Image Classifier Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\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",
    "from collections import OrderedDict\n",
    "from time import time\n",
    "from torch.autograd import Variable\n",
    "from PIL import Image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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)). The data should be included alongside this notebook, otherwise you can [download it here](https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz). The dataset is split into three parts, training, validation, and testing. 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. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.\n",
    "\n",
    "The validation and testing sets are 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 you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three 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.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'flowers'\n",
    "train_dir = data_dir + '/train'\n",
    "valid_dir = data_dir + '/valid'\n",
    "test_dir = data_dir + '/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define your transforms for the training, validation, and testing sets\n",
    "train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
    "                                       transforms.RandomResizedCrop(224),\n",
    "                                       transforms.RandomHorizontalFlip(),\n",
    "                                       transforms.ToTensor(),\n",
    "                                       transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                            [0.229, 0.224, 0.225])])\n",
    "\n",
    "val_transforms = transforms.Compose([transforms.Resize(255),\n",
    "                                      transforms.CenterCrop(224),\n",
    "                                      transforms.ToTensor(),\n",
    "                                      transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                           [0.229, 0.224, 0.225])])\n",
    "\n",
    "test_transforms = transforms.Compose([transforms.Resize(255),\n",
    "                                      transforms.CenterCrop(224),\n",
    "                                      transforms.ToTensor(),\n",
    "                                      transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                           [0.229, 0.224, 0.225])])\n",
    "\n",
    "# TODO: Load the datasets with ImageFolder\n",
    "train_data = datasets.ImageFolder(data_dir + '/train', transform=train_transforms)\n",
    "val_data = datasets.ImageFolder(data_dir + '/valid', transform=val_transforms)\n",
    "test_data = datasets.ImageFolder(data_dir + '/test', transform=test_transforms)\n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\n",
    "val_loader = torch.utils.data.DataLoader(val_data, batch_size=64)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('cat_to_name.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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. 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.\n",
    "\n",
    "One last important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to\n",
    "GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.\n",
    "\n",
    "**Note for Workspace users:** If your network is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. Typically this happens with wide dense layers after the convolutional layers. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with `ls -lh`), you should reduce the size of your hidden layers and train again."
   ]
  },
  {
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   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n"
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       "      (denselayer1): _DenseLayer(\n",
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       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock4): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Linear(in_features=1024, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Build and train your network\n",
    "model = models.densenet121(pretrained=True)\n",
    "model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Freeze feature parameters (weights and biases)\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "    \n",
    "#Reconstruct the architecture acc. to our needs, input exp. 1024. output 102 classes\n",
    "classifier = nn.Sequential(OrderedDict([\n",
    "                          ('fc1', nn.Linear(1024, 500)),\n",
    "                          ('relu', nn.ReLU()),\n",
    "                          ('fc2', nn.Linear(500, 102)),\n",
    "                          ('output', nn.LogSoftmax(dim=1))\n",
    "                          ]))\n",
    "#Set the models classifier to the new arch    \n",
    "model.classifier = classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu0): ReLU(inplace)\n",
       "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (denseblock1): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock2): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock3): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock4): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (fc1): Linear(in_features=1024, out_features=500, bias=True)\n",
       "    (relu): ReLU()\n",
