FlowerClassifier_Batch32_Dropout_Unfreezed_Acc92.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/wanghsinwei/pytorch_challenge/blob/master/FlowerClassifier_Batch32_Dropout_Unfreezed_Acc92.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "HMDj9fZXyTm7",
        "colab_type": "text"
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      "cell_type": "markdown",
      "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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    {
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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": "77154369-9025-4901-e7dd-0b18d32b3f07",
        "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: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.6)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (5.4.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6dl0fstCyTnC",
        "colab_type": "code",
        "outputId": "870a52ec-a378-4bc7-9f35-984991940559",
        "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": "859dd9f5-65f7-4fd0-b365-afc9e2748bdd",
        "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",
        "colab_type": "text"
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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": "e8f6a632-e7d8-4123-c558-6a60d73730ce",
        "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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      "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": "2251b96f-d419-48c3-cd64-fad38e3ebe76",
        "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": "c1fba540-81f1-47b3-c45a-7e74291fe052",
        "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: 30.643 seconds\n",
            "Epoch 1/10.. Step: 20.. Train loss: 4.544.. Test loss: 4.184.. Test accuracy: 0.163\n",
            "Validate time: 30.752 seconds\n",
            "Epoch 1/10.. Step: 40.. Train loss: 4.094.. Test loss: 3.768.. Test accuracy: 0.184\n",
            "Validate time: 30.808 seconds\n",
            "Epoch 1/10.. Step: 60.. Train loss: 3.662.. Test loss: 3.209.. Test accuracy: 0.304\n",
            "Validate time: 30.756 seconds\n",
            "Epoch 1/10.. Step: 80.. Train loss: 3.132.. Test loss: 2.722.. Test accuracy: 0.354\n",
            "Validate time: 30.825 seconds\n",
            "Epoch 1/10.. Step: 100.. Train loss: 2.624.. Test loss: 2.176.. Test accuracy: 0.495\n",
            "Validate time: 30.776 seconds\n",
            "Epoch 1/10.. Step: 120.. Train loss: 2.352.. Test loss: 1.697.. Test accuracy: 0.655\n",
            "Validate time: 30.843 seconds\n",
            "Epoch 1/10.. Step: 140.. Train loss: 2.014.. Test loss: 1.540.. Test accuracy: 0.618\n",
            "Validate time: 30.819 seconds\n",
            "Epoch 1/10.. Step: 160.. Train loss: 1.887.. Test loss: 1.285.. Test accuracy: 0.692\n",
            "Validate time: 30.862 seconds\n",
            "Epoch 1/10.. Step: 180.. Train loss: 1.640.. Test loss: 1.043.. Test accuracy: 0.751\n",
            "Validate time: 30.895 seconds\n",
            "Epoch 1/10.. Step: 200.. Train loss: 1.460.. Test loss: 1.056.. Test accuracy: 0.729\n",
            "Time per epoch: 808.678 seconds\n",
            "Validate time: 30.951 seconds\n",
            "Epoch 2/10.. Step: 220.. Train loss: 1.483.. Test loss: 0.948.. Test accuracy: 0.777\n",
            "Validate time: 30.872 seconds\n",
            "Epoch 2/10.. Step: 240.. Train loss: 1.234.. Test loss: 0.844.. Test accuracy: 0.769\n",
            "Validate time: 30.900 seconds\n",
            "Epoch 2/10.. Step: 260.. Train loss: 1.186.. Test loss: 0.729.. Test accuracy: 0.830\n",
            "Validate time: 30.931 seconds\n",
            "Epoch 2/10.. Step: 280.. Train loss: 1.196.. Test loss: 0.730.. Test accuracy: 0.808\n",
            "Validate time: 30.973 seconds\n",
            "Epoch 2/10.. Step: 300.. Train loss: 1.022.. Test loss: 0.833.. Test accuracy: 0.770\n",
            "Validate time: 30.764 seconds\n",
            "Epoch 2/10.. Step: 320.. Train loss: 1.175.. Test loss: 0.694.. Test accuracy: 0.819\n",
            "Validate time: 30.879 seconds\n",
            "Epoch 2/10.. Step: 340.. Train loss: 1.060.. Test loss: 0.668.. Test accuracy: 0.826\n",
            "Validate time: 30.895 seconds\n",
            "Epoch 2/10.. Step: 360.. Train loss: 0.978.. Test loss: 0.596.. Test accuracy: 0.864\n",
            "Validate time: 30.872 seconds\n",
            "Epoch 2/10.. Step: 380.. Train loss: 0.936.. Test loss: 0.525.. Test accuracy: 0.877\n",
            "Validate time: 30.920 seconds\n",
            "Epoch 2/10.. Step: 400.. Train loss: 1.015.. Test loss: 0.645.. Test accuracy: 0.811\n",
            "Time per epoch: 810.374 seconds\n",
            "Validate time: 30.880 seconds\n",
            "Epoch 3/10.. Step: 420.. Train loss: 0.876.. Test loss: 0.621.. Test accuracy: 0.833\n",
            "Validate time: 30.737 seconds\n",
            "Epoch 3/10.. Step: 440.. Train loss: 0.858.. Test loss: 0.499.. Test accuracy: 0.874\n",
            "Validate time: 30.912 seconds\n",
            "Epoch 3/10.. Step: 460.. Train loss: 0.837.. Test loss: 0.506.. Test accuracy: 0.867\n",
            "Validate time: 30.903 seconds\n",
            "Epoch 3/10.. Step: 480.. Train loss: 0.928.. Test loss: 0.526.. Test accuracy: 0.858\n",
            "Validate time: 30.904 seconds\n",
            "Epoch 3/10.. Step: 500.. Train loss: 0.875.. Test loss: 0.498.. Test accuracy: 0.872\n",
            "Validate time: 30.847 seconds\n",
            "Epoch 3/10.. Step: 520.. Train loss: 0.858.. Test loss: 0.424.. Test accuracy: 0.896\n",
            "Validate time: 30.938 seconds\n",
            "Epoch 3/10.. Step: 540.. Train loss: 0.801.. Test loss: 0.385.. Test accuracy: 0.909\n",
            "Validate time: 30.936 seconds\n",
            "Epoch 3/10.. Step: 560.. Train loss: 0.777.. Test loss: 0.417.. Test accuracy: 0.888\n",
            "Validate time: 30.881 seconds\n",
            "Epoch 3/10.. Step: 580.. Train loss: 0.732.. Test loss: 0.441.. Test accuracy: 0.882\n",
            "Validate time: 30.884 seconds\n",
            "Epoch 3/10.. Step: 600.. Train loss: 0.695.. Test loss: 0.405.. Test accuracy: 0.900\n",
            "Time per epoch: 810.109 seconds\n",
            "Validate time: 31.277 seconds\n",
            "Epoch 4/10.. Step: 620.. Train loss: 0.720.. Test loss: 0.427.. Test accuracy: 0.875\n",
            "Validate time: 31.027 seconds\n",
            "Epoch 4/10.. Step: 640.. Train loss: 0.713.. Test loss: 0.411.. Test accuracy: 0.888\n",
            "Validate time: 30.986 seconds\n",
            "Epoch 4/10.. Step: 660.. Train loss: 0.638.. Test loss: 0.388.. Test accuracy: 0.894\n",
            "Validate time: 30.995 seconds\n",
            "Epoch 4/10.. Step: 680.. Train loss: 0.662.. Test loss: 0.398.. Test accuracy: 0.881\n",
            "Validate time: 30.939 seconds\n",
            "Epoch 4/10.. Step: 700.. Train loss: 0.719.. Test loss: 0.398.. Test accuracy: 0.892\n",
            "Validate time: 30.919 seconds\n",
            "Epoch 4/10.. Step: 720.. Train loss: 0.819.. Test loss: 0.473.. Test accuracy: 0.881\n",
            "Validate time: 30.947 seconds\n",
            "Epoch 4/10.. Step: 740.. Train loss: 0.651.. Test loss: 0.356.. Test accuracy: 0.913\n",
            "Validate time: 30.961 seconds\n",
            "Epoch 4/10.. Step: 760.. Train loss: 0.637.. Test loss: 0.337.. Test accuracy: 0.903\n",
            "Validate time: 30.925 seconds\n",
            "Epoch 4/10.. Step: 780.. Train loss: 0.670.. Test loss: 0.443.. Test accuracy: 0.874\n",
            "Validate time: 31.036 seconds\n",
            "Epoch 4/10.. Step: 800.. Train loss: 0.699.. Test loss: 0.392.. Test accuracy: 0.894\n",
            "Validate time: 30.979 seconds\n",
            "Epoch 4/10.. Step: 820.. Train loss: 0.584.. Test loss: 0.412.. Test accuracy: 0.879\n",
            "Time per epoch: 843.465 seconds\n",
            "Validate time: 31.020 seconds\n",
            "Epoch 5/10.. Step: 840.. Train loss: 0.678.. Test loss: 0.347.. Test accuracy: 0.907\n",
            "Validate time: 30.958 seconds\n",
            "Epoch 5/10.. Step: 860.. Train loss: 0.556.. Test loss: 0.309.. Test accuracy: 0.919\n",
            "Validate time: 30.905 seconds\n",
            "Epoch 5/10.. Step: 880.. Train loss: 0.624.. Test loss: 0.328.. Test accuracy: 0.920\n",
            "Validate time: 30.950 seconds\n",
            "Epoch 5/10.. Step: 900.. Train loss: 0.744.. Test loss: 0.374.. Test accuracy: 0.903\n",
            "Validate time: 30.972 seconds\n",
            "Epoch 5/10.. Step: 920.. Train loss: 0.585.. Test loss: 0.336.. Test accuracy: 0.901\n",
            "Validate time: 30.952 seconds\n",
            "Epoch 5/10.. Step: 940.. Train loss: 0.573.. Test loss: 0.330.. Test accuracy: 0.916\n",
            "Validate time: 30.897 seconds\n",
            "Epoch 5/10.. Step: 960.. Train loss: 0.567.. Test loss: 0.345.. Test accuracy: 0.907\n",
            "Validate time: 30.824 seconds\n",
            "Epoch 5/10.. Step: 980.. Train loss: 0.634.. Test loss: 0.365.. Test accuracy: 0.907\n",
            "Validate time: 30.861 seconds\n",
            "Epoch 5/10.. Step: 1000.. Train loss: 0.609.. Test loss: 0.287.. Test accuracy: 0.927\n",
            "Validate time: 30.934 seconds\n",
            "Epoch 5/10.. Step: 1020.. Train loss: 0.638.. Test loss: 0.326.. Test accuracy: 0.919\n",
            "Time per epoch: 811.322 seconds\n",
            "Validate time: 30.883 seconds\n",
            "Epoch 6/10.. Step: 1040.. Train loss: 0.544.. Test loss: 0.301.. Test accuracy: 0.914\n",
            "Validate time: 30.893 seconds\n",
            "Epoch 6/10.. Step: 1060.. Train loss: 0.604.. Test loss: 0.280.. Test accuracy: 0.927\n",
            "Validate time: 30.963 seconds\n",
            "Epoch 6/10.. Step: 1080.. Train loss: 0.585.. Test loss: 0.276.. Test accuracy: 0.932\n",
            "Validate time: 30.919 seconds\n",
            "Epoch 6/10.. Step: 1100.. Train loss: 0.632.. Test loss: 0.316.. Test accuracy: 0.921\n",
            "Validate time: 30.877 seconds\n",
            "Epoch 6/10.. Step: 1120.. Train loss: 0.540.. Test loss: 0.329.. Test accuracy: 0.923\n",
            "Validate time: 30.918 seconds\n",
            "Epoch 6/10.. Step: 1140.. Train loss: 0.574.. Test loss: 0.285.. Test accuracy: 0.931\n",
            "Validate time: 30.888 seconds\n",
            "Epoch 6/10.. Step: 1160.. Train loss: 0.594.. Test loss: 0.349.. Test accuracy: 0.902\n",
            "Validate time: 30.860 seconds\n",
            "Epoch 6/10.. Step: 1180.. Train loss: 0.655.. Test loss: 0.359.. Test accuracy: 0.902\n",
            "Validate time: 30.987 seconds\n",
            "Epoch 6/10.. Step: 1200.. Train loss: 0.609.. Test loss: 0.311.. Test accuracy: 0.919\n",
            "Validate time: 30.902 seconds\n",
            "Epoch 6/10.. Step: 1220.. Train loss: 0.593.. Test loss: 0.322.. Test accuracy: 0.917\n",
            "Time per epoch: 810.790 seconds\n",
            "Validate time: 30.901 seconds\n",
            "Epoch 7/10.. Step: 1240.. Train loss: 0.544.. Test loss: 0.347.. Test accuracy: 0.898\n",
            "Validate time: 30.879 seconds\n",
            "Epoch 7/10.. Step: 1260.. Train loss: 0.590.. Test loss: 0.320.. Test accuracy: 0.915\n",
            "Validate time: 30.928 seconds\n",
            "Epoch 7/10.. Step: 1280.. Train loss: 0.596.. Test loss: 0.292.. Test accuracy: 0.922\n",
            "Validate time: 31.201 seconds\n",
            "Epoch 7/10.. Step: 1300.. Train loss: 0.517.. Test loss: 0.329.. Test accuracy: 0.911\n",
            "Validate time: 31.300 seconds\n",
            "Epoch 7/10.. Step: 1320.. Train loss: 0.536.. Test loss: 0.327.. Test accuracy: 0.915\n",
            "Validate time: 31.320 seconds\n",
            "Epoch 7/10.. Step: 1340.. Train loss: 0.546.. Test loss: 0.290.. Test accuracy: 0.925\n",
            "Validate time: 31.267 seconds\n",
            "Epoch 7/10.. Step: 1360.. Train loss: 0.559.. Test loss: 0.337.. Test accuracy: 0.909\n",
            "Validate time: 31.235 seconds\n",
            "Epoch 7/10.. Step: 1380.. Train loss: 0.645.. Test loss: 0.280.. Test accuracy: 0.933\n",
            "Validate time: 31.283 seconds\n",
            "Epoch 7/10.. Step: 1400.. Train loss: 0.630.. Test loss: 0.281.. Test accuracy: 0.925\n",
            "Validate time: 31.375 seconds\n",
            "Epoch 7/10.. Step: 1420.. Train loss: 0.480.. Test loss: 0.336.. Test accuracy: 0.901\n",
            "Time per epoch: 815.247 seconds\n",
            "Validate time: 31.271 seconds\n",
            "Epoch 8/10.. Step: 1440.. Train loss: 0.525.. Test loss: 0.283.. Test accuracy: 0.922\n",
            "Validate time: 31.213 seconds\n",
            "Epoch 8/10.. Step: 1460.. Train loss: 0.478.. Test loss: 0.339.. Test accuracy: 0.898\n",
            "Validate time: 31.282 seconds\n",
            "Epoch 8/10.. Step: 1480.. Train loss: 0.609.. Test loss: 0.262.. Test accuracy: 0.934\n",
            "Validate time: 31.261 seconds\n",
            "Epoch 8/10.. Step: 1500.. Train loss: 0.519.. Test loss: 0.302.. Test accuracy: 0.919\n",
            "Validate time: 31.265 seconds\n",
            "Epoch 8/10.. Step: 1520.. Train loss: 0.516.. Test loss: 0.279.. Test accuracy: 0.926\n",
            "Validate time: 31.265 seconds\n",
            "Epoch 8/10.. Step: 1540.. Train loss: 0.498.. Test loss: 0.268.. Test accuracy: 0.925\n",
            "Validate time: 31.351 seconds\n",
            "Epoch 8/10.. Step: 1560.. Train loss: 0.561.. Test loss: 0.257.. Test accuracy: 0.935\n",
            "Validate time: 31.213 seconds\n",
            "Epoch 8/10.. Step: 1580.. Train loss: 0.523.. Test loss: 0.320.. Test accuracy: 0.906\n",
            "Validate time: 31.315 seconds\n",
            "Epoch 8/10.. Step: 1600.. Train loss: 0.525.. Test loss: 0.307.. Test accuracy: 0.914\n",
            "Validate time: 31.328 seconds\n",
            "Epoch 8/10.. Step: 1620.. Train loss: 0.589.. Test loss: 0.291.. Test accuracy: 0.924\n",
            "Validate time: 31.279 seconds\n",
            "Epoch 8/10.. Step: 1640.. Train loss: 0.463.. Test loss: 0.292.. Test accuracy: 0.916\n",
            "Time per epoch: 848.376 seconds\n",
            "Validate time: 31.331 seconds\n",
            "Epoch 9/10.. Step: 1660.. Train loss: 0.521.. Test loss: 0.261.. Test accuracy: 0.931\n",
            "Validate time: 31.257 seconds\n",
            "Epoch 9/10.. Step: 1680.. Train loss: 0.503.. Test loss: 0.304.. Test accuracy: 0.921\n",
            "Validate time: 31.339 seconds\n",
            "Epoch 9/10.. Step: 1700.. Train loss: 0.457.. Test loss: 0.313.. Test accuracy: 0.909\n",
            "Validate time: 31.277 seconds\n",
            "Epoch 9/10.. Step: 1720.. Train loss: 0.516.. Test loss: 0.290.. Test accuracy: 0.921\n",
            "Validate time: 31.306 seconds\n",
            "Epoch 9/10.. Step: 1740.. Train loss: 0.560.. Test loss: 0.302.. Test accuracy: 0.916\n",
            "Validate time: 31.307 seconds\n",
            "Epoch 9/10.. Step: 1760.. Train loss: 0.525.. Test loss: 0.284.. Test accuracy: 0.926\n",
            "Validate time: 31.342 seconds\n",
            "Epoch 9/10.. Step: 1780.. Train loss: 0.489.. Test loss: 0.273.. Test accuracy: 0.932\n",
            "Validate time: 31.318 seconds\n",
            "Epoch 9/10.. Step: 1800.. Train loss: 0.578.. Test loss: 0.268.. Test accuracy: 0.934\n",
            "Validate time: 31.371 seconds\n",
            "Epoch 9/10.. Step: 1820.. Train loss: 0.527.. Test loss: 0.246.. Test accuracy: 0.928\n",
            "Validate time: 31.281 seconds\n",
            "Epoch 9/10.. Step: 1840.. Train loss: 0.589.. Test loss: 0.283.. Test accuracy: 0.917\n",
            "Time per epoch: 817.855 seconds\n",
            "Validate time: 31.320 seconds\n",
            "Epoch 10/10.. Step: 1860.. Train loss: 0.501.. Test loss: 0.287.. Test accuracy: 0.921\n",
            "Validate time: 31.320 seconds\n",
            "Epoch 10/10.. Step: 1880.. Train loss: 0.453.. Test loss: 0.267.. Test accuracy: 0.922\n",
            "Validate time: 31.318 seconds\n",
            "Epoch 10/10.. Step: 1900.. Train loss: 0.457.. Test loss: 0.291.. Test accuracy: 0.921\n",
            "Validate time: 31.316 seconds\n",
            "Epoch 10/10.. Step: 1920.. Train loss: 0.511.. Test loss: 0.268.. Test accuracy: 0.931\n",
            "Validate time: 31.355 seconds\n",
            "Epoch 10/10.. Step: 1940.. Train loss: 0.451.. Test loss: 0.231.. Test accuracy: 0.928\n",
            "Validate time: 31.353 seconds\n",
            "Epoch 10/10.. Step: 1960.. Train loss: 0.561.. Test loss: 0.293.. Test accuracy: 0.920\n",
            "Validate time: 31.366 seconds\n",
            "Epoch 10/10.. Step: 1980.. Train loss: 0.498.. Test loss: 0.287.. Test accuracy: 0.922\n",
            "Validate time: 31.410 seconds\n",
            "Epoch 10/10.. Step: 2000.. Train loss: 0.518.. Test loss: 0.253.. Test accuracy: 0.929\n",
            "Validate time: 31.339 seconds\n",
            "Epoch 10/10.. Step: 2020.. Train loss: 0.461.. Test loss: 0.291.. Test accuracy: 0.921\n",
            "Validate time: 31.387 seconds\n",
            "Epoch 10/10.. Step: 2040.. Train loss: 0.534.. Test loss: 0.319.. Test accuracy: 0.911\n",
            "Time per epoch: 818.363 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wYGk3CDuMh74",
        "colab_type": "code",
        "outputId": "f2c8353d-e40d-4b35-d0d2-e2b5f46095bd",
        "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 0x7f23ab4433c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uMEw3tTWrua1FRXn+1WlWpgEAABCyMG2xWEok/UnSf61Wa/Q7aPdTcYeOHuUd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\nCUnJtIiIiIhISP8PgzoGU4ycvOcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f23ab40ae48>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 361,
              "height": 248
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "UlKn5K1WAMn3",
        "colab_type": "code",
        "outputId": "5cf80fc7-31ca-4942-fcc4-5e22b4929cb3",
        "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 0x7f23abcc9dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OzKTBuEN6fBiu3ZClWHfZ2rRp3aG4Yl26YvlPb5Q5J/cBgLX5cc6BibkpEchL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\nxTvvvM111/2VZ599mksuuYrJkw/huuuuZdq0O9sF8hhfYd68uRx88NS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cY9yzgzZrPI5p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\n6P8DYt8AuQ7nqQIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f23abcc9a58>"
            ]
          },
          "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": "442df11c-c515-4e4b-e5a4-b7cac069a42e",
        "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_Unfreezed_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": 18
        }
      ]
    },
    {
      "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": "26807651-9b56-4381-8c08-bdb5741788af",
        "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([[9.9709e-01, 1.6234e-03, 7.4365e-04, 1.0503e-04, 9.3990e-05]],\n",
            "       grad_fn=<TopkBackward>)\n",
            "tensor([[ 7, 77,  6, 73, 24]])\n",
            "['king protea', 'passion flower', \"colt's foot\", 'water lily', 'artichoke']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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D7KQaV2LHkeYJrUwmP72yx+qq9O354wscOSbGzqCsmNMJWZJpG1qgp8VVKUuLum9RU+j2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J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\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Q8nDV748b6cwJf4xP3hNanE8mipd7Zroi60ILcGe1Wwd7oE8oYwMAAADQS2R2AFw1XNYE\noHtMqdTLWdvdGc+yOa7I5lFJVWHd3fRUkZPdonZ2+xw52/bngvtrtdNUNPlN82we0JTRSeOnXVdm\n1NpucHw5zMrUwh32pmnT3fiimH9PeWla2nRWdhYfswu1e13jsZAxcc3csS9iKkBtNz9RPPx5J6ds\ntdZOtRTuWi+txM5wbWr64ORUDv0/PctLK1rb7xsxXHPNqoZDv/wszY7ZdZKzrfPHKGx7FkraUtme\ntSkLNh5vpWM1mzVqmpgl82sdb040a/02ylLdPDzOdj0sUr3YMHVK8534wlt2bZbR2J4WaGbTrkyt\n7bqtGZtlVbKMZXzM5XMAmSwT13RlbPOZlpA1MGauDC09G5NAxXzGZ748y59HXYmnzUrL5t/b+e6Q\nzlWbtt35dvrkWUk+s7Nn1U8w2kw358pE2yucYVicrHi7vNSwO1atk4bhezIa+Uxe0w41GW+lV5aj\nmJ01Kgfx8wqZvlIq/SHR/rVS193g5+JaWi51ZuO0JGlrPNGeNf85POxheyRJq2tLKRPqmlJq/UrO\nnZzq9Ol1v2/hLxxr2q7oczhJnSEazeQGF/pbDnjwI9gBcNWwTdbKObZ/Nl2bYB/4xOfzF3avc7Fd\nkcmWXXwuPuW2l5fkuthgl0E9eRnbfDVZ2sfu4rkrU8s7WsUSlzxgaNv8qjFbNraPtSaW46tt2zRe\nZFCWqabKZbOnpsvWhavgxYv0ppmlGMnHMsOw5VIhfknvYWAKNeHzKrL3adRo5CttdOAafwF24003\naG2fL7MxhVQOfUlOM2u1FcdwSJrO/CXZ1qa/UNw4t6nJJHT0yg6JM0rzj8Zj5ez8RVs8JmVZaHnZ\nb29pyQdWukYp+LC2C0S2xlva2vLbixOiGtN2Hc+cmwsG83bRcWxVLJ+zrVPbTNMxi8GDyWobu4Ai\nPy+79zFr2oUxOguLZ9fn+fnXBT2m62CXXcxba7vgy2SBXjYuaMft7iA/7kVhVNpuQtTY9Wsy9j/P\nrZ9LAW05KDSdZh31YgfDGPTdl2vwS3itu0ABqMm6CBpjs79/XOrOOAvnryvWsmZ2UxUD/8KlFaeD\nh/z7Xwk3AkbL0v5D/vzcs29Fo6H/zs1aabDiz9fjx1udOxe/ByckSat7B1pd9Zdwrh1q7OMbnTi6\npWN3+e+PnfoDMRwauTKc76MtleH7OSik4RLt2NB/lLEBAAAA6CUyOwCuGnNlMHGAb94ZyhXdhHjZ\n7dv7WoixU7O3hSV2f2ahg5ic0j7auQXUTUya3WeaW3O2rrhPsWwvL12yzqbMg7NOhe3ma0nLl92g\nbxcHvOcN4bJsVprPxWhusLVruvKbdNc/6xRWprvxAxWhddS1h1dVPf4G//vDDvv1u5nuuvtuSdLJ\n02c0Hvu71NNpq3G46z/ZajWL8/PE/bZdWVlhiqzqy0rD7d3JTPbL3P3qcBt+NIyTlZYpEzMcDbS0\n5O+mr66uaXUl3KUP772Ztml/x5MmfTDtLJvzRK7rtpZlE11qcGGzfe+WSeVx2c77LFB83nVdDPKM\nUvhZ2CwnZLsyzVQSVnSZJmMkU8SSvzY7b7s0UZqM1N67b1S3h0Wa48XYLiMWGw5ubWxq34F9kqSy\n3Ny+v1e5+WyO/60sTJfVS1+5Vs767MrqHqdPqvykqjc+ek1r/leNluLfBd3cO411mk59eaXZkvaG\nkr/y8EinTvkmD+fO+fWun5mqDBMOq5EmvieBJuck085/fkvDUkeu91mi8sBUSys+k1QMSg0Go0s/\nIMCDBJkdAAAAAL1EZgfA1aPJMh5Z5iEfW5Ie3qF1rpSPg7gXzM6v2XWC9jSvhrStbW7WUMFIKRPg\nhxCV2xadT+3kG4xjLbLlUyvtbLHCpMcba9Jt8pjtGQxLDUp/97YslMYX2LYbYJ/Gt8i3g457UoTx\nJbaVlsMYg4MH1yRJRw4f1rl1fzv52LEz6Y7+2t4VnTzjB6TfcdRnc06dOqdzoQ2xzTIXbaOFhhMx\nixXmIlE3XsuYspsDyDW+JXR2LOZbKbuuJbO67EdsGGDMfCqhHHTjw+KYiZVwV311z4pW9/jxRpNJ\no3OhjfJmO02njXNWJs6fkg16WtilsGzX1jkmUpxzcx99N85Lu4hzN5m5O5ZpHqK0VDYjTCEVsRFF\nlrmJx61tx98G1gAAIABJREFUbdpw9lHsqhsClLdDNxrE87YouizhoDvn4oFYW1vVdBYGmtjyvqdn\n7yfzcw91absytp4upbjzto3zMZ3QwcP+zT/9M27U459wSJI0WN7U2fWjkrrxPdZa2Zlfdrw1VTPz\nx6+ZDDQLWc/ZuJUNy8fsbGGN3NS/bmCMRjGjO5La8Dm7cL4fPrSk6gk+27p6ZKoiZHNmjbS5cXXM\nZwTcnwh2AFw15q538jlusgtad4GLsJ3mvox2a7i0W3zkzM4XAvFiPB94nubycd0lplM3107evCxu\n0HeEizvVNTvIpbKxousuZ+Xkyu3BYB7opYHjTdsFbfnMk3PvJ/s9LLw0Gmr/Hv8+jxzeqxtvfLgk\naU+48D+3vqlbb92QJJ06OdF04hsNHD16TrNwkRUDp9Z2ZXPWFnLxnx5XpgsyaZIG77cpCLDpWDdZ\n9OBM2UWiaeB/1zrAuazUy3SfRxs6mpVFNzeMta2amU3HoQnLjCe+nGi0KQ1DADQYDLSyuhJ+H2kr\nBHCxiYKUlXQZpVBksQFGDIjiRamfM6jrvBEn+gx7pe1CV74seJ4LetJ28gddmkE0j2VckQU4tnt9\nkX532Vw9JjU02B5Kxv0w2c/YrMG/z6labWz4AHm0vKql0Dii3WjvW0OC+1MKduxc4BM/0/luiP5Y\nHL5WeuZn+uDiUY/eq43xbZKkU2dPqw0BkbP+OzAZO23GErSxNG26joKxxNOUpVaX/Ge9L8y3U45W\nNJv6x86c3lIbGmusrow0WvPn6yTUtq2uWq0uhfN5tqHJhv/enj451T13Te7zIQKudpSxAQAAAOgl\nc0klHwBwP1heife0C5Xxjr6ddRkf093Q3z5s27/ufC1kF/+2Mxe63zM/ot//yBomOJePEI8P5iV3\n5dxWi4W99XMB+d+tsglWjEkDy+dfks0tlNYplXGuFSlV1c3C3ebWlenBspCGoYRrZJyGYX0xP7Cy\nVGhtzZdvHTi4XwevPei3URSpDO3YPb717clTZ7S1Ee7Yt0r73lqTyqnmBuXH3112x98axQl8ytKl\nz273f5byXsvh8GRtlrttuLl17FTxuNN5YvL1xZK6sptLqSyVStrKYpgaF2xtbqUsSpHKD0uZVOeW\n7YPrMgQ2ZL5ms3bh3NwtbzL/WJHNQ1TslNrRfHanKGIWzXX7W2xftpBSvaiVS4e9LEqVMYsRs0+z\nNmVMy1JybhjWYVSaOLdQTE8q1ZPsObSq+M0+e2Ii1y5kv1yWojVFOoDW+W/5tjcaOe18AmXtyrtl\nF8tjU8Po9P7K+DdSIcVvilOhovBvpBw5aeCzJoev9c8/5TNHevSjfSeCjTPnVNquRfTW1O/zqfXw\nfTq1rnNnQ4avcen4rq6W2rfPZxEP7N+rMjQcWT/ryyjPnploc8u/bqkstWeP397hQ9fKhPf14Q/f\nIsm3/D54wK/LmFbj8LpTp1qdOu7f4PETuxXtAg9+lLEBuGqYdFFepu5WRkWqBXNy2fCaUNKk+TlE\nzhfAODcfvMRVGV343/msEVPWLWyH5Ux3wVuUxdw2t01c6GzXVW5hXfFifP79bH9vRjY9mk/OmK73\nBqWs8X/VF7aZC9QO7PclMYcP+HE4y8MyXeS2TauP3HK7JOn0manObfjymzixp3OumxMlm+TUZhfg\nOzFG3RxAc/OVlJq72NzR9tZreSgZOdm5XbjYW3pOWXDgukAwBgTNzGn9rC8NGgyGaX+KUtkcSDGQ\ny/bXSTGoM9alEqh4LpeFSWWHvoQvxfzb5dWOc8F4VihZxjZopquDLEqZQQzYrRTmbY194opuPl4Z\nmS5oG7bpMyqLUmWaydL/mLqtNAbIGHXzCLmsZLINY0uGJpU2Tjca7TnoA8dm2Wm85Xeom4+oULvj\nF0wpSNnpLDvfZ73tcC6uIN/39P2L8wYV3XfDloqXT0070b4DPsB76jMfL0l61ONaTcZn/KraFR0/\n6svGxpsTnQrz5ZwLY87aUlre41+/Z6nUgQP+mOzdt6JBPO+mrU6fOOlfd9qXnbWzUo+6zk9Aet11\n12k09N/hE8c3dPSuU357Pi7S1nqp9ZO+i1vrpCZ89s3MaTYmxkH/UcYGAAAAoJfI7AC4ivi7qL7x\nkL/bqaxzlnFWzsx3JPNlZeEurJnPpCzKpw8pZFNpmtvtfnC+quJC28i7Um1/1tksW1Nkj114kp8d\ntmTn7lQ14a9yU2bHJaR4BraRUejk5Gwa4PyI66/VwbVwZ731t3qPnzips2Eej/G01Tn/MrWtkxnE\nJgjhMWvSvCJ5WZ3R+fI6cZlucPelyj9//2e7PXN2CVKmKZaa5ZkWl938t7ab08hINnY6i53W2jal\nhIysXDiWRZE1j4hzKcl02Z6stHFObBLQVQHmD2vuqOcriCVmA6lc8ufJoCw1K8MOjWdpuZgoKouy\nmwNHrVJnQJn0ezwQxWCYSs5a12oUGjwUsinjMxyF87MotRznpLFGe+XLJN3SGc1CJqTJvstdFtem\n7RbbWxjO8ZnD3c7AxdflfzZZp0dJLjYhCU0U1KT5qspRoSIcv2Lk9KhP8hmW/dcsS5Ju/fAJHb37\ntN/z8VRFyK7t3SMdPOzLyY6Ebn+D1RWNjM/2GDvVMMwF1czO6ewZ/13c2mgVvqK69rAvV9uzuk/W\n+mXvuPUe3XnnxyRJJ45tamM9lKoO/Lam05HGY58RKgatXOjU6Gzpu/ABPUewA+Dqka4py65EKg8i\nslr61NFMZu6C97zDEF03eMLX/l/gH3rTXcK7rHSou5bsSofSEBtbzC0bL/jmLsbjxcYl9tv1l5yh\n7EkD2WIU1lemsqWB8xdQg1mjtWW/vYMHV3Xtw45Iktb2rOnYcT/+5u5jvkRmYzLVdBbXW8rFfyEG\nRk28yI+xZynN4h9sNq7D7FZeFMcVmS4YtJdWQuOycRnx/MiD3vtiMWAyst3FoXMqyq49dj6h7GL5\npCkLLS2FCRud1TR8Hnm3wKUQBBzYvz91aDuzvq7pNJS8FVkwk12IF+m06c5nk41kiVGUldKgk2Ik\nlav+4nhpuCITxmCN4wfbuNRVrFShMn6pijLr7ubkNM22ID+5bRHL+YxCPKXhQJqE9zoJkV5ROA3D\nG1pSqc0wUWax1E3y2k5jS/GuFbaPQi7+u7JLV3pt7yWfD+xyKQg3st2i8WbLwErxpsFSmz6P5TWp\nNf7788EP+QBnNh5oOXy2j3jkXu0JE9WurRUy4X1uhjK28XSi2aYPatQ02mp9meR4s9FkK36/Birk\nz6UT53zXw4+d3dTZsz4C2jjXarwZ21937zNUr8raUrYJ44bsYO7vSMZt46GAMjYAAAAAvURmB8BV\nY6kIg+DlNLWxq5Xpys9MNjo7m/Miyu/4L85tIoVuaBessdqprVU+f02+gqwDWNytYudNGFOcvxGC\nc92NbN+Kwf9utmeBfCYhHoBCMv6ur20VawBVhuOwb1jqpmt9mc3aNWvanPi7yB+58x4dP+1/DwVN\nassy/e6KUm3TpXG6zyD/GedtsSkLlk+bulPmysnNZXTyjM/FykuVduy0tsvN6t3u+OfZHJtm+gzP\nzS+ZGjSYpu066bk2raNIB6BUWcbPcKBRKE+aTafptDp0yJck7dt3UGfDRKxaz7a52/5mDQ66t2qy\nWsL4floNhmEfhs53DpOkstVSKGecho5ybdPka0qrGJSlYq84a61msUwtlDVe97CDevRjHyVJuvba\ng3rcjddKkg4fPqATZ/17eu8H/1GSdNutd+ro7T77MV7fVNP482+oJS2HlFAsFZtM267DnstLVbPP\nd/G7r/nSSGsXM33zJ0aRZYzySUON6SbaNQN/XEYrXQeH5RXp0CFfZrv/GqMDfs5QLe3x29uzvKb9\na3v8ezNjtSFbY5uppmFOnTZkWjbONNo44T+D2VarWZiyaWtTahu/D9NJq8mWP1bT8PxkUqZmGFIh\nkyZGalOWrCjj97dVmCpK1i2pyT7rYmGCXaCPyOwAAAAA6CUyOwCuGp/6OD9g+YMfOyEXBs42KlWG\nfEHbtmm+idTq9zzjNBbHX5xvDp6512VtrbfLWvzuJJuefnHXdtrn7i71/KiLbVvNx/w4qTX+OFgr\nKYwHKaxTGXoK7xn55296+CEd2usHKt92/JiOn/G3lrdmrcahtt+FcShtO99oIM/TFHF7+Y53k9Kc\n1+JntOO7zBo/XGiOnPM9vm0FF/2i+UXyYTE7rbpp2rnHUsvl7lFNJv6xwaDUUrjbvjwaqRzEAf/e\nqZPHdeaMH79i21axu7OV0jia1Gq7Gx4lYyXn4udSpKxTOYqfm9VgOf7WyobxNjJGZRkGyIdBNO2s\ny6qYgdJGbN7F27YKQ1H0sOv82K9v//Zv0s3P+kxJ0treka4JA/CXl4eahTTFHXfdIUk6fs8pve0v\n3yVJ+oPf/XPddsuxcKQaDQq/PyvLS2FTY83C3wGt7dqc+2Mw351ibgzX+cZtLXyWxrk0Nic/nctS\nGobjsrTPv+F9Bwqt+UScjhxZ0vXXX+OfX7FyxmerVPjPcFCclrUhg9XOVIQstZ0VGp/1+3c6ZHNO\nnWzUjn2jkFMnJhqP/Wcwm5rUItraQjZ8zkVIHRo3lAlzGpmilW38+sqi0aCM513IkrlWpoj70KgI\ng76KMjTSAHqOYAfAVeNHvv9rJEn/7ZWv1evf9lFJUjubqk0doPIxy12jgWi3Tkz5heuOja4ueA28\nUMx0vpy47YIcJ3feeXbONzb4vNflRammDXMSGam04SJ21uqa/f5i81Mef5MkaVQ4ffTjt0qS7jy3\npXEc81wYtWV3XP16lSaANGrToPsdu1tlQV2hfH6anXf5wuOgs+YSi63W7q1Lfd29MD/Ie6clrLrr\nyFZtqBwaFoVG4Z/e9ZnvkDWdzNQ0Xd1c6g1QmK4hQgzyrZWLc1BZ3wHNb66b1ybOUVUMpUEon7Nl\n103MaJq+N9aFK2pjU3WhGzi5Igs847KN02ooN/vi5zxTkvT1X/0cPey6/eEdb6VuiW27pUHrB9M/\n9oAv6XrM2j7dELosbnziLv3ZxlslSUdPzzSdjsN++J0YDstuTqfFFn9ZY4gkD3y2P6zCFLLtwklq\njEz4vSykMpT8DUql8r9B+M4OCqPrr90rSXrUjYc0HPrXTcanNQ21acUgNjNoNRz4MsHlcr/amf9O\nHj+6pTMxyLnHl6VNtoxcuJGwsW60sZG/zRDYFGU6D1yMY5tWZfx+upnKMGHsYGDmJnmVpIF1qSS1\nKKdpniubrw/oMcrYAAAAAPQSmR0AV429Q9/C9d+88Mu0tP+vJEl/9Bfv1DS0YB0tjdIdSpcNLI52\nyx6k7Iq5d3d4dsvgnG8+F1NkQ+2tmVtJam0b7uQ6M5/52X1+kMWqsVIq/d3iwk1VhhKW/aulPu1x\nN0iSDhzwz3+g/rCOh7lzNlqpiQO4TSlbZDU8kgo3Sy2tje1ac+fz6BRZjZ7N0mppXPguVUQXSraY\nrPnE+Y7DxXgAEjsL50A+x0+XEYnlVnkpWONamVkYbB/XVfg78vHVKaNWdgulMrlCKk0sZXKaTcP2\nCmXdEXwaabBUpkrEpaWB9uzxWZVH3PBwTcJA+fef+njYgJMJg/KbotUo7I+xSunTPXtKffI/eYQk\n6cu/5PMkSQf3D7R+/BZJ0tbmKR0P7ZBP3HFUm3fcKUkqN3zWxkyc7j6+LknauPUO7Q/n35lBqdk0\nHqswl01RamnJ7+9s1qqN8xfZrPlI9jnH479YIhofN8aoXDhJfTODWPLVZUKGwzLNoTTZ9M+fsVMd\n3xOO5cBpOPDv07ZjLYfW7suhXHTgpHK2Jkk6eXepOz7qy9uO3zWRm4XLrpnP/MzGW5oMt9JnkOau\nMuraXhdtagOfsjZDqZzFzKtNmR+5rk2+Ce2q3cylTPhoWSoHcQ4zk+b1AfqMYAfAVeNd7/obSdLT\nPueL9cJv/BxJkp2d1GveEC6mtqTC+JKZ1vgLhMI4NTaU4qidK1uJtVVlLHspyqzKymXjIPIr9K7r\n0m7XzGaXoCo9H6/FumlvpGx+mTippJHS1ct8uVu3DptKbooUBDgVKsN726dWh1f8Bcv1N1yjWKnz\nrg/4MsDT6xM1zm+jHJhszhSrYrFc33bveu4tOqVyH2UBUJEvdIlz5qSLUc0Hht22LqUAYZeIa7dg\n+CKWOZ/5wCe/oO5Kq9Ln6aRpE1+n9HwZLlYH5SAdBitp1qZeeeFF/jyW/EW/bbu5m+I6Vvf4f9of\nfuOa1vb7ZQ8f3qPrrvUX4I97zKfp797jSxs/9L6Ph5cbDcNYIhWtTDan7yAEFw+/bq8+/1lPkiTt\nWfIB9vv+9k26/aO+29o9n/iE3n+bn7vpnjuOq9j0+z6chNVaq42xP8CnN1qth8fLQaHl0n+HJxth\ngtKZNBiGeYqWjew4jkmRBmGsSixvbc0s7a9rpEGszWqlODFUUUiDECS2risPiwHFYFB0Z9qk1CzO\nEVT47U5nTrfd4iPEzZONjhz2Y4v271vSLJQNboUBQLONoU6f8gHebbePdewTYQTcdKSVUZyM1R+H\n8WQkG8+HUaOl1bDr1qX9LIpSpozndDimA2kw6r7Ag2FYr5FMeN3amv+5csNerYTue3sP7tH+a/y+\n79u/TwcPHBHQd5SxAQAAAOglMjsArhofvvWUJKkxb9QTPuXRkqRvef5TVcz8Xd83v+Vund0I83GU\n/k5l46Y+hSIpn/vFSOkGfxzI27qs35mx3TwwZmEem5gdijOR36v7Ql23Nl/GlJXVzE/hEp7P2n/F\njlLZ2tJ8H0WZBrEb2+pAGED+tBv267qQ2blna6b3fvQuSdLdIXvkrDSK3QOsS3/p23yOllhWpy57\n1MqXSW3bofz4dnuZj/lOg9TT6rNOa9vk7d/y1lhhveYSatKc2/nz2n0fLq5L30WtK5Pvet5IY1tX\nMOMbD0jyTSLiYTetXLijn5c3DZb9AsurSyrW/UbOHp9oGOZV+bQnP0aS9IRPOaTW+tLQPSuFlsK6\n1k/erjtu8dlSN/GvGapQrJQbjkoVIZM0XJGuOejLs256zCFNZn59r3/dn0qSzhw9prtu9efc5umx\n7tz039XxTHJNOK+yDGLTxgYGRkWYH8ottRrGNGPjP7tzZ1u1JqQ8Vk2aP2dojdw4fL/C/k5NqzZ+\nZ+U0CN/XgTGyIf1oCpsej6PyTTnQZOYzxM7NtDLwz+8dDeRCB7Xx2jAcv2Utx200W5qF477lpOmW\n389Tx/17Xz+xoqMn/HrvOdVq2O4N+1aqsb6Mz4YMzUY50sj5TEvZtAp9DWRKqQzfVtOuqAwZKNf6\nkrihXMraHTmypMMP89tYW1vVXXcclSQ94no/v9anf/rjtBTa6K3t3a/9h/02VlYOaO/ehwvoO4Id\nAFeNU+f8P95H3/khnVi/R5L06Mc9Qs/7sk+VJLUa6PVv9uU3rfUXCKaVTCi2b2zrJw5VCDHiNW+6\nZs8CHKeuFfWu19PdRbPbNkHhzkxRdK1xte26dn7ZvJ3uLs+V4Sq3ta1sGK90zb4VPf85T5Mk3fzE\nSn/3tndIkuq//YDOhsDQrview41r5EIdXznJ1i+TlcpdPk72kkrBdl3fhdu47fCay/mOLo+8wVxs\nHxzHpzin1LnN2mlqTV0MpNGS//3gIV+CdujIfh046M/9Gx5xRB/+R/89effbP6KlZX++Xv8I3xZ5\n1o516rS/gfCJ285p0PoL9/Uzd+gjt5yRJIXYQ4Oh8S2n5ceDLIfxPQf2DrS25q/AJ1vn9O73vN+/\nbsuXd002Gm2eCefcrNRWHG9kjNTG8SChBK10OnKd3/cjR/ZrKdRsnW4bra35mTmXQpnqW//y73TX\nWR8YOFukoH95ZZQu7BWCoRPjmaYhODNbpQbjUGo5Vvput5KmcTLWVE/aqgyR2MOvHenpT/Lj3R51\n3SG1oTJ29LB/Ikl6widXcqHr2gff/259/KO1JOnMmWOahB7R01D71raNhkv+94OHpTJ8uIdX96id\n+nZr58J3cSCrWTiWw2FWyWmM2hgYapaG78TW+yvLTo9+uB9E9Cmf9kg96lH+Mx+OBnpn4dteP+Ym\nP8HrU570+NTBb0lLWtkbxuhpn0oXjiXQY5SxAQAAAOglcyl3zQAAAADgakdmBwAAAEAvEewAAAAA\n6CWCHQAAAAC9RLADAAAAoJcIdgAAAAD0EsEOAAAAgF4i2AEAAADQSwQ7AAAAAHqJYAcAAABALxHs\nAAAAAOglgh0AAAAAvUSwAwAAAKCXCHYAAAAA9BLBDgAAAIBeItgBAAAA0EsEOwAAAAB6iWAHAAAA\nQC8R7AAAAADoJYIdAAAAAL1EsAMAAACglwh2AAAAAPQSwQ4AAACAXiLYAQAAANBLBDsAAAAAeolg\nBwAAAEAvEewAAAAA6CWCHQAAAAC9RLADAAAAoJcIdgAAAAD0EsEOAAAAgF4i2AEAAADQSwQ7AAAA\nAHqJYAcAAABALxHsAAAAAOglgh0AAAAAvUSwAwAAAKCXCHYAAAAA9BLBDgAAAIBeItgBAAAA0EsE\nOwAAAAB6iWAHAAAAQC8R7AAAAADoJYIdAAAAAL1EsAMAAACglwh2AAAAAPQSwQ4AAACAXiLYAQAA\nANBLBDsAAAAAeolgBwAAAEAvEewAAAAA6CWCHQAAAAC9RLADAAAAoJcIdgAAAAD0EsEOAAAAgF4i\n2AEAAADQS4MrvQO4qrljx9av9D7gKnHkyF5JEucEIs4JLOKcQI7zAYvCOWEeyG2S2QEAAADQSwQ7\nAAAAAHqJYAcAAABALxHsAAAAAOglgh0AAAAAvUSwAwAAAKCXCHYAAAAA9BLBDgAAAIBeItgBAAAA\n0EsEOwAAAAB6iWAHAAAAQC8R7AAAAADoJYIdAAAAAL1EsAMAAACglwh2AAAAAPTS4ErvAK5ez3vR\nq670LgAAAKAHfv3Fz74i2yWzAwAAAKCXCHYAAAAA9BLBDgAAAIBeItgBAAAA0EsEOwAAAAB6iWAH\nAAAAQC8R7AAAAADoJYIdAAAAAL1EsAMAAACglwh2AAAAAPQSwQ4AAACAXiLYAQAAANBLBDsAAAAA\neolgBwAAAEAvEewAAAAA6CWCHQAAAAC9RLADAAAAoJcIdgAAAAD0EsEOAAAAgF4i2AEAAADQS1d1\nsFNV1cerqvrd8zz/kqqqXFVVyw/kfgEAAAC4+l3Vwc5F+GlJ19d1Pb7SO3I5VFX15qqqXnCl9wMA\nAADog8GV3oH7oq7rc5LOXen9uByqqhpIepqkV1zpfQEAAAD64EEV7FRVdUjSX0u6Q9JzJf2gpB+R\ntFLX9biqqjdJOi3plZL+o6RHS7pF0vfVdf1n2XpeKOnFkm6Q9PeS/o2k35L0trquX7DLtl8Stvd0\nSb8i6dMlnZT0c3VdvzQsc7OkN0r6Gkn/QdLD6rq+Njz3pZL+fXidlfQeST9a1/Vrq6q6SdLHwqZe\nUVXVK+q6NuF1XxS2+6nyn9dbJb2orusPZvv2RWHdT5bkJH1I0o/Xdf2HF3tsAQAAgL550JSxVVW1\nIumP5TM5X17X9WSXRZ8o6V9K+kZJz5C0IemVVVWthvU8R9J/k/QGSU+R9OOSXi7p2ovYjZGkX5T0\nQ/JByysl/eeqqr52Ybkfkg8+nhK2+QWS/kjSe+WDpWfKB2yvrqrqKZJul/Q54bXfJen68LrPk/Sn\nku6U9NmSni1pSdKbq6o6HJZ5TFj3P0p6Utivv5D0+1VVPfki3hMAAABwvzpyZO8V2e6DIrNTVVUh\n6X9IOiTps+u6Xj/P4o+Q9My6ro+H1/6ipN+U9Fj5LM43SToq6Vvqum4kfaCqKicfMFyMX6jr+vXh\n9x+oqurr5AOr38uWeV1d16/K/vx9kj4YtunCfv1zSV8g6dvquv7mqqqOh2XP1HV9d/j9xZI+Lun5\ndV234XXfIOlWSS+U9BPyQdOnSbq9ruuNsMyPhtd+gXwGCQAAAHjIebBkdn5e0j+V9Jy6ro9dYNmP\nxEAniMsfDD8fI+k9IdCJ/kLS7CL35S0Lf36PpJsWHnvnwp+fLumtMdCRpLqup2G5p5xnW58h6Q0x\n0AmvOyrp/fF1oTnDp0j6o6qq7qyqal1SfP+HLuYNAQAAAPenY8fOl6u4/zwYMjvPlbQmaSxpz0Us\nv9iwIAYYJvw8JOm2fIG6rqdVVZ29yP05vcP2DlxgmX2SzuywrrOSPuk829on6Zuqqvr6hceX5Y+H\nqqr6Ckl/IOl/yo8VOir/nj98nvUCAAAAvfdgCHZOy49X+Q1Jv1tV1TPuY6vpiaTV/IGqqoaSLraQ\ncK+kzYU/n7rAa05L2r/D4/u1PTDKnZLPOr1kh+fimKXny5eyfV1d11aSqqq6/gL7AwAAAPTeg6GM\n7W11Xb9P0tfLZ0H+y31c34clPbWqqjJ77Mvkmw9cjJsX/vwU+eYA5/M3kj67qqqYXVKYCPVpkt6x\nsKzJfn+7pE+u6/oj+X/yQepdYZmRpJMx0AlesMO6AAAAgIeUB0NmR5JU1/WHqqr6Dkm/XlXVa+9D\nW+Xfl/TPJP1UVVW/LOnxkn5AO5eZ7eS7qqo6Jd844JvlGyL8xgVe85Py3d9eVlXVz8qXob0k/Py5\nsEzMDt1cVdW75dtHv1TSm6qqepmkX5a0JekrJf2/4ecfSXqbpC8OHeH+VtJXyY/1uV3SU6qqur6u\n6xgYAQAAAA8ZD4bMTlLX9Svku579WlVVN17iOn5H0g9L+gZJfyfpe+VbVc8UxsFcwHfLByp/L19C\nNjeHzy7bfLN89uip8g0N3io/DulZdV3/Y1jmqKSXyY+7eYOkw3Vdv1XSF8l3W3u7pPfJBzNfW9d1\n7B73s5J+Wz4Yepf8fDz/XNJ/lfRZ4TkAAADgIcc45y68VI+EUrLrJN2dtYE+KD9B6PfXdf1Tu7zu\nJcomMH2AdveKet6LXvXQOjkAAABwv/j1Fz87zrXzgA6zeNCUsV1Gny/ptfKTgf6afIe3/yjfVe13\nruQcvehiAAAgAElEQVSOAQAAALh8HlRlbJdDXdevky8/+0JJ75UvGVuT9AV1XX/iSu4bAAAAgMvn\nIVfGhotHGRsAAAAuhytVxvaQy+wAAAAAeGgg2AEAAADQSwQ7AAAAAHqJYAcAAABALxHsAAAAAOgl\ngh0AAAAAvUSwAwAAAKCXCHYAAAAA9BLBDgAAAIBeItgBAAAA0EsEOwAAAAB6iWAHAAAAQC8R7AAA\nAADoJYIdAAAAAL1EsAMAAACglwh2AAAAAPSScc5d6X3A1csdO7Z+pfcBV4kjR/ZKkjgnEHFOYBHn\nBHKcD1gUzgnzQG6TzA4AAACAXiLYAQAAANBLBDsAAAAAeolgBwAAAEAvEewAAAAA6CWCHQAAAAC9\nRLADAAAAoJcIdgAAAAD0EsEOAAAAgF4i2AEAAADQSwQ7AAAAAHqJYAcAAABALxHsYFfPe9GrrvQu\nAAAAAJeMYAcAAABALxHsAAAAAOglgh0AAAAAvUSwAwAAAKCXCHYAAAAA9BLBDgAAAIBeItgBAAAA\n0EsEOwAAAAB6iWAHAAAAQC8R7AAAAADoJYIdAAAAAL1EsAMAAACglwh2AAAAAPQSwQ4AAACAXiLY\nAQAAANBLBDsAAAAAeolgBwAAAEAvEewAAAAA6CWCHQAAAAC9RLADAAAAoJcGV3oHLpeqqt4kabmu\n62c+ANtakfQ/JT1L0pslfZukj0n61rquf/n+3j4AAACAC+tNsCPpKx/AbT1P0pdI+teS/o+k1Qdw\n2wAAAAAuQm/K2Oq6PlnX9ckHaHMHw8/X1HV9zwO0TQAAAAD3wn3K7FRV9XFJr5H0D5K+V9K1kt4n\n6dvqun5Httx3SXqhpMdKWpf0TknfV9f134fnjaQflPQCSY+UdE7SWyR9b13XHw3LfIWkfyepCqt9\nn6Qfqev6deH5NykrY6uqaknSSyR9vaQbJJ2S9GeSvj8GKFVV/YakJ0n6Lkk/I+kJku6U9GN1Xf/m\nLu/5NyR9U/jjx6qqenPY78XlniDppZI+V9KKpFsk/VJd1z9fVdUeSSclfU9d178Yll+VdFrSe+u6\nfnq2nt+UVNV1/cyqqkaS/r2kr5L0SZKOS/rtcBwm2XE4HY7Pd0n6wbquf2Gn9wIAAAD02eXI7DxX\n0jPCz8+VVEr606qq1iSpqqp/Iem/SPoF+WDnWZJaSa8OY18k6V/JBzLfJx/MfLGkA5L+JKzj8ZJ+\nX9L/kg9OniHpXWEdj9xlv35VfizNf5APYl4Qtv3qEFxFRyT9iKTvCOv+oKRfPc96v1PSD4Tfn6Ed\nyueqqrpW0l9KOhTeyxMl/Zakn62q6t/Wdb0h6a/D8Yo+R9JRSU+qqmpv9vizJP15+P1l8sfov4Z1\nvkjS/yPplxZ24VMlPU7S08J2AQAAgIecyzFmZ03SN9d1PZakqqq+V9LrJT1H0h9K+iNJn1rX9T+E\n5W+rqurn5LMsT5T0DklPlXRbXdevypb5Gkk3VlVVyAchA0mvqOv6aNjOd0v6HfmMzZyqqh4u6fmS\nXlzX9X8PD99SVdX3SPoDSZ8l6a3h8RskfWHcv6qqfkrSl0p6sqTbF9dd1/WZqqrOhj8eq+v6ZFVV\n+xYW+78lXSPpq+u6vis89p+qqvpMSf9W0s/JZ8S+I3vNsyX9haSb5QOfV1dV9Vj5TNefV1V1g6R/\nKenH67r+1ew93SDp/6uq6ofrur4jPP5ISU+r63rbsbm3jhzZe+GF8JDCOYFFnBNYxDmBHOcDrqTL\nEey8IwY6wbvDz5vCzw1Jzw3lXzfKl3SV4blD4eefSHphVVWvk89EvKGu69vly7RUVdVfSzom6c1V\nVf2KpNeFEri37bJPT5Vk5Evhcn8dfj5FXbCzkQViCtuRunE5l+Lpkj6SBTr59r80BEevkfQTVVU9\nrq7rD8sHOz8vn237PEmvls/qnJQPCL8kPPeahXW+Xv69PllSDHY+ejkCHQAAAODB7HIEO6cX/nwu\n/DwQfv60fAbjxyS9StJZSZ8h6ZXxBXVdv7qqqpvlx5j8rKT9VVX9jaQX1XX9V3Vdf6KqqmfIl3B9\nt6SfqarqVvmxNS/fYZ9ipuXMwuMxI5PfYji3sIwLP40u3b4dtr24/XfLB1afW1XVPfLBypvlA8F/\nHZZ7tnxg12bZo9dWVWWzdcb9vD57bPEzuWTHjq1frlXhQS7emeOcQMQ5gUWcE8hxPmDRlcjyXY4x\nO4t7Hf8cMwvPl/R7dV3/SF3X767r+iOS7MJrVNf1W+u6/mpJh+UzGmNJf1ZV1YHw/Mfruv72uq4f\nKV/+9ueSfq2qqmfvsE/xYn//wuP7F56/v5zeYdv59s/Ude0kvU5+3M7Nkm6v6/pW+bE+Tw3jdm6W\nL22TuuP5jfJlffG/T5cfn/P7l/1dAAAAAA9ilyPY+Yys0YDkS8gk6R/Dz5G60rDoBeGnkaSqqr6w\nqqpPlqS6rpu6rt8kn8HZK+mTqqp6Uh7U1HX9/rquv0U+U/KkHfbpnfIB1ecuPP7Z4ec7dP/6G0mP\nCeNpFrf/wbquYzbpNfLjcz5f0pskqa7rWyTdIz8+5zp1wc475Rs7PKqu64/E/yTdJamt63qnTBIA\nAADwkHU5ytjGkl5eVdVPyE+u+VPy7ZtfG55/m6SvrKrqt+WDkx+Q9NHw3GdVVfU2+Qv7J1dV9R3y\nQdI+Sd8jf9H/QflWzz8dnn9zeO1XyDdHiGNvkrqu7w5jhH6wqqrb5IOPJ8p3hXtjXdd/exne9/m8\nQr4V9++Fpghn5DMyz1XXtlrywc4rJP0zST+cPf4W+ff/D7HpQF3XR6uqermkl1RVdUr+fR+Rb6/9\nxKqqqrquN+/XdwUAAAA8iFyOYOcvJf2tpD+Vz0T8vaQvq+u6Cc9/m6Rfk/QG+cH2vyTpP8lfqH+f\npEa+ffJLJb1cfq6e05LeLuk5dV1vSfrlMDfN90v6RUlTSR+Q9H+dJ3D5VvmM0kvlx7Mck/S/5efz\nuV/VdX08jEH6KflStWX5IO5f1HX9W9lyd1ZV9Q/ygdibslW8RdLXyo93yn27fCD5EvmOa+vymZ/P\nJdABAAAA5hnn3IWX2kWYVPTtdV1/3eXaIVw9nveiV7lff/FOQ6LwUMRAUyzinMAizgnkOB+wKJwT\n96UJ2L12OcbsAAAAAMBVh2AHAAAAQC/dpzE7dV3fdJn2AwAAAAAuKzI7AAAAAHqJYAcAAABALxHs\nAAAAAOglgh0AAAAAvUSwAwAAAKCXCHYAAAAA9BLBDgAAAIBeItgBAAAA0EsEOwAAAAB6iWAHAAAA\nQC8R7AAAAADoJYIdAAAAAL1EsAMAAACglwh2AAAAAPQSwQ4AAACAXiLYAQAAANBLBDvY1R//zJdf\n6V0AAAAALhnBDgAAAIBeItgBAAAA0EsEOwAAAAB6iWAHAAAAQC8R7AAAAADoJYIdAAAAAL1EsAMA\nAACglwh2AAAAAPQSwQ4AAACAXiLYAQAAANBLBDsAAAAAeolgBwAAAEAvEewAAAAA6CWCHezqeS96\n1ZXeBQAAAOCSEewAAAAA6CWCHQAAAAC9RLADAAAAoJcIdgAAAAD0EsEOAAAAgF4i2AEAAADQSwQ7\nAAAAAHqJYAcAAABALxHsAAAAAOglgh0AAAAAvUSwAwAAAKCXCHYAAAAA9BLBDgAAAIBeItgBAAAA\n0EsEOwAAAAB6iWAHAAAAQC8R7AAAAADoJYIdAAAAAL1EsAMAAACglwh2AAAAAPQSwc4Oqqq6qaoq\nV1XVt5xnGVdV1QvuwzZWqqr6k6qqNqqqevWlrgcAAADAzgh2LlJVVbdWVXXzZVzl8yR9iaTvlvSC\ny7heSffL/gIAAAAPKgQ7F6GqqodLetRlXu3B8PM1dV3fczlXfD/tLwAAAPCgMrjSO3C5VVVVyGdL\nvkXSIyTdKulldV3/XLbMP5X045KeIX8MPijpJ+u6/t0d1nezpDeGP76xqqpb67q+aWGZGyX9pKTP\nk3RA0h2SfkvSj9V13e6wzt+Q9E3hjx+rqurNdV3fXFXVfkn/WdKXSzos6aikP5T07+q63she/wJJ\n3yPp8ZImkt4elnnXxewvAAAA8FDQu2BH0osl/ZCkb5X0V/IByK9UVdXUdf2yqqo+WdIbJL0mPLcl\nHxj9TlVVW3Vdv2phfX8t6Rsk/Q9JXyXpLZJU17XJlnmlpJmk50o6IekzJP1qWPdLd9jH75T0gfDc\nMyTdEh7/Y0mPkfRtkv5e0tMk/bKkR0r6SkmqqupfSXq5pB8L+7QmH2i9Mby3Hff3Uh05sve+vBw9\nxDmBRZwTWMQ5gRznA66kXgU7VVWNJL1I0q/Wdf3fw8O3hLKu/eHP3ykfhHxtXdfj+FhVVc+W9B2S\n5oKduq6nVVWdCn88Wdf1sR02/VRJP1rX9XvCn2+rqur9kjZ2WFZ1XZ+pqups+OOxuq5PhmzT54T9\n+j/huY9WVfVIST9dVdUj67q+XdIP6P9v787DJKvKO45/J6wugCKgaFTE4ItEEVEYg5FlMC5RgopE\nVNQBN5BFVBYNQVAHRaIoQlwBRyeKgCK4IKCyKosgQkScN4KiuJEBHPYt2vnjnMKi6B56uqp6Of39\nPE8/t+uubw2H2/dX595T8J3MfG/X+341pTdpfmYuGEe9kiRJUvOaCjuUXpE1gYu7Z2bmB7pebgZc\n0hV0Oi4AdpzgcU8FDomIxwKnAedl5lXLuY/N6rS3J+aCOn1mRNxMuXVtYfcKmXl9RFwDbLqcx3xQ\nS5bcOuhdaobqfDJnm1CHbUK9bBPqZntQr6no5WttgIJH1Omy/q9aHbh5lPm3ABP9L/B6YH/K7Wun\nATdGxLH1GZzxWr1Oe2vr9ACttox1OuvZTyxJkiRVrfXsdEY1e+Qy1lnKX29p67ZGXbbcMvNe4Cjg\nqIhYk/J8zeHASpQgNB6dY68B3NFTV2f5zT3z6Fnv2vFXLUmSJLWttZ6d31ACwVbdMyPi/RFxXH15\nMbBZRKzatXwO8FzgkgfZ/5zeGRGxZkTsHBErAGTmTZl5DGU0tk2Wo/bOrXdb9sz/R+AvwGWZeStl\nYIP7rRMR6wLrj1L/A+qVJEmSZoumenYy896IOAI4KCIuBL5HCT4HAPvW1T4B7AJ8OSIOBv5MGbRg\nQ2DPMXbdeeD/BRGxFLg8M0fqvDmUEdPmRcSRwE1AAP8CfGs5ar8kIs4CPhoRt1GGw96CMrLcFzLz\nD3XVw4AvRsR7geOBRwEfoYwC1wl0y6pXkiRJmhVa69mBMiTzQZSQkHW6X2YeBZCZCWxLudXtIuDH\nwMbASzPz7FH3WHpMTqV8t83pdP27ZeaNwPMpw0OfA/yCEn5OAvZbztpfTglIx9baF1Buj3tr1/EW\nUcLajsCVtZ4bgC0z84YHq1eSJEmaLeaMjPiBv0a33btOHTnu3fOmugxNE46qo162CfWyTaib7UG9\napuY1Mcs/MRfkiRJUpMMO5IkSZKaZNiRJEmS1CTDjiRJkqQmGXYkSZIkNcmwI0mSJKlJhh1JkiRJ\nTTLsSJIkSWqSYUeSJElSkww7kiRJkppk2JEkSZLUJMOOJEmSpCYZdiRJkiQ1ybAjSZIkqUmGHUmS\nJElNMuxIkiRJapJhR5IkSVKTDDuSJEmSmmTYkSRJktQkw47G9M2Pbj/VJUiSJEkTZtiRJEmS1CTD\njiRJkqQmGXYkSZIkNcmwI0mSJKlJhh1JkiRJTTLsSJIkSWqSYUeSJElSkww7kiRJkppk2JEkSZLU\nJMOOJEmSpCYZdiRJkiQ1ybAjSZIkqUmGHUmSJElNMuxIkiRJapJhR5IkSVKTDDuSJEmSmmTYkSRJ\nktQkw44kSZKkJhl2JEmSJDXJsCNJkiSpSYYdSZIkSU0y7EiSJElqkmFHkiRJUpMMO5IkSZKaZNiR\nJEmS1CTDjiRJkqQmGXYkSZIkNcmwI0mSJKlJhh1JkiRJTTLsSJIkSWqSYUeSJElSkww7kiRJkppk\n2JEkSZLUJMPONBUR8yNiJCI2rK8XRsQfu5ZfGxFfmboKJUmSpOltxakuYKaKiFWAW4DIzGsn4ZBv\nB1aehONIkiRJTTDsTNyzmcTwkZk3T9axJEmSpBbMirATEb8Fjs/M/XrmrZyZ63TNex/wFuApwKHA\nDsA6wPXAGcD+mXljRMwHPl83+1VEnJuZW0fEHEoPzHxgA+A24BTggMxcWo+xENgE+DTwQeC4zNx3\nHO9hIfCizHxMz/y1gN8Ch2XmIT3LTgcemZlzH2z/kiRJUmtmyzM73wW27LyIiADWAFaKiI261tuG\nEmqOBF5LCS3rA6+pyz5b1zsBOKD+vjnwivr7gcARwFeAjYE3AC8ETu6pZy3gZcBWlMAzYZl5A/A1\n4PU1bHXe41rAtsAx/exfkiRJmqlmRc8OcCawc0Q8LDNvB+YBF9dlWwNXRcRDgbnAJ4FzgQWZ+cu6\nznURcSKwZ0TMycw7I+KWumxJZt4UESsB+wGLMvOwuuyaiNgH+HpEbJGZF9T5j6P00lw5oPf3aUog\n2xo4u857JXA3JXhN2Nprr9ZXYWqPbUK9bBPqZZtQN9uDptJs6tlZAdiivp4HnFd/tqrznksJf98F\n/gLsFRGLI2JpRNwGvBN4GLDKGMd4KrA6JVh1O6tON+2ad9cAgw6ZeT7wM0pPVMdOwAmZeeugjiNJ\nkiTNJLOiZyczb4iIy4AtI+J7lB6Qoylhb/e62jzgUuAm4CfAEygB51LgLmBvYK9lHGb1Oj0mIj4z\nyvJ1u34fxmADnwEOi4g9gdWA5wHv6XenS5aYlVR0PpmzTajDNqFetgl1sz2o11T08s2KsFOdSXlu\nZ2Pg4cBFlLDzqPpdNp3ndZ4GPAPYLTMXdjauQ00vy5/qdH/gO6MsX9pP8eOwCDgM2B54FPDzzLxw\nyMeUJEmSpq3ZFnbeAfwzcFFm3g0QEZcCLwGeRenJ6QwnvaSzYUSszl8HIbhvEICe10kJNOtn5tVd\n264APKUOJDA0mbk0Ik4AXk0ZAMGBCSRJkjSrzZZndgAuAP5MuW3tnK7551FuT7uNMmjBYkovzR4R\n8ZSIeA5wOmUIaYBt6mAGnZ6cl0TE0zPz/4DDgd0jYu+I2CAiNqEMUX1xRDxuuG8PKAMVvIDSM7Vo\nEo4nSZIkTVuzJuxk5j2Ukcoez/3DzvnAE4HvZeaf62htrwEeC1wBfI5ye9h7gKuAr1IGM/g28EPK\nUNML6zE+BOxLCVQ/owSpNYEtM/N3Q32D5fg/onznzsmZeeOwjydJkiRNZ3NGRkamugYNSEQ8C7gE\n2DwzLx3ALkd8qFAdPmiqXrYJ9bJNqJvtQb1qm+h9JGSoZtMzO82qXyC6EXAccPyAgo4kSZI0o82a\n29gadzzwTcptdbtNcS2SJEnStGDPTgMy85+mugZJkiRpurFnR5IkSVKTDDuSJEmSmmTYkSRJktQk\nw44kSZKkJhl2JEmSJDXJsCNJkiSpSYYdSZIkSU0y7EiSJElqkmFHkiRJUpMMO5IkSZKaZNiRJEmS\n1CTDjiRJkqQmGXYkSZIkNcmwI0mSJKlJhh1JkiRJTTLsSJIkSWqSYUeSJElSkww7kiRJkppk2JEk\nSZLUJMOOJEmSpCYZdiRJkiQ1ybAjSZIkqUmGHUmSJElNMuxIkiRJapJhR5IkSVKTDDuSJEmSmmTY\nkSRJktQkw44kSZKkJhl2JEmSJDXJsCNJkiSpSYYdSZIkSU0y7EiSJElqkmFHkiRJUpMMO5IkSZKa\nZNiRJEmS1CTDjiRJkqQmGXYkSZIkNcmwI0mSJKlJhh1JkiRJTTLsSJIkSWqSYUeSJElSkww7kiRJ\nkppk2JEkSZLUJMOOJEmSpCYZdiRJkiQ1ybAjSZIkqUmGHUmSJElNMuxIkiRJapJhR5IkSVKTDDuS\nJEmSmmTYkSRJktQkw844RMTWETESES8a5/rr1fXnD+DYh9R9rdrvviRJkqTZxLAziojYJSLO6Zp1\nAbAucNbUVCRJkiRpea041QVMU1t0v8jMe4A/TlEtkiRJkiag6bATEQ8HPgjsAKwDXA+cAeyfmTdG\nxCHAPsB84FPAD4G1gK3q9iPALsC1wNnAizPz9LpsLvBhYDPgFuC7db/doWiFiDgMeCOwGnAOsGtm\n/r7uYw6wL/Am4EnAbfU4B2Tm1ct4Xx8F3gxsk5k/jojVgUOBFwJPAH4HfBY4PDNHlv9fTpIkSZr5\nmg47wCeA7YGdgMWUQLGQEgR2qOusCOwFbAf8GvgzJbjcDbwCuBmY273TiNgA+D5wArAn8HDgGOAb\nwOZdq+4DLKL0FD0d+BIlIL2uLn8fcACwP3Aa8BjgaOCsiNgoM2/rfUMRsQ+wB/DPmfnjOvtkYBPg\nbcCPgW2BI4FVgPeP619qDGuvvVo/m6tBtgn1sk2ol21C3WwPmkqth50DgQWZ+cv6+rqIOBHYs/aq\nADwM+FhmXtrZKCLuBe7p9NJERO9+9wbuBN6amf9X19kdeHNErNO13uLMPLz+/ouIOJsanCJiZUoY\nOjYzj+xaZ1fgUuBlwH91HzQiXgkcDuyUmWfVeXMp4WbXzDyxrnpNRGwE7BsRH87Mu8f7DyZJkiS1\novWw8xdgr4h4MaXXZEVgZWAlSq9Hx6WjbLssmwOXdYIOQGaeD5wPZTS2OvvCnu2WAM+sv29IubXt\n/J51fgLcBWzK/cPO8+rrvTLz5K75nV6nM3v2833g7cAGwJXjeVOjWbLk1oluqsZ0PpmzTajDNqFe\ntgl1sz2o11T08jUbdmrPzRmUZ1jeSQk0d1F6ZfbqWX3pcu7+EcB141jvjp7XI0CnR2n1Or25e4XM\n/EtE3EYJQt2+Sglp6/bM7+wne3qgOiPtrUsfYUeSJEmaqZoNO8DTgGcAu2Xmws7MiFhlzC3G73+B\nR/a5j07AWqN7ZkT8DSXo9AawAyi33H04Is7KzPPq/D/V6TzgplGO84c+65QkSZJmpJa/Z2flOl3S\nmVFHLXtFfTnnAVvc37KW/xTYLCIe0rXv50TEDyLiyeOsLym9Olv2zH825Ra7S3rmLwSOoNye9uWI\neFSdf1GdrpuZV3d+KCHo9sy8fZz1SJIkSU1puWdnMeWCf4+IuBJYkxIWTqEM9bwN8NAxtv0TsElE\nPJsyXHWvT1CGq/5iRBxIua3tPynPCP0SeOKDFZeZ99YhpA+KiJ8CpwPrUUZjWwycOso2IxHxBuAK\n4Dhg+zr09BnA0fXWvcvr8T8CzImIzRx+WpIkSbNRsz07tUfjNcBjKeHgc8BhwHuAqyjPwDx3jM2P\noPTs/ADYcZR9LwaeTxn04HLKkNNXAS9dzmCxgDJi3DsoPT0nUIaOnjfWCGp1hLj5wHYRsXedvQPw\nNUpQuho4iTLQwYsMOpIkSZqt5oyMeC2sMY04goo6HFVHvWwT6mWbUDfbg3rVNvFgj5IMVLM9O5Ik\nSZJmN8OOJEmSpCYZdiRJkiQ1ybAjSZIkqUmGHUmSJElNMuxIkiRJapJhR5IkSVKTDDuSJEmSmmTY\nkSRJktQkw44kSZKkJhl2JEmSJDXJsCNJkiSpSYYdSZIkSU0y7EiSJElqkmFHkiRJUpMMO5IkSZKa\nZNiRJEmS1CTDjiRJkqQmGXYkSZIkNcmwI0mSJKlJhh1JkiRJTTLsSJIkSWqSYUeSJElSkww7kiRJ\nkppk2JEkSZLUJMOOJEmSpCYZdiRJkiQ1ybAjSZIkqUmGHUmSJElNMuxIkiRJapJhR5IkSVKTDDuS\nJEmSmmTYkSRJktQkw44kSZKkJhl2JEmSJDVpzsjIyFTXIEmSJEkDZ8+OJEmSpCYZdiRJkiQ1ybAj\nSZIkqUmGHUmSJElNMuxIkiRJapJhR5IkSVKTDDuSJEmSmmTYkSRJktQkw44kSZKkJhl2JEmSJDXJ\nsCNJkiSpSYYdSZIkSU0y7EiSJElqkmFHkiRJUpNWnOoCNLkiYk3gYOBlwLrADcBpwEGZ+YdxbL8F\ncBDwHOAhwP8AnwOOzsyRYdWt4RlAm/jHuv3mwKrAdcDXgA9k5m3DqlvD02+b6NnXqsAVwFOAbTLz\nnMFWq2EbwDliFeDdwM7A4+v23wYOzMwbhlW3hmcAbWJnYDfgGcDKwG+AbwELMvPGYdWt4YqIlYEF\nwL7AeZm59XJsO7TrS3t2ZpGIeAhwDrA75WJ0PvAZ4FXADyPikQ+y/TzgbGAD4BDgzZTG+AngY0Mq\nW0M0gDbxWuB8ygXMwXU//w3sD5wZEZ5jZph+28QoDqIEHc1AAzhHrEgJNv9OuZh9E/BV4I3AufXi\nSDPIANrEB4FFwErAv1FCzznAXsBFEbH6kErXEEVEABdS2sWc5dx2qNeX9uzMLvsATwf2yMxPdmZG\nxBXA1ykXJe9cxvafBO4Cntf1yc2iiDgF2DsiPp+ZVwyndA3JhNtE/bT2U5SenLmZeXNddFxEfJ3y\nid+LKJ/2aebo9zxxn4h4OrAf8BPgmYMvVZOg3/awG7At8IbM/GKd918RcQOwKzCX8oGJZo5+/m6s\nSTknXAtsmZl310Wfr23i3cAuwJFDq14DVwPuZcAvgGcDi5dzF0O9vvRT19nl9cDtwLE9808Ffgvs\nHBGjpvGImAsEcOIoXdRHU1L8zoMtV5Ngwm0CeAxwMvChrqDT0Qk4Gw+qUE2aftrEfWqv3ueAX1M+\n9dXM1G972INyAbSoe2ZmLsjM9TPToDPz9NMmnkD5oP1HXUGn47w6XW9AdWryrAx8EXhOZubybDgZ\n15eGnVmidgtvCFzWe4Kp90L+CFgbeNIYu9i8Ti8cZdnFdTp3AKVqkvTbJjLz15k5PzM/NcriNer0\nlgGWrCEbwHmi256Uc8JuQO9FjWaAfttDRPxt3f7Mzj33EbHqeMKypqcBnCN+RTkfbDDKsvXq9MqB\nFKtJk5nXZ+bumXnXBDYf+vWlYWf2eGKd/naM5b+p0/XHWL7eWNtn5q3A0mVsq+mp3zYxqnoP/q7A\nHcApEytNU2QgbSIiHg8cCizKzO8PqDZNvn7bw4Z1ek1EvD0irgXuBO6MiFMi4u8GUqUmU19togcK\nkAwAAASaSURBVN4F8AHgmRFxVEQ8OSLWiYiXAgcClwNfGmTBmvbWq9OhXV8admaP1er0jjGW396z\n3kS2H2tbTU/9tokH6Lp16amUUXl+P/HyNAUG1SY+BdwDvGsQRWnK9Nse1qzTNwBvpQTg7Sm3NW5H\neZh93QHUqcnT9zkiMw+lPID+JuBq4Hrgm5Rn++ZNsHdAM9fQry8doEDSQNQRer5MGZjgPzPziCku\nSVMgInYCXgLsmplLproeTanOSGuPBp7WNaTwNyLiekr4eRdlmFrNEhGxO2WUrTOB44EllNuU9gdO\ni4gXZ+bSKSxRjTHszB6dZyceNsbyh/esN5HtfT5jZum3TdwnItYGvkEZH/8Dmfne/svTFOirTdSR\nlo4Ezs3Mzw+4Nk2+fs8Rne/Z+sYo351yLCXsbD3h6jQV+j1HBCXofD8zX9K16Iw6mtsplOGo9x9A\nrZoZhn59adiZPX4FjAB/O8byzn24vxhj+S/r9AHbR8QalAfSL+unQE26ftsEABHxaMrQsU8CdsnM\nhYMqUJOu3zbxH8AjgEPqw+kdne/dWLvOXzLKSEyafvptD9fW6QqjLLuh7tvvVJlZ+m0T8yjXnieP\nsuw7dd/b9FOgZpyhX1/6zM4skZm3U77scdP6jeb3iYgVgC2A6zLzN6NtD1xQp88dZdnz6vQHg6hV\nk2MAbaIzMs/plOFE/8WgM7MNoE1sS7l16WzK9y91fjq3NJ5YX//D4KvXoA2gPVwF3AxsMsqyx1OG\nlB3rQXdNQwNoE51P71cdZdkqlDYx2jK1a+jXl4ad2eVY4KGUB0W77QysAxzTmRERG0bEfUNHZubl\nlGS9Y/cntnUI0XcA9wJfGF7pGpIJt4nqSMqFzKsz8zvDLFSTpp82sSvlwfPen4/X5f9WX/90KJVr\nGPr5u3EP5Tm+Z0XEdj3b71mn3xx4xRq2fs4RnQvbV40yBPmOPeuoQVNxfeltbLPLp4HXAh+JiCcC\nlwJ/T/mm458CH+la9+dA8tehQwHeRvnE9ryI+DhlOMCdKN3SB2XmNUN/Bxq0CbeJiNiYMsrSVcAK\nEfHKUfa/JDPPHV75GoIJt4nMPGu0HUbEWvXXCzPznOGUrSHp9+/GwcALgZMi4jDKrW3zgNdRhhn+\n9JDr1+D1c464ICJOogSbH0TEiZQBCjajfAFtZ+AKzSARsRGwUc/stXuuC07LzDuYgutLe3Zmkcy8\nF3gBcBSwA7CQcrF6DLB1bYTL2v5iYEtgMfB+yvChj6GMurRgeJVrWPpsE5tSbjnYCDhpjJ/3Dat2\nDUe/5wm1ZQB/N5ZQBi75AvAW4LPAVpRbG7fOzDuHVryGYgDniFcDe1FuWzu0bv9y4DjgWcu6dVrT\n1r9y/7/98MBrg3XG2njY15dzRkZG+t2HJEmSJE079uxIkiRJapJhR5IkSVKTDDuSJEmSmmTYkSRJ\nktQkw44kSZKkJhl2JEmSJDXJsCNJkiSpSYYdSZIkSU0y7EiSJElqkmFHkiRJUpMMO5IkSZKaZNiR\nJEmS1CTDjiRJkqQmGXYkSZIkNcmwI0mSJKlJhh1JkiRJTTLsSJIkSWrS/wMbfelf4SkUbgAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f23ac6af0b8>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 413,
              "height": 423
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "eArGnvY8yTn3",
        "colab_type": "code",
        "outputId": "def522f6-ffd4-4cbb-caa4-1a9988da31d8",
        "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: 0.265.. Test accuracy: 0.927\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bF2H_ePl_Bbd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}