+uBHZqsRHUSmKwrpS\nNdoGzZHRdoc7AL3JjcZKbUyt9lweXxDvfjaMX73ZhgMnRzA67UQwxEIpE4DPXdoEwlc+7MOQfrYG\n7vGbq7D3+nJs1GkBCtCb3AjOTNz0+kMQ8Dlxk+2l6Bm14r9eboqqTaco4Et31CFLJUbfWHh1S5YF\nznUbUFuiglJ2eVw9WSpfIIRf7WvDM+90wuryo75MfdlNCE4E+Z5ObyS+6S8VY0wmICZRKhbFp6oJ\npx7/cebnCDDhrgkUKHy94QnUqHWLPHJtpUKMzXYvzvYYcLZrGr1jNmQqRfj63etQMKfX9Uqf/wfP\nn4lqsaeUCXDdhjzsXJ8bNakxEAzhyLlxvHNiGE5PbAcMmqJQka9AQ7kGjZUaZCrFcV9zyuLGPzx1\nMjKBszBTin/64uaoEX6nJ4C/f+qzyOsI+Rz8+9e2LWmSJRCeqHm6axqnO6fRN26Luk0i5OJrd9Vh\n1+YihBgWP3zmBM50GyK3S0U8/N0jjchRp0/JxxvH+vHOp8ORn7+9twHrStVruEerIxWOYeLSIfFN\nf6kYYzIBMYlS8WwpVcn4UmQIFGgxtke2NRvaIOFJUCjLS9kRslSIsUjARVmuAjvqc3Hb9iLs3liQ\n1IVWRAIuyvLkONGmx4VzZq8/hM5hCw6dGYPB6kGGTIDWARN+9WYbTncZIq31LsYCMNm9aB8y48Nz\n4+ByaZTnKWLi+9KhHoxOOyM/P3ZzVUziyudxwOdx0DoQXh49GGIRCDCoL5s/AXS4/fi0TY/XjvTh\nxYO9aB0ww3zRyH6+VoLvPtyIkmw5JBIBaIpCZa4c/eM2GG3hMhp/kMH5PiM26TIhEnCX9DmmsilL\neJVMZs6Qg8MdwPa6+CVF6SQVjuG1ZnP68If3e3CuZxqluYq0+J2+gMQ3/aVijMkExCRKxQCnsnxZ\nLixeK8ac4TrZEMugzdSJYccYKpVlEHJT77J6qsWYpqhLcuKhlguRpRSjtd+E0JyMi2Fnu2uc7zNG\nTY4EAIWUj4o8BcwOX1SbQCCcWHcMWeD2BlFbqors97jRhT+83x25X0mOHHuvi20pCACFWVKc6pyC\nyxuu4x6ZcmBLdRakothJkk29Bvz4xSac6zFEkuKLbdJp8Y376iOdTy7E1+sNoLFSi46h2ZIXjy+E\nkSkHrl6Xs+Bnl+pYlsUz73Ri0uSO2m6werBJp4VcsrxVNy83qXYMr7ZgiMF/v3IerQNmjBlcsDp9\n2FSVuda7lTRXenyvBKkYY5JMJ1EqBjjVVakqMGAbgtk7u7S1wWPEZ/oz0IjUyJFkLfDo1XclxThf\nK8XO9bkQC7jQm93w+uef8CcRcnHXjhJ85fZaXFOfiz2bC1CaK4eQz4XV5YNvzmMHJu3Qm91oKNeA\nQ1N44f1uTMxJ7L50a/W85SAX2gqe7poGEK5ptjh82FId/XtyvteIX+1rQyDOiLlUxMP2dTl44Ppy\n3Ly1ELw5Nd1z48vj0thQqcX5XmOktMRo8+Kq2vjJ+0q4vUFMGF2QiXlJmby6kPN9Rrz9yVDc2xiW\nxfpyzSV9/bWWjsewyxvAmx8PomfUiuJs2YILPL31ySBOdkxHfjZYPbhxc0FS53qspXSMLxEtFWNM\nkukkSsUApzoOzcHmrA3g0jz0WQdwoVtzgAng3HQLjB4T1EIlAAp8Dm/Nyz+utBgLeOFJfrs35iM/\nUwqb0xe1yiKfR+OWqwrx5J11qClWRf4gczk0ctQSrK/QYM/mAogEXLQPznbFGDe6MDBhg1ouxGtH\n+yPbKwsycPc1JQvGOUctRsewJbIfkyY3qouUkS4mzX1G/HJfa9SIuljAxdbaLNy/qxyP7KnEhgot\nNApRzOtcHF8Bj4OibBk+bpmM3Ecu4UOX4MTHeNoHzfi3F87i0NkxnO02oCBTCnUS2v7F4w+E8LPX\nWyIL9FAUoFEIIz9PGF3YtT4XglVcdXK1zXcM+wMhUJfoSs+l9szbHTjWPImeUSu6RyzYWp0VNznu\nn7Dh2f1dUT3xQwyLkhx52swHuNK+o69EqRjjRJPp9CmwItYch+bg5uLrUavW4bmOl6F3zbYqO6U/\nh1P6cwDCkxRlfCkUfBmkfCkoioq09mLAgmUZcCgONmWtx7bczWv1dtISl0Njc1UmNldlYljvwIl2\nPYR8Dq5rzIdikbIAmqJw05ZCyCV8PLu/M5LkdgxZ0DVsjbrvYok0EO46sve6cvzbH85Gtr16pA//\n8OhGtA6YYxLpzVWZ+PJtNQuO1i2kLFcOtVwIkz1cKnKqcwq3by9O6Lkudr7XiF+92RrpUjJhdOFH\nL5zDrg15uO/aMoiFsV+10xY3Pmufgt7ihkYhQlGWFIVZMmgUwkU/uwMnR6JKXnZtyENFvgJPvdUB\nAAgEGXx0fgK3Jen9XS7e/HgAb30yhCylCF+7sw5F2bK13qUls7n8ONszO1m2f8KOX77Zim/cWx+V\nUPv8oZk6+di5+U09BjRWaldlfwmCmEWSaSLpCmR5+NtN38BbA+/hyOhxRK8pCLBgYfc7YPcvPIO3\ny9ILDs3BluzGS7m7V6yibFlCyca22mzIRDz8cl8bfDM9ouf+Ya8tUUFXqFzSc5XnKbCpKhNnZso9\nBibsePFQLz46PxFJTIFwTfT/uqMGHDrxS9gURWFLdSYOnBwBAIwbXBgzOJGvXVn3lFOdU3j67Y6o\nxP+Co03jaOo14JEbK9FYqYXHF8Lpril82qZH75gtzrOFR98LZxLrzVWZKM2VRyXXBqsH7342271D\nKuLh7mtKIeRz8JqsP9Jy8fC5Mdy8tTBtLvsvpmPIjLdmyl6mLB78+MVz+Mt71qG6+PJYmfVUxxQu\nzo/bBsx4dn8nvnx7TaRs6JUjfVGtIOdq7jchxDArOk4Iglg+UuYRRypeerjccGgOatQ6lGeUos86\nAE8w/sSxxXSYulGrroZCkNwRJhLjlclUilFbosK5ntgOIF+9o3ZZvZyLsqQ40jQeSSQGJ+1RyfnG\nSi2+emftspLC+eIrFfGiFpSRiHioLlpa4h/PJ62TeOad6G4amRmiyMRKIDySeLprGs39Jrx+tB/n\neoxRJTYXC4QYGG1e9E/Y8XHLJJr7TeDSNLJVYnA4NJ7d34lxoyty/4dvrERFfgZomkIwxKBz2BJ5\n3Wy1OGmtFlPN3Bj7AyH85LWWqM89GGJxqnMKWSox8lZ4wrQa/niwJ2YVVgAYM7jg9gZRV6pC64AJ\nLx/ui9ymkPBx05YCdI+Grwz5gwxqipTQKESrtt+XCvmOTn+pGGNSM51EqRjgy5VapMLOvO2oVVeh\nWq1DeUYJCqR50IrVkAtkEHFFyBAooBRmQCnIgFqohJgngsMfbq/GsAw6zd3YnN0IPid53QlIjFdO\nKRNgQ6UWzX3GSK3uhgoNbt5auKznkYh4cHoCGJiIXX58Q4UGX7urbtmjq/PFVyHh42TnNFwzExGt\nTh92b8xPqL72SNM4njvQFbXtqposfOeh9SjNkaNvzBrVJcXm9IOJM3otFnARCM2/cqjN6UdTrxFH\nz09gzOCKTNoEwh1THtlTGdn/XI0Eh8+ORUbJTXYvrm3IvSzrhxczN8ZvHh/E+T5jzH0YFjjbbYBE\nyEVprmK1d3HJJk0uvHFsIPJzeZ4CNpc/coI5MGmHP8jgrU+GIleDAODJu+qwrlSND06PRraJBby0\n6DNOvqPTXyrGmNRMEymLQ3NQoihCyRLvz7AMftX8LDrNPQAAk9eC59pfwtcbngBNkcuXqSRbJcY/\nPLoR7342AhYs7r6mNKHnuePqEnzSqofHNzuy2FCmxpMJJNILoSgKW6szI+UA0xYPRqaccctdGIbF\n/s+G0TtmBZemwefR4HM54PNo+AIhfNKqj7r/zoYcfOGmKtA0hYZyDXSFGXjz40EcPDMac/leJOBg\nc1UWrl6XjfI8BTy+EEanHRiecmJkyoHhKQfGDa6oxzg9AZxon31NCsAjeyqjuoZIRTxsr8vG0ZnR\n92G9A71jtoRXmLwcjE078d5M6Q4Q/gwq8hVo6g0n1yyAFw/1wuby456dpSl5YnGifSrq5wd3V0R6\niF8w9z0CwHUb8iK92YuyZRieWXW0qdeAB3eXp+T7JIh0RZJpIuXQFI3Hax/Cf5z+GUwzrfY6zT14\ne+B93Fl2yxrvHXExhVSAh26oWNFzSEU83L+rDL+f6VNdX6bG1+9ed0nqfTdXZ0WSaQA42TkVN5l+\n58QQ3vx4cEnPuXtjPh66oSIqsRXyuXhwdwW21mThpUO9GJ5yoLpIie112VhfrgF/TqcNsZALXaEy\nqtZ8wujC4XNj+LRVHzUaecE1DbkoyZHHbL9hU0EkmQaAg6dHUz6ZHpy048OzY7C7A/AHQvAHQ/AH\nGPgCITAsi+pCJe7bVQbFRYsahRgWz73XFVWv/tANFdhanYU/HuzBkabxyPb9J4Zhc/nx+M3hE55U\nwbIsPptzkpSlFKEkR4bSXDmc7gBeOtwb85gspQh7ryuP/LyhQhNJpo02L8YMrrQt7yGIVESSaSIl\nSXkSfGXdY/jvs79AgAmPVn4wfARFsnysz1y3xntHXAq7NuQhVyOB1x9EXan6kvVpztNIkK+VYGxm\n5Pd05xTu21UW9XpTFnfUEt0L+dxVRbj32vlHPEty5Pj7RzeCZdlljRbmaiR4dI8O9+4swydtk/jw\n7BimZiaeKWUC3Htt/KsAuRoJ6kpVaBsItzA812uAweqBNiM162jtLj/+++XzkVKheD5p06NlwIQn\nPleNhjn9s9/7dDCqPKi2RIWrarJAURQe2VMJhZQfdUJ0vGUSXJrCozfpEhq59QVCONGmh0LKR0O5\nJim/o31zVugEwhN8L+zbjZsL4PD4o34XaYrCl2+vgYA/ezLWWKGNep9NPYa0TKZ9gRBeP9qPsWkn\nbr+6GDWXyeRSIv2Rmuk4UrGO50qkEMigEirRbGiLbGs3daFeWwsZf2V/KEiMU5NaIUSWSrziS9SL\nxdflDUYm6nl8IdSVqKGa6QnNsix+81YHpsyzC9Bkq8SQCLng8zjgzIxqCngc3LOzFLdfXbyk/U30\nPfG4NMpyFbh+Yz4qCzJQnqfAg9eXQy6Zf5KnTMyLLh1ggbo5q1XOJ8QwsDp8EPI5q1Ym8MqRvnk7\nm8zlDzA42TEFpzuAqsIM+EMs/u250wjO1JvzuTS+tbchshAPRVHQFSqRIeWjpd8UeZ4hvQMcDr3s\nHuMMy+KnrzXj0NkxnOqcRv+4DdVFqhUv4b3/xDCG9LOdjR6/pQqSOYsJVRUq4fAEMDQZvs99u8pi\nFjcKx1sP98wETLcviF0b8la0X2vt4mM4vOJnB463TMJk9+JM1zQ2V2cmfeElYvWk4t9hUjNNpKUt\n2Y0Yto/i6NgnAABfyI//OvMLVCjLUKWsgE5VjmxxJqkPJJZlS3Vm1ISvU51TKM8PT1A73TUdtTBN\neZ4Cf/tI4yVf0XAxNEWhpliFmuLF71tbrEKuRoKJma4fh86OoanXiHWlKqwrVaOqSAmRgItgiMGQ\n3oHuEQu6R63oG7PB6w+hMFOKL99Wg/xLPLo5bnTh2NzuKkIu8rRS8Hk0BFwO+DwOhvT2qCXTD58b\nQ9eoBWqFKKrG/s4dJciMM/p+7fo8iARc/ObP7ZEmnfuODUApFWBH/dKXlP/o/AQ6hmZXeG0fsuB7\nz5zEIzdV4qqa7GW861nBEIPTnbMnPWV58phVQymKwqN7dNhWkw2aplCaG1vaQ1EUNlRoIxMRR6ac\nMNm8kQWQ0sG7nw3jVOfs5Ft/kMHzB7rwnYc2rPmxSRAkmSZS3j3lt2HUMYF+W/gypjfkQ6uxA63G\n8OQcBV+GSmUFatSVqFVXQcKLv4Q1QVyQqRSjJEeGwZnRvtNd03hwdwW8/lBUjSpNUfjCTbrL7o81\nRVG4cVM+nn+vO7LNZPfi6PkJHD0/AQ5NIU8rgd7shj8Q20lkZNqJHzx/BvfvKsPuTfnzvv9Jkwut\n/SbY3H443QE4PbP//IEQ1pWq8fCNlfPWvr92pC+qDeITt1ZjQ0X0oiP+QAivHunDh+dm65/HDa6o\nCZoFmVLcuLlg3s9jS3UW7C4/Xjw0G9vnDnRBLuFHJvEtxGz34rUjfTHb3b4gnnqrA+d7jXhkj27Z\no6St/aaodn7baudPyi+c7M1nQ4UmqqtHU68BN2yK/kysTh/+dLQfbl8QdSUqNOoyF12s6VKbtrhh\nsvtQka+Y9/ekpd+INz4aiNneNWLFsfMTl/0o/JXIaPPgUNMEcjQSVOfJU2oeQyJImUccqXjp4UpG\nUzRqNVVoNrTDHYxdrMAX8mPCNYlmQxsOjx5Dt6UXroAbEq4YUn78pXUvjrEv5MfZqfP4ePwE3AEP\n8qQ5ZLT7MraUY9jjC6F9KDwC7QuEoCtU4kjTeKT8AwBu2lKIbXWJjTqutTyNFL2j1siKj3OxbHjF\nvXgLzVzAsCzaBs3on7CjplgJIT889sIwLJr7TPjjwW68/GEf2gbN6BuzYWTKCb3ZDbPdB6cnAI8v\nhCG9AxanD+vLNTHHU8eQGfvm1PnqCjJw77VlMffjcGjUl2lQmCVFx5Alpq85BeAb99UvunR7aa4C\n/kAIfePhkhIWtRKMkAAAIABJREFUQFOvEbUlqgX7orMsi6fe7ojq7S0V8aL2Y9zowol2PfI0kpiR\n5YXsOzaAiZlRdw5N4YnPVSe8BLxSJsCRc+OR/QqGWFy9bnbk3e7y48cvNqFz2AK92Y2WfhM+OD2C\n7hELfIEQVDJBJMarpX3IjB+9cBbHWybR1GtAcY48EosLx3D/qAX/82ozAnMWcZr7K9I9asW22uwV\nl9ukKpZl4Q8w4NBU2vxNauox4P9/9Tyaug040TqJ0Wkn1ldoUmKBKdJnOolIMp16BBwBtudugVKY\nAR6HB4fPgQATiLkfCxZmrxVd5l58NP4pzuibMOmegivgAo/Dg4QbrseVSARgWAYt4914d+ggXuh8\nFeemWzDiGEezsR2gKFQqy9bgnRLJsJRjWCUXRI3kGayeqOWcVXJB0lvzrSYOTeHqddlYX6GBSi5E\nKMTA6vBjvvQ5Y2ZSnUzEi5oQZ7B68EmrHiq5EO2DZjzzTgeONI3DYF3aQkwjU07weRxU5M/WKDMM\ni1/ua4PdNRufv7inDkrZ/AlxjlqCq2qzMWZwRr32DZvysbMhd0n7Ul2sxLTVE5l8GmJYnO8NL8Et\nmWdU+WTHFN79bLYtXVGWDD/40hawLCKJOQB4/SGcaJ9CIMhAV5ix6NUMtzeI597rjozMN5Rplvw+\n4qEpCpMmN0amwz36zXYfdm/KB5/LgcsbwH+9fD5S9jOX0eYNJ9anRtE7ZkVRthxy8aUfrXZ5AzMT\nT8OdauzuAD5umYDPH0J5vgJymRBOTwD/8uypqMVsrqnPwaaqzMhJbzDEQm92Y+vMxNN0wLIsxg0u\nHDo7ij+8341XjvShZ9SKulI1hPzETrZSQYhh8MZHA/jjwZ6oFW71Zjc6hsxYX6Fd8/eXaDJNsRc3\nQE0xBoNj1XdQq5VdeO3VfmliiRiWwZhzAt3mPnRb+tBnHYh0/ViImCtCsaIQhaocnJtoxbTLNO99\nH6i8Czvztydzt4lVstRj+EcvnJ138ttf3rMOGyq1cW+7XLm8AXQOWdA6YMKk2Y3MDBF0BRnQFWZA\nmyECRVFgWBaHTo/i9Y/6o/7gLYRDU5CKeJF/IgEXzf3GqP7aX7+rDpu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z4aRlSG9HrkaC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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff169616a90>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 361,
              "height": 248
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "UlKn5K1WAMn3",
        "colab_type": "code",
        "outputId": "a11998dd-0155-4ca9-891c-aacfe62fbbc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(test_accuracies, label='Validation accuracy')\n",
        "plt.legend(frameon=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff168d1f0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6vIN/egf/3jfglzvZpVT38J8kpbldys5wa83iwlFLeYfqH/Brb8jo+NK5ubr5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\niIgP/T+ZCviAEYr7TwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff168d1f2b0>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 370,
              "height": 248
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yP80nPH9yTnY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Save the checkpoint\n",
        "\n",
        "Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: `image_datasets['train'].class_to_idx`. You can attach this to the model as an attribute which makes inference easier later on.\n",
        "\n",
        "```model.class_to_idx = image_datasets['train'].class_to_idx```\n",
        "\n",
        "Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, `optimizer.state_dict`. You'll likely want to use this trained model in the next part of the project, so best to save it now."
      ]
    },
    {
      "metadata": {
        "id": "hcEl2PbUyTnZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# TODO: Save the checkpoint\n",
        "checkpoint = {'arch': 'resnet152',\n",
        "              'epoch': epoch + 1,\n",
        "              'model_state_dict': model.state_dict(),\n",
        "              'optimizer_state_dict': optimizer.state_dict(),\n",
        "              'class_to_idx': train_data.class_to_idx,\n",
        "              'learning_rate': 0.001}\n",
        "\n",
        "torch.save(checkpoint, 'checkpoint.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RP317ukTyTnb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Loading the checkpoint\n",
        "\n",
        "At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network."
      ]
    },
    {
      "metadata": {
        "id": "iJtQaC_nyTnc",
        "colab_type": "code",
        "outputId": "114e2675-4072-45ec-aca0-cf4924917584",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8279
        }
      },
      "cell_type": "code",
      "source": [
        "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
        "def load_checkpoint(filepath):\n",
        "    checkpoint = torch.load(filepath)\n",
        "    # checkpoint = torch.load(filepath, map_location='cpu')\n",
        "    epoch = checkpoint['epoch']\n",
        "    \n",
        "    model = models.resnet152(pretrained=True)\n",
        "    for param in model.parameters():\n",
        "        param.requires_grad = False\n",
        "    model.fc = Classifier()\n",
        "    model.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
        "    model.class_to_idx = checkpoint['class_to_idx']\n",
        "    \n",
        "    optimizer = optim.Adam(model.fc.parameters(), lr=checkpoint['learning_rate'])\n",
        "    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "    \n",
        "    return model\n",
        "  \n",
        "model = load_checkpoint('checkpoint_Batch32_Dropout_Acc92.pth')\n",
        "model.eval()"
      ],
      "execution_count": 0,
      "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)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\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): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (4): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (5): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (6): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (7): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (4): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (5): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (6): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (7): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (8): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (9): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (10): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (11): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (12): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (13): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (14): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (15): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (16): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (17): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (18): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (19): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (20): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (21): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (22): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (23): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (24): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (25): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (26): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (27): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (28): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (29): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (30): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (31): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (32): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (33): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (34): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (35): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)\n",
              "  (fc): Classifier(\n",
              "    (fc1): Linear(in_features=2048, out_features=1024, bias=True)\n",
              "    (fc2): Linear(in_features=1024, out_features=102, bias=True)\n",
              "    (dropout): Dropout(p=0.1)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "0De3Vmu4yTne",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Inference for classification\n",
        "\n",
        "Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called `predict` that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like \n",
        "\n",
        "```python\n",
        "probs, classes = predict(image_path, model)\n",
        "print(probs)\n",
        "print(classes)\n",
        "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
        "> ['70', '3', '45', '62', '55']\n",
        "```\n",
        "\n",
        "First you'll need to handle processing the input image such that it can be used in your network. \n",
        "\n",
        "## Image Preprocessing\n",
        "\n",
        "You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training. \n",
        "\n",
        "First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.\n",
        "\n",
        "Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.\n",
        "\n",
        "As before, the network expects the images to be normalized in a specific way. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`. You'll want to subtract the means from each color channel, then divide by the standard deviation. \n",
        "\n",
        "And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html). The color channel needs to be first and retain the order of the other two dimensions."
      ]
    },
    {
      "metadata": {
        "id": "HlfYCRD3yTng",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def process_image(image):\n",
        "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
        "        returns an Numpy array\n",
        "    '''\n",
        "    \n",
        "    # TODO: Process a PIL image for use in a PyTorch model\n",
        "    preprocess = transforms.Compose([transforms.Resize(256),\n",
        "                                     transforms.CenterCrop(224),\n",
        "                                     transforms.ToTensor(),\n",
        "                                     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
        "    \n",
        "    img_tensor = preprocess(image)\n",
        "    return img_tensor"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YOqgB3WoyTnm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your `process_image` function works, running the output through this function should return the original image (except for the cropped out portions)."
      ]
    },
    {
      "metadata": {
        "id": "057KUkpUyTnn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def imshow(image, ax=None, title=None):\n",
        "    \"\"\"Imshow for Tensor.\"\"\"\n",
        "    if ax is None:\n",
        "        fig, ax = plt.subplots()\n",
        "    \n",
        "    # PyTorch tensors assume the color channel is the first dimension\n",
        "    # but matplotlib assumes is the third dimension\n",
        "    image = image.numpy().transpose((1, 2, 0))\n",
        "    \n",
        "    # Undo preprocessing\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    image = std * image + mean\n",
        "    \n",
        "    # Image needs to be clipped between 0 and 1 or it looks like noise when displayed\n",
        "    image = np.clip(image, 0, 1)\n",
        "    \n",
        "    ax.imshow(image)\n",
        "    \n",
        "    return ax"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_zfMn3bAyTnq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Class Prediction\n",
        "\n",
        "Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n",
        "\n",
        "To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n",
        "\n",
        "Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n",
        "\n",
        "```python\n",
        "probs, classes = predict(image_path, model)\n",
        "print(probs)\n",
        "print(classes)\n",
        "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
        "> ['70', '3', '45', '62', '55']\n",
        "```"
      ]
    },
    {
      "metadata": {
        "id": "Yh4mLmNhyTns",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def predict(image_path, model, topk=5):\n",
        "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
        "    '''\n",
        "    \n",
        "    # TODO: Implement the code to predict the class from an image file\n",
        "    \n",
        "    # Get the class probabilities\n",
        "    # Use .unsqueeze_(0) to add a dimension for the batch\n",
        "    logps = model(process_image(Image.open(image_path)).unsqueeze_(0))\n",
        "    probs = torch.exp(logps)\n",
        "    return probs.topk(topk, dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YwrSDDV7yTny",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Sanity Checking\n",
        "\n",
        "Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the validation accuracy is high, it's always good to check that there aren't obvious bugs. Use `matplotlib` to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:\n",
        "\n",
        "<img src='assets/inference_example.png' width=300px>\n",
        "\n",
        "You can convert from the class integer encoding to actual flower names with the `cat_to_name.json` file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the `imshow` function defined above."
      ]
    },
    {
      "metadata": {
        "id": "ggJ58JflyTnz",
        "colab_type": "code",
        "outputId": "b505b8df-4b8c-4edd-cda3-e1dd28483e37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        }
      },
      "cell_type": "code",
      "source": [
        "# TODO: Display an image along with the top 5 classes\n",
        "def sanity_check(image_path, model, topk):\n",
        "    # Set up plot\n",
        "    fig, (ax1, ax2) = plt.subplots(figsize=(6,6), nrows=2)\n",
        "    ax1.axis('off')\n",
        "    \n",
        "    # Set up title\n",
        "    flower_num = image_path.split('/')[-2]\n",
        "    flower_title = cat_to_name[flower_num]\n",
        "    ax1.set_title(flower_title)\n",
        "    \n",
        "    # Flower\n",
        "    img = process_image(Image.open(image_path))\n",
        "    imshow(img, ax1);\n",
        "    \n",
        "    # Make prediction\n",
        "    probs, labs = predict(image_path, model, topk)\n",
        "    print(probs)\n",
        "    print(labs)\n",
        "    \n",
        "    # Convert indices to classes\n",
        "    idx_to_class = {val: key for key, val in model.class_to_idx.items()}\n",
        "#     print(idx_to_class)\n",
        "    flowers = [cat_to_name[idx_to_class[lab_idx]] for lab_idx in labs.tolist()[0]]\n",
        "    print(flowers)\n",
        "    \n",
        "    # Plot bar chart\n",
        "    ax2.barh(np.arange(topk), probs.data.numpy().squeeze())\n",
        "    ax2.set_aspect(0.1)\n",
        "    ax2.set_yticks(np.arange(topk))\n",
        "    ax2.set_yticklabels(flowers, size='small')\n",
        "    ax2.set_xlim(0, 1.0)\n",
        "    \n",
        "    plt.gca().invert_yaxis()\n",
        "    plt.tight_layout()\n",
        "    \n",
        "image_path=valid_dir+'/13/image_05759.jpg'\n",
        "sanity_check(image_path, model, 5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[9.8901e-01, 5.6039e-03, 1.7326e-03, 1.0479e-03, 8.1246e-04]],\n",
            "       grad_fn=<TopkBackward>)\n",
            "tensor([[ 7,  8, 24, 80, 77]])\n",
            "['king protea', 'spear thistle', 'artichoke', 'bird of paradise', 'passion flower']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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6W2TcrzqQcPVuWQDYv3ED1+yScNCHnpIFgge+0uV5Ve/rzS9h1EheNzXX1Aor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5hbUue9eVnjtVYdR3PiijTlL78qEtkXvv9dTqxIsDbsy7Vi6zAYla6DKcuLIlUsgFQLOBca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\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clLV+2XvuekD33vtRSdLpk5vauBjKYgf+vabTkba3fWapGLRyoQOls6XvLgjc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\nf3IPiTeKY4ZYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff16399d2e8>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 418,
              "height": 423
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "eArGnvY8yTn3",
        "colab_type": "code",
        "outputId": "e3aff1f8-a162-4d7a-8ba0-6efd365c8529",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_loss = 0\n",
        "accuracy = 0\n",
        "model.eval()\n",
        "model.to(device)\n",
        "\n",
        "with torch.no_grad():\n",
        "    for inputs, labels in validloader:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        logps = model.forward(inputs)\n",
        "        batch_loss = criterion(logps, labels)\n",
        "\n",
        "        test_loss += batch_loss.item()\n",
        "\n",
        "        # Calculate accuracy\n",
        "        ps = torch.exp(logps)\n",
        "        top_p, top_class = ps.topk(1, dim=1)\n",
        "        equals = top_class == labels.view(*top_class.shape)\n",
        "        accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
        "\n",
        "print(f\"Test loss: {test_loss/len(validloader):.3f}.. \"\n",
        "      f\"Test accuracy: {accuracy/len(validloader):.3f}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.294.. Test accuracy: 0.921\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bF2H_ePl_Bbd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}