       "    (fc2): Linear(in_features=500, out_features=102, bias=True)\n",
       "    (output): LogSoftmax()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify loss fn, optimizer\n",
    "#To record time\n",
    "start_time = time()\n",
    "#To choose between gpu and cpu\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "#Use Negative log loss coz of using logsoftmax\n",
    "criterion = nn.NLLLoss()\n",
    "# Only train the classifier parameters, feature parameters are frozen\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fn for training\n",
    "def train(model, criterion, train_loader, optimizer):\n",
    "    model.to(device)\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    for data, target in train_loader:\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        # print(\"data\", data.shape)\n",
    "        output = model(data)\n",
    "        target = Variable(target)\n",
    "        # print(\"train target\", target)\n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "    train_loss = train_loss / len(train_loader.dataset)\n",
    "    return train_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for validation & testing\n",
    "def validation(model, val_loader, criterion):\n",
    "    model.to(device)\n",
    "    model.eval()\n",
    "    correct = 0.0\n",
    "    loss_all = 0.0\n",
    "    with torch.no_grad():\n",
    "        for data, target in val_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            target = Variable(target)\n",
    "            loss = criterion(output, target)\n",
    "            pred = output.max(dim=1)[1]\n",
    "            correct += pred.eq(target).sum().item()\n",
    "            loss_all += loss.item()\n",
    "\n",
    "\n",
    "    return loss_all / len(val_loader.dataset), float(correct) / len(val_loader.dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 train loss: 0.038464925955794524 validation loss: 0.01459231757738771 validation accuracy: 0.7701711491442543\n",
      "epoch: 1 train loss: 0.014321413459722522 validation loss: 0.00815607733969875 validation accuracy: 0.8606356968215159\n",
      "epoch: 2 train loss: 0.011022250594455245 validation loss: 0.006909697783386795 validation accuracy: 0.8765281173594132\n",
      "epoch: 3 train loss: 0.009172717366317544 validation loss: 0.007234209269503801 validation accuracy: 0.8814180929095354\n",
      "epoch: 4 train loss: 0.00858265390762916 validation loss: 0.0051400507720583515 validation accuracy: 0.9144254278728606\n",
      "\n",
      "** Total Elapsed Runtime: 0:10:6\n"
     ]
    }
   ],
   "source": [
    "# Training code\n",
    "epochs = 5\n",
    "running_loss = 0\n",
    "for epoch in range(epochs):\n",
    "    train_loss = train(model, criterion, train_loader, optimizer)\n",
    "    valid_loss, valid_accuracy = validation(model, val_loader, criterion)\n",
    "    print(\"epoch:\", epoch, \"train loss:\", train_loss, \"validation loss:\", valid_loss, \"validation accuracy:\",\n",
    "          valid_accuracy)\n",
    "\n",
    "\n",
    "end_time = time()\n",
    "tot_time = end_time - start_time  # calculate difference between end time and start time\n",
    "print(\"\\n** Total Elapsed Runtime:\",\n",
    "      str(int((tot_time / 3600))) + \":\" + str(int((tot_time % 3600) / 60)) + \":\"\n",
    "      + str(int((tot_time % 3600) % 60)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing your network\n",
    "\n",
    "It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.005255893036559388 Test accuracy: 0.9096459096459096\n"
     ]
    }
   ],
   "source": [
    "# TODO: Do validation on the test set\n",
    "test_loss, test_accuracy = validation(model, test_loader, criterion)\n",
    "print(\"Test loss:\", test_loss, \"Test accuracy:\", test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Save the checkpoint \n",
    "model.class_to_idx = train_data.class_to_idx\n",
    "checkpoint = {'classifier': model.classifier,\n",
    "              'epoch': epochs,\n",
    "              'class_to_idx': model.class_to_idx,\n",
    "              'state_dict': model.state_dict()}\n",
    "\n",
    "torch.save(checkpoint, 'checkpoint.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "def load_the_checkpoint(path):\n",
    "    load_dict_checkpt = torch.load(path)\n",
    "    model = models.densenet121(pretrained=True)\n",
    "    model.classifier = load_dict_checkpt['classifier']\n",
    "    model.epochs = load_dict_checkpt['epoch']\n",
    "    model.class_to_idx = load_dict_checkpt['class_to_idx']\n",
    "    model.load_state_dict(load_dict_checkpt['state_dict'])\n",
    "    for param in model.parameters():\n",
    "            param.requires_grad = False\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n"
     ]
    }
   ],
   "source": [
    "model = load_the_checkpoint('checkpoint.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "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",
    "    #RESIZE:\n",
    "    im_process = Image.open(image)\n",
    "#     print(\"size before resize\", im_process.size)\n",
    "    # new_width, new_height = 0, 0\n",
    "    # aspect_ratio = im_process.size[0]/im_process.size[1]\n",
    "    #Use ANTIALIAS to preserve aspect ratio\n",
    "    if im_process.size[0] < im_process.size[1]:\n",
    "        new_width = 256\n",
    "        im_process.thumbnail((new_width, im_process.size[1]), Image.ANTIALIAS)\n",
    "    else:\n",
    "        new_height = 256\n",
    "        im_process.thumbnail((im_process.size[0], new_height), Image.ANTIALIAS)\n",
    "#     print(\"size after resize\", im_process.size)\n",
    "\n",
    "    #CROP:\n",
    "    #Since both w and h are > than 224\n",
    "    left_pt = (im_process.size[0] - 224) / 2\n",
    "    right_pt = left_pt + 224\n",
    "    top_pt = (im_process.size[1] - 224) / 2\n",
    "    bottom_pt = top_pt + 224\n",
    "\n",
    "    im_process = im_process.crop((left_pt, top_pt, right_pt, bottom_pt))\n",
    "#     print(\"size after crop\", im_process.size)\n",
    "\n",
    "    #TO NP ARRAY:\n",
    "    np_image = np.array(im_process)\n",
    "    #normalize to 0-1\n",
    "    np_image = np_image / 255\n",
    "#     print(\"image R G B\", np_image[0][0])\n",
    "    means = [0.485, 0.456, 0.406]\n",
    "    standard_deviations = [0.229, 0.224, 0.225]\n",
    "    np_image = np_image - means\n",
    "#     print(\"image R G B after subtraction\", np_image[0][0])\n",
    "    np_image = np_image / standard_deviations\n",
    "#     print(\"image R G B after division\", np_image[0][0])\n",
    "#     print(\"Before transpose\", np_image.shape)\n",
    "    np_image = np.transpose(np_image, (2, 0, 1))\n",
    "#     print(\"After transpose\", np_image.shape)\n",
    "#     print(\"Type of array\", type(np_image))\n",
    "    return np_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "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",
    "    image = image.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fecc5989748>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fecc59a2cc0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 259
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = data_dir + '/test/1/image_06764.jpg'\n",
    "imshow(process_image(image_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(image_path, model, top_k=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",
    "    image_input = process_image(image_path)\n",
    "    image_input = torch.from_numpy(image_input).float().to(device)\n",
    "    #add one dimension for batch as tensor expects batch number\n",
    "    image_input = image_input.unsqueeze(0)\n",
    "#     print(\"predict one image shape is {}\".format(image_input.shape))\n",
    "\n",
    "    model.to(device)\n",
    "    model.eval()\n",
    "    output = model(image_input)\n",
    "    ps = torch.exp(output)\n",
    "    top_p, top_class = ps.topk(top_k)\n",
    "    # Detach from tensor and convert to numpy\n",
    "    top_p = top_p.detach().cpu().numpy().tolist()[0]\n",
    "    top_class = top_class.detach().cpu().numpy().tolist()[0]\n",
    "\n",
    "    #model.class_to_idx has mapping from class->idx. So reverse it to idx->class/cat\n",
    "    idx_to_class = {}\n",
    "    for key, value in model.class_to_idx.items():\n",
    "        idx_to_class[value] = key\n",
    "    #Now iterate idx->class to get top 5 classes\n",
    "    top_c = []\n",
    "    for clas in top_class:\n",
    "        top_c.append(idx_to_class[clas])\n",
    "\n",
    "    return top_p, top_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.9954708218574524,\n",
       "  0.0026475258637219667,\n",
       "  0.0007794779958203435,\n",
       "  0.00030464856536127627,\n",
       "  0.0002232319675385952],\n",
       " ['19', '55', '51', '1', '44'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imagepred_path = data_dir + '/test/19/image_06196.jpg'\n",
    "\n",
    "predict(imagepred_path, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 testing 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probs: [0.9954708218574524, 0.0026475258637219667, 0.0007794779958203435, 0.00030464856536127627, 0.0002232319675385952]\n",
      "Class Names:  ['balloon flower', 'pelargonium', 'petunia', 'pink primrose', 'poinsettia']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fecc5503cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 428
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "\n",
    "def sanity_check(image_path):\n",
    "\n",
    "#     fig = plt.figure(figsize=(8,4))\n",
    "    image_title_cat = image_path.split('/')[2]\n",
    "    image_title = cat_to_name[image_title_cat]\n",
    "    probs, classes = predict(image_path, model)\n",
    "    \n",
    "    class_list = []\n",
    "    for clas in classes:\n",
    "        class_list.append(cat_to_name[clas])\n",
    "    \n",
    "    print(\"Probs:\", probs)\n",
    "    print(\"Class Names: \", class_list)\n",
    "    ax1 = plt.subplot(2,2,1)\n",
    "#     fig, ax1 = plt.subplot(1, figsize=(12, 4))\n",
    "    pil_im = Image.open(image_path)\n",
    "    ax1.set_title(image_title)\n",
    "    ax1.imshow(pil_im)\n",
    "    \n",
    "    ax2 = plt.subplot(2,1,2)\n",
    "    y_pos = np.arange(len(class_list))\n",
    "    yvals = np.asarray(class_list)\n",
    "    xvals = np.asarray(probs)\n",
    "\n",
    "    plt.barh(y_pos, xvals[::-1], color='royalblue', align='center', alpha=0.5)\n",
    "    plt.yticks(y_pos, yvals[::-1])\n",
    "    plt.xlabel('probabilities')\n",
    "#     plt.title('')\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "    \n",
    "image_pathsan = 'flowers/test/19/image_06196.jpg'\n",
    "sanity_check(image_pathsan)\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}