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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports here\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, transforms, models\n",
    "from workspace_utils import keep_awake\n",
    "import numpy as np\n",
    "from PIL import Image"
   ]
  },
  {
   "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": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define your transforms for the training, validation, and testing sets\n",
    "train_transforms = transforms.Compose([transforms.Resize(224),\n",
    "                                      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",
    "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_datasets = datasets.ImageFolder(train_dir,transform = train_transforms) \n",
    "test_datasets = datasets.ImageFolder(test_dir,transform = test_transforms) \n",
    "valid_datasets = datasets.ImageFolder(valid_dir,transform = test_transforms) \n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "train_dataloader = torch.utils.data.DataLoader(train_datasets,batch_size=64,shuffle=True)\n",
    "test_dataloader = torch.utils.data.DataLoader(test_datasets,batch_size=64,shuffle=True)\n",
    "valid_dataloader = torch.utils.data.DataLoader(valid_datasets,batch_size=64,shuffle=True)"
   ]
  },
  {
   "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": 53,
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.torch/models/vgg16-397923af.pth\n",
      "100%|██████████| 553433881/553433881 [00:05<00:00, 102336904.74it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace)\n",
       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace)\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU(inplace)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace)\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Dropout(p=0.5)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (4): ReLU(inplace)\n",
       "    (5): Dropout(p=0.5)\n",
       "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = models.vgg16(pretrained=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace)\n",
       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace)\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU(inplace)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace)\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (dropout): Dropout(p=0.3)\n",
       "    (fc1): Linear(in_features=25088, out_features=120, bias=True)\n",
       "    (relu): ReLU()\n",
       "    (fc2): Linear(in_features=120, out_features=90, bias=True)\n",
       "    (relu2): ReLU()\n",
       "    (fc3): Linear(in_features=90, out_features=80, bias=True)\n",
       "    (relu3): ReLU()\n",
       "    (fc4): Linear(in_features=80, out_features=102, bias=True)\n",
       "    (output): LogSoftmax()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "    \n",
    "from collections import OrderedDict\n",
    "classifier = nn.Sequential(OrderedDict([\n",
    "                          ('dropout',nn.Dropout(0.3)),\n",
    "                          ('fc1',nn.Linear(25088,120)),\n",
    "                          ('relu',nn.ReLU()),\n",
    "                          ('fc2',nn.Linear(120,90)),\n",
    "                          ('relu2',nn.ReLU()),\n",
    "                          ('fc3',nn.Linear(90,80)),\n",
    "                          ('relu3',nn.ReLU()),\n",
    "                          ('fc4',nn.Linear(80,102)),\n",
    "                          ('output', nn.LogSoftmax(dim=1))\n",
    "                          ]))\n",
    "\n",
    "model.classifier = classifier\n",
    "\n",
    "criterion = nn.NLLLoss()\n",
    "\n",
    "optimizer = optim.Adam(model.classifier.parameters(),lr=0.001)\n",
    "\n",
    "model.to(device)\n",
    "model.class_to_idx = train_datasets.class_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15.. Train loss: 4.615.. Validation loss: 4.558.. Vaildation accuracy: 0.039\n",
      "Epoch 1/15.. Train loss: 4.548.. Validation loss: 4.463.. Vaildation accuracy: 0.068\n",
      "Epoch 1/15.. Train loss: 4.468.. Validation loss: 4.335.. Vaildation accuracy: 0.106\n",
      "Epoch 1/15.. Train loss: 4.318.. Validation loss: 4.181.. Vaildation accuracy: 0.147\n",
      "Epoch 1/15.. Train loss: 4.271.. Validation loss: 3.985.. Vaildation accuracy: 0.170\n",
      "Epoch 1/15.. Train loss: 3.998.. Validation loss: 3.887.. Vaildation accuracy: 0.149\n",
      "Epoch 1/15.. Train loss: 3.996.. Validation loss: 3.628.. Vaildation accuracy: 0.226\n",
      "Epoch 1/15.. Train loss: 3.848.. Validation loss: 3.487.. Vaildation accuracy: 0.212\n",
      "Epoch 1/15.. Train loss: 3.707.. Validation loss: 3.280.. Vaildation accuracy: 0.265\n",
      "Epoch 1/15.. Train loss: 3.612.. Validation loss: 3.128.. Vaildation accuracy: 0.303\n",
      "Epoch 1/15.. Train loss: 3.330.. Validation loss: 3.077.. Vaildation accuracy: 0.303\n",
      "Epoch 1/15.. Train loss: