FlowerClassifier_Batch32_Acc92.ipynb

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      "metadata": {
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      "source": [
        "# Developing an AI application\n",
        "\n",
        "Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. \n",
        "\n",
        "In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below. \n",
        "\n",
        "<img src='assets/Flowers.png' width=500px>\n",
        "\n",
        "The project is broken down into multiple steps:\n",
        "\n",
        "* Load and preprocess the image dataset\n",
        "* Train the image classifier on your dataset\n",
        "* Use the trained classifier to predict image content\n",
        "\n",
        "We'll lead you through each part which you'll implement in Python.\n",
        "\n",
        "When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.\n",
        "\n",
        "First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here."
      ]
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "# !ls \"/content/drive/My Drive/\"\n",
        "\n",
        "import os\n",
        "os.chdir('/content/drive/My Drive/Colab Notebooks/pytorch_challenge')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "db3ZnI_kyTm9",
        "colab_type": "code",
        "outputId": "545122b3-139b-4f10-eac1-65d48375eb4f",
        "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: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (5.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6dl0fstCyTnC",
        "colab_type": "code",
        "outputId": "dda6b025-61a1-42b7-e4da-dbaed6555633",
        "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": "fe15b560-3dff-4d15-e1e0-1bbc289e22b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "cell_type": "code",
      "source": [
        "# TODO: Define your transforms for the training and validation sets\n",
        "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
        "\n",
        "train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
        "                                       transforms.RandomResizedCrop(224),\n",
        "                                       transforms.RandomHorizontalFlip(),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       normalize])\n",
        "\n",
        "valid_transforms = transforms.Compose([transforms.Resize(256),\n",
        "                                      transforms.CenterCrop(224),\n",
        "                                      transforms.ToTensor(),\n",
        "                                      normalize])\n",
        "\n",
        "# TODO: Load the datasets with ImageFolder\n",
        "train_data = datasets.ImageFolder(train_dir, transform=train_transforms)\n",
        "valid_data = datasets.ImageFolder(valid_dir, transform=valid_transforms)\n",
        "print('class_to_idx:')\n",
        "print(train_data.class_to_idx)\n",
        "\n",
        "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
        "trainloader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True)\n",
        "validloader = torch.utils.data.DataLoader(valid_data, batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "class_to_idx:\n",
            "{'1': 0, '10': 1, '100': 2, '101': 3, '102': 4, '11': 5, '12': 6, '13': 7, '14': 8, '15': 9, '16': 10, '17': 11, '18': 12, '19': 13, '2': 14, '20': 15, '21': 16, '22': 17, '23': 18, '24': 19, '25': 20, '26': 21, '27': 22, '28': 23, '29': 24, '3': 25, '30': 26, '31': 27, '32': 28, '33': 29, '34': 30, '35': 31, '36': 32, '37': 33, '38': 34, '39': 35, '4': 36, '40': 37, '41': 38, '42': 39, '43': 40, '44': 41, '45': 42, '46': 43, '47': 44, '48': 45, '49': 46, '5': 47, '50': 48, '51': 49, '52': 50, '53': 51, '54': 52, '55': 53, '56': 54, '57': 55, '58': 56, '59': 57, '6': 58, '60': 59, '61': 60, '62': 61, '63': 62, '64': 63, '65': 64, '66': 65, '67': 66, '68': 67, '69': 68, '7': 69, '70': 70, '71': 71, '72': 72, '73': 73, '74': 74, '75': 75, '76': 76, '77': 77, '78': 78, '79': 79, '8': 80, '80': 81, '81': 82, '82': 83, '83': 84, '84': 85, '85': 86, '86': 87, '87': 88, '88': 89, '89': 90, '9': 91, '90': 92, '91': 93, '92': 94, '93': 95, '94': 96, '95': 97, '96': 98, '97': 99, '98': 100, '99': 101}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nE-b9jVqyTnJ",
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      "cell_type": "markdown",
      "source": [
        "### Label mapping\n",
        "\n",
        "You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."
      ]
    },
    {
      "metadata": {
        "id": "MiOO7pmayTnJ",
        "colab_type": "code",
        "outputId": "e843f20a-0724-47f3-f3da-ff0570850755",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('cat_to_name.json', 'r') as f:\n",
        "    cat_to_name = json.load(f)\n",
        "    print(cat_to_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'21': 'fire lily', '3': 'canterbury bells', '45': 'bolero deep blue', '1': 'pink primrose', '34': 'mexican aster', '27': 'prince of wales feathers', '7': 'moon orchid', '16': 'globe-flower', '25': 'grape hyacinth', '26': 'corn poppy', '79': 'toad lily', '39': 'siam tulip', '24': 'red ginger', '67': 'spring crocus', '35': 'alpine sea holly', '32': 'garden phlox', '10': 'globe thistle', '6': 'tiger lily', '93': 'ball moss', '33': 'love in the mist', '9': 'monkshood', '102': 'blackberry lily', '14': 'spear thistle', '19': 'balloon flower', '100': 'blanket flower', '13': 'king protea', '49': 'oxeye daisy', '15': 'yellow iris', '61': 'cautleya spicata', '31': 'carnation', '64': 'silverbush', '68': 'bearded iris', '63': 'black-eyed susan', '69': 'windflower', '62': 'japanese anemone', '20': 'giant white arum lily', '38': 'great masterwort', '4': 'sweet pea', '86': 'tree mallow', '101': 'trumpet creeper', '42': 'daffodil', '22': 'pincushion flower', '2': 'hard-leaved pocket orchid', '54': 'sunflower', '66': 'osteospermum', '70': 'tree poppy', '85': 'desert-rose', '99': 'bromelia', '87': 'magnolia', '5': 'english marigold', '92': 'bee balm', '28': 'stemless gentian', '97': 'mallow', '57': 'gaura', '40': 'lenten rose', '47': 'marigold', '59': 'orange dahlia', '48': 'buttercup', '55': 'pelargonium', '36': 'ruby-lipped cattleya', '91': 'hippeastrum', '29': 'artichoke', '71': 'gazania', '90': 'canna lily', '18': 'peruvian lily', '98': 'mexican petunia', '8': 'bird of paradise', '30': 'sweet william', '17': 'purple coneflower', '52': 'wild pansy', '84': 'columbine', '12': \"colt's foot\", '11': 'snapdragon', '96': 'camellia', '23': 'fritillary', '50': 'common dandelion', '44': 'poinsettia', '53': 'primula', '72': 'azalea', '65': 'californian poppy', '80': 'anthurium', '76': 'morning glory', '37': 'cape flower', '56': 'bishop of llandaff', '60': 'pink-yellow dahlia', '82': 'clematis', '58': 'geranium', '75': 'thorn apple', '41': 'barbeton daisy', '95': 'bougainvillea', '43': 'sword lily', '83': 'hibiscus', '78': 'lotus lotus', '88': 'cyclamen', '94': 'foxglove', '81': 'frangipani', '74': 'rose', '89': 'watercress', '73': 'water lily', '46': 'wallflower', '77': 'passion flower', '51': 'petunia'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "# Building and training the classifier\n",
        "\n",
        "Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from `torchvision.models` to get the image features. Build and train a new feed-forward classifier using those features.\n",
        "\n",
        "We're going to leave this part up to you. If you want to talk through it with someone, chat with your fellow students! You can also ask questions on the forums or join the instructors in office hours.\n",
        "\n",
        "Refer to [the rubric](https://review.udacity.com/#!/rubrics/1663/view) for guidance on successfully completing this section. Things you'll need to do:\n",
        "\n",
        "* Load a [pre-trained network](http://pytorch.org/docs/master/torchvision/models.html) (If you need a starting point, the VGG networks work great and are straightforward to use)\n",
        "* Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout\n",
        "* Train the classifier layers using backpropagation using the pre-trained network to get the features\n",
        "* Track the loss and accuracy on the validation set to determine the best hyperparameters\n",
        "\n",
        "We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!\n",
        "\n",
        "When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project."
      ]
    },
    {
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        "colab_type": "code",
        "outputId": "8e9874ed-81b9-43c5-cdb8-a2cda183eb8a",
        "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",
        "    def forward(self, x):\n",
        "        # Now with dropout\n",
        "        x = 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": "2ba23c09-e2da-415c-b2f9-ee3ca85b65f5",
        "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: 229.839 seconds\n",
            "Epoch 1/10.. Step: 20.. Train loss: 4.585.. Test loss: 4.268.. Test accuracy: 0.210\n",
            "Validate time: 31.253 seconds\n",
            "Epoch 1/10.. Step: 40.. Train loss: 4.149.. Test loss: 3.917.. Test accuracy: 0.221\n",
            "Validate time: 31.119 seconds\n",
            "Epoch 1/10.. Step: 60.. Train loss: 3.690.. Test loss: 3.244.. Test accuracy: 0.332\n",
            "Validate time: 31.430 seconds\n",
            "Epoch 1/10.. Step: 80.. Train loss: 3.157.. Test loss: 2.594.. Test accuracy: 0.434\n",
            "Validate time: 31.160 seconds\n",
            "Epoch 1/10.. Step: 100.. Train loss: 2.739.. Test loss: 2.241.. Test accuracy: 0.490\n",
            "Validate time: 30.979 seconds\n",
            "Epoch 1/10.. Step: 120.. Train loss: 2.340.. Test loss: 1.806.. Test accuracy: 0.541\n",
            "Validate time: 31.079 seconds\n",
            "Epoch 1/10.. Step: 140.. Train loss: 2.009.. Test loss: 1.631.. Test accuracy: 0.596\n",
            "Validate time: 30.663 seconds\n",
            "Epoch 1/10.. Step: 160.. Train loss: 1.863.. Test loss: 1.326.. Test accuracy: 0.671\n",
            "Validate time: 30.274 seconds\n",
            "Epoch 1/10.. Step: 180.. Train loss: 1.585.. Test loss: 1.131.. Test accuracy: 0.724\n",
            "Validate time: 30.167 seconds\n",
            "Epoch 1/10.. Step: 200.. Train loss: 1.462.. Test loss: 1.006.. Test accuracy: 0.778\n",
            "Time per epoch: 2257.627 seconds\n",
            "Validate time: 29.970 seconds\n",
            "Epoch 2/10.. Step: 220.. Train loss: 1.265.. Test loss: 0.909.. Test accuracy: 0.772\n",
            "Validate time: 30.034 seconds\n",
            "Epoch 2/10.. Step: 240.. Train loss: 1.254.. Test loss: 0.918.. Test accuracy: 0.769\n",
            "Validate time: 29.999 seconds\n",
            "Epoch 2/10.. Step: 260.. Train loss: 1.138.. Test loss: 0.808.. Test accuracy: 0.786\n",
            "Validate time: 30.295 seconds\n",
            "Epoch 2/10.. Step: 280.. Train loss: 1.091.. Test loss: 0.755.. Test accuracy: 0.809\n",
            "Validate time: 30.800 seconds\n",
            "Epoch 2/10.. Step: 300.. Train loss: 1.090.. Test loss: 0.726.. Test accuracy: 0.812\n",
            "Validate time: 30.729 seconds\n",
            "Epoch 2/10.. Step: 320.. Train loss: 0.964.. Test loss: 0.603.. Test accuracy: 0.857\n",
            "Validate time: 30.865 seconds\n",
            "Epoch 2/10.. Step: 340.. Train loss: 0.964.. Test loss: 0.645.. Test accuracy: 0.837\n",
            "Validate time: 30.406 seconds\n",
            "Epoch 2/10.. Step: 360.. Train loss: 1.023.. Test loss: 0.568.. Test accuracy: 0.852\n",
            "Validate time: 31.148 seconds\n",
            "Epoch 2/10.. Step: 380.. Train loss: 0.879.. Test loss: 0.515.. Test accuracy: 0.886\n",
            "Validate time: 31.019 seconds\n",
            "Epoch 2/10.. Step: 400.. Train loss: 0.861.. Test loss: 0.547.. Test accuracy: 0.862\n",
            "Time per epoch: 550.605 seconds\n",
            "Validate time: 31.189 seconds\n",
            "Epoch 3/10.. Step: 420.. Train loss: 0.870.. Test loss: 0.512.. Test accuracy: 0.879\n",
            "Validate time: 30.768 seconds\n",
            "Epoch 3/10.. Step: 440.. Train loss: 0.749.. Test loss: 0.493.. Test accuracy: 0.879\n",
            "Validate time: 30.371 seconds\n",
            "Epoch 3/10.. Step: 460.. Train loss: 0.837.. Test loss: 0.530.. Test accuracy: 0.865\n",
            "Validate time: 30.978 seconds\n",
            "Epoch 3/10.. Step: 480.. Train loss: 0.836.. Test loss: 0.482.. Test accuracy: 0.872\n",
            "Validate time: 30.680 seconds\n",
            "Epoch 3/10.. Step: 500.. Train loss: 0.802.. Test loss: 0.468.. Test accuracy: 0.884\n",
            "Validate time: 30.390 seconds\n",
            "Epoch 3/10.. Step: 520.. Train loss: 0.786.. Test loss: 0.444.. Test accuracy: 0.878\n",
            "Validate time: 30.249 seconds\n",
            "Epoch 3/10.. Step: 540.. Train loss: 0.797.. Test loss: 0.490.. Test accuracy: 0.874\n",
            "Validate time: 30.861 seconds\n",
            "Epoch 3/10.. Step: 560.. Train loss: 0.799.. Test loss: 0.491.. Test accuracy: 0.877\n",
            "Validate time: 30.783 seconds\n",
            "Epoch 3/10.. Step: 580.. Train loss: 0.781.. Test loss: 0.417.. Test accuracy: 0.887\n",
            "Validate time: 30.743 seconds\n",
            "Epoch 3/10.. Step: 600.. Train loss: 0.660.. Test loss: 0.391.. Test accuracy: 0.894\n",
            "Time per epoch: 553.776 seconds\n",
            "Validate time: 30.650 seconds\n",
            "Epoch 4/10.. Step: 620.. Train loss: 0.664.. Test loss: 0.363.. Test accuracy: 0.903\n",
            "Validate time: 30.688 seconds\n",
            "Epoch 4/10.. Step: 640.. Train loss: 0.777.. Test loss: 0.382.. Test accuracy: 0.907\n",
            "Validate time: 30.347 seconds\n",
            "Epoch 4/10.. Step: 660.. Train loss: 0.750.. Test loss: 0.457.. Test accuracy: 0.883\n",
            "Validate time: 30.506 seconds\n",
            "Epoch 4/10.. Step: 680.. Train loss: 0.617.. Test loss: 0.385.. Test accuracy: 0.902\n",
            "Validate time: 30.239 seconds\n",
            "Epoch 4/10.. Step: 700.. Train loss: 0.622.. Test loss: 0.369.. Test accuracy: 0.904\n",
            "Validate time: 30.135 seconds\n",
            "Epoch 4/10.. Step: 720.. Train loss: 0.749.. Test loss: 0.377.. Test accuracy: 0.901\n",
            "Validate time: 30.249 seconds\n",
            "Epoch 4/10.. Step: 740.. Train loss: 0.667.. Test loss: 0.398.. Test accuracy: 0.904\n",
            "Validate time: 29.926 seconds\n",
            "Epoch 4/10.. Step: 760.. Train loss: 0.632.. Test loss: 0.344.. Test accuracy: 0.913\n",
            "Validate time: 29.851 seconds\n",
            "Epoch 4/10.. Step: 780.. Train loss: 0.653.. Test loss: 0.335.. Test accuracy: 0.904\n",
            "Validate time: 30.292 seconds\n",
            "Epoch 4/10.. Step: 800.. Train loss: 0.604.. Test loss: 0.352.. Test accuracy: 0.913\n",
            "Validate time: 29.967 seconds\n",
            "Epoch 4/10.. Step: 820.. Train loss: 0.618.. Test loss: 0.404.. Test accuracy: 0.881\n",
            "Time per epoch: 575.438 seconds\n",
            "Validate time: 29.971 seconds\n",
            "Epoch 5/10.. Step: 840.. Train loss: 0.618.. Test loss: 0.397.. Test accuracy: 0.903\n",
            "Validate time: 29.793 seconds\n",
            "Epoch 5/10.. Step: 860.. Train loss: 0.628.. Test loss: 0.356.. Test accuracy: 0.914\n",
            "Validate time: 29.799 seconds\n",
            "Epoch 5/10.. Step: 880.. Train loss: 0.571.. Test loss: 0.351.. Test accuracy: 0.904\n",
            "Validate time: 30.330 seconds\n",
            "Epoch 5/10.. Step: 900.. Train loss: 0.529.. Test loss: 0.325.. Test accuracy: 0.917\n",
            "Validate time: 31.340 seconds\n",
            "Epoch 5/10.. Step: 920.. Train loss: 0.571.. Test loss: 0.387.. Test accuracy: 0.890\n",
            "Validate time: 31.254 seconds\n",
            "Epoch 5/10.. Step: 940.. Train loss: 0.620.. Test loss: 0.341.. Test accuracy: 0.907\n",
            "Validate time: 31.435 seconds\n",
            "Epoch 5/10.. Step: 960.. Train loss: 0.613.. Test loss: 0.382.. Test accuracy: 0.891\n",
            "Validate time: 30.736 seconds\n",
            "Epoch 5/10.. Step: 980.. Train loss: 0.588.. Test loss: 0.326.. Test accuracy: 0.923\n",
            "Validate time: 31.366 seconds\n",
            "Epoch 5/10.. Step: 1000.. Train loss: 0.519.. Test loss: 0.319.. Test accuracy: 0.918\n",
            "Validate time: 31.073 seconds\n",
            "Epoch 5/10.. Step: 1020.. Train loss: 0.526.. Test loss: 0.332.. Test accuracy: 0.915\n",
            "Time per epoch: 553.559 seconds\n",
            "Validate time: 31.344 seconds\n",
            "Epoch 6/10.. Step: 1040.. Train loss: 0.525.. Test loss: 0.304.. Test accuracy: 0.922\n",
            "Validate time: 30.851 seconds\n",
            "Epoch 6/10.. Step: 1060.. Train loss: 0.537.. Test loss: 0.325.. Test accuracy: 0.912\n",
            "Validate time: 31.263 seconds\n",
            "Epoch 6/10.. Step: 1080.. Train loss: 0.567.. Test loss: 0.317.. Test accuracy: 0.920\n",
            "Validate time: 31.225 seconds\n",
            "Epoch 6/10.. Step: 1100.. Train loss: 0.540.. Test loss: 0.323.. Test accuracy: 0.917\n",
            "Validate time: 31.058 seconds\n",
            "Epoch 6/10.. Step: 1120.. Train loss: 0.547.. Test loss: 0.290.. Test accuracy: 0.931\n",
            "Validate time: 31.142 seconds\n",
            "Epoch 6/10.. Step: 1140.. Train loss: 0.564.. Test loss: 0.296.. Test accuracy: 0.918\n",
            "Validate time: 30.925 seconds\n",
            "Epoch 6/10.. Step: 1160.. Train loss: 0.510.. Test loss: 0.325.. Test accuracy: 0.902\n",
            "Validate time: 30.821 seconds\n",
            "Epoch 6/10.. Step: 1180.. Train loss: 0.539.. Test loss: 0.346.. Test accuracy: 0.914\n",
            "Validate time: 30.652 seconds\n",
            "Epoch 6/10.. Step: 1200.. Train loss: 0.599.. Test loss: 0.304.. Test accuracy: 0.916\n",
            "Validate time: 30.684 seconds\n",
            "Epoch 6/10.. Step: 1220.. Train loss: 0.549.. Test loss: 0.293.. Test accuracy: 0.923\n",
            "Time per epoch: 558.177 seconds\n",
            "Validate time: 29.682 seconds\n",
            "Epoch 7/10.. Step: 1240.. Train loss: 0.440.. Test loss: 0.319.. Test accuracy: 0.916\n",
            "Validate time: 30.404 seconds\n",
            "Epoch 7/10.. Step: 1260.. Train loss: 0.411.. Test loss: 0.311.. Test accuracy: 0.914\n",
            "Validate time: 30.535 seconds\n",
            "Epoch 7/10.. Step: 1280.. Train loss: 0.503.. Test loss: 0.299.. Test accuracy: 0.920\n",
            "Validate time: 30.632 seconds\n",
            "Epoch 7/10.. Step: 1300.. Train loss: 0.553.. Test loss: 0.346.. Test accuracy: 0.920\n",
            "Validate time: 30.618 seconds\n",
            "Epoch 7/10.. Step: 1320.. Train loss: 0.450.. Test loss: 0.294.. Test accuracy: 0.915\n",
            "Validate time: 30.613 seconds\n",
            "Epoch 7/10.. Step: 1340.. Train loss: 0.490.. Test loss: 0.350.. Test accuracy: 0.903\n",
            "Validate time: 31.270 seconds\n",
            "Epoch 7/10.. Step: 1360.. Train loss: 0.569.. Test loss: 0.281.. Test accuracy: 0.929\n",
            "Validate time: 31.339 seconds\n",
            "Epoch 7/10.. Step: 1380.. Train loss: 0.468.. Test loss: 0.266.. Test accuracy: 0.928\n",
            "Validate time: 31.103 seconds\n",
            "Epoch 7/10.. Step: 1400.. Train loss: 0.531.. Test loss: 0.355.. Test accuracy: 0.905\n",
            "Validate time: 30.011 seconds\n",
            "Epoch 7/10.. Step: 1420.. Train loss: 0.564.. Test loss: 0.306.. Test accuracy: 0.926\n",
            "Time per epoch: 552.348 seconds\n",
            "Validate time: 30.174 seconds\n",
            "Epoch 8/10.. Step: 1440.. Train loss: 0.492.. Test loss: 0.265.. Test accuracy: 0.921\n",
            "Validate time: 30.109 seconds\n",
            "Epoch 8/10.. Step: 1460.. Train loss: 0.451.. Test loss: 0.299.. Test accuracy: 0.925\n",
            "Validate time: 30.364 seconds\n",
            "Epoch 8/10.. Step: 1480.. Train loss: 0.578.. Test loss: 0.304.. Test accuracy: 0.920\n",
            "Validate time: 30.109 seconds\n",
            "Epoch 8/10.. Step: 1500.. Train loss: 0.518.. Test loss: 0.289.. Test accuracy: 0.925\n",
            "Validate time: 30.469 seconds\n",
            "Epoch 8/10.. Step: 1520.. Train loss: 0.470.. Test loss: 0.263.. Test accuracy: 0.943\n",
            "Validate time: 30.720 seconds\n",
            "Epoch 8/10.. Step: 1540.. Train loss: 0.466.. Test loss: 0.250.. Test accuracy: 0.939\n",
            "Validate time: 30.633 seconds\n",
            "Epoch 8/10.. Step: 1560.. Train loss: 0.488.. Test loss: 0.299.. Test accuracy: 0.922\n",
            "Validate time: 30.504 seconds\n",
            "Epoch 8/10.. Step: 1580.. Train loss: 0.455.. Test loss: 0.252.. Test accuracy: 0.934\n",
            "Validate time: 30.366 seconds\n",
            "Epoch 8/10.. Step: 1600.. Train loss: 0.470.. Test loss: 0.277.. Test accuracy: 0.927\n",
            "Validate time: 30.888 seconds\n",
            "Epoch 8/10.. Step: 1620.. Train loss: 0.474.. Test loss: 0.270.. Test accuracy: 0.925\n",
            "Validate time: 30.814 seconds\n",
            "Epoch 8/10.. Step: 1640.. Train loss: 0.459.. Test loss: 0.306.. Test accuracy: 0.917\n",
            "Time per epoch: 579.496 seconds\n",
            "Validate time: 30.746 seconds\n",
            "Epoch 9/10.. Step: 1660.. Train loss: 0.453.. Test loss: 0.319.. Test accuracy: 0.926\n",
            "Validate time: 30.586 seconds\n",
            "Epoch 9/10.. Step: 1680.. Train loss: 0.450.. Test loss: 0.297.. Test accuracy: 0.914\n",
            "Validate time: 30.610 seconds\n",
            "Epoch 9/10.. Step: 1700.. Train loss: 0.502.. Test loss: 0.281.. Test accuracy: 0.919\n",
            "Validate time: 30.621 seconds\n",
            "Epoch 9/10.. Step: 1720.. Train loss: 0.494.. Test loss: 0.279.. Test accuracy: 0.924\n",
            "Validate time: 31.257 seconds\n",
            "Epoch 9/10.. Step: 1740.. Train loss: 0.392.. Test loss: 0.236.. Test accuracy: 0.932\n",
            "Validate time: 31.075 seconds\n",
            "Epoch 9/10.. Step: 1760.. Train loss: 0.481.. Test loss: 0.266.. Test accuracy: 0.932\n",
            "Validate time: 31.067 seconds\n",
            "Epoch 9/10.. Step: 1780.. Train loss: 0.405.. Test loss: 0.248.. Test accuracy: 0.934\n",
            "Validate time: 30.798 seconds\n",
            "Epoch 9/10.. Step: 1800.. Train loss: 0.554.. Test loss: 0.256.. Test accuracy: 0.932\n",
            "Validate time: 31.297 seconds\n",
            "Epoch 9/10.. Step: 1820.. Train loss: 0.528.. Test loss: 0.275.. Test accuracy: 0.928\n",
            "Validate time: 30.979 seconds\n",
            "Epoch 9/10.. Step: 1840.. Train loss: 0.466.. Test loss: 0.244.. Test accuracy: 0.941\n",
            "Time per epoch: 556.843 seconds\n",
            "Validate time: 31.262 seconds\n",
            "Epoch 10/10.. Step: 1860.. Train loss: 0.422.. Test loss: 0.270.. Test accuracy: 0.922\n",
            "Validate time: 31.368 seconds\n",
            "Epoch 10/10.. Step: 1880.. Train loss: 0.333.. Test loss: 0.260.. Test accuracy: 0.920\n",
            "Validate time: 30.264 seconds\n",
            "Epoch 10/10.. Step: 1900.. Train loss: 0.366.. Test loss: 0.246.. Test accuracy: 0.929\n",
            "Validate time: 30.171 seconds\n",
            "Epoch 10/10.. Step: 1920.. Train loss: 0.498.. Test loss: 0.281.. Test accuracy: 0.928\n",
            "Validate time: 30.108 seconds\n",
            "Epoch 10/10.. Step: 1940.. Train loss: 0.420.. Test loss: 0.392.. Test accuracy: 0.886\n",
            "Validate time: 30.512 seconds\n",
            "Epoch 10/10.. Step: 1960.. Train loss: 0.433.. Test loss: 0.275.. Test accuracy: 0.934\n",
            "Validate time: 30.961 seconds\n",
            "Epoch 10/10.. Step: 1980.. Train loss: 0.453.. Test loss: 0.298.. Test accuracy: 0.925\n",
            "Validate time: 30.878 seconds\n",
            "Epoch 10/10.. Step: 2000.. Train loss: 0.450.. Test loss: 0.252.. Test accuracy: 0.937\n",
            "Validate time: 30.719 seconds\n",
            "Epoch 10/10.. Step: 2020.. Train loss: 0.426.. Test loss: 0.269.. Test accuracy: 0.937\n",
            "Validate time: 30.782 seconds\n",
            "Epoch 10/10.. Step: 2040.. Train loss: 0.385.. Test loss: 0.254.. Test accuracy: 0.932\n",
            "Time per epoch: 553.224 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wYGk3CDuMh74",
        "colab_type": "code",
        "outputId": "a9186d6c-f59d-45db-e887-0eaf75f28701",
        "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 0x7f38ca07d3c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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o3mHanNBaJN9gdxg7IgIAAKB/8nmYtlqtYyTd6uvzBkNSTKLSYls7eDREHfH4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\nX0PA8nuuKOT80tCWfHi8XvabO9i8t4Gq5t6g56Mj1Jxfmsr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SKW3buczldVPe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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f38ca140f28>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 361,
              "height": 248
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "UlKn5K1WAMn3",
        "colab_type": "code",
        "outputId": "6f02839e-e101-4a97-93ee-a28a0afb772e",
        "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 0x7f38ca090dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JX//Q1UXjHoxPlMTYSL546zK+8+ftnjSrwbWsL1s1+nJ/4K7r/PTbJT4zRNqA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\n4hsn7k7hNsY7SZKUD2ond9522y1UV1c3OZkTEhPvpk17gl/96kpOP/0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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f38ca090fd0>"
            ]
          },
          "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": "583ce20c-47a8-4ea4-9313-724a53003d32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8262
        }
      },
      "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_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",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "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": "a27947d8-9c1d-41fb-99d7-3b47a2c3eaef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        }
      },
      "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([[0.9722, 0.0116, 0.0068, 0.0053, 0.0014]], grad_fn=<TopkBackward>)\n",
            "tensor([[ 7, 77, 24,  8, 66]])\n",
            "['king protea', 'passion flower', 'artichoke', 'spear thistle', 'spring crocus']\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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u+/1xI5096Y/xqbtDi/PxWPFyz0yWZF1oAe6sdutgD/QJZWwAAAAAeonMDoA9w2VNALrH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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f38c9f9f908>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 413,
              "height": 423
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "eArGnvY8yTn3",
        "colab_type": "code",
        "outputId": "a1a4182d-b5a6-4c99-9039-4df5ec125355",
        "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: 0.317.. Test accuracy: 0.922\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0GgpzhcH_68I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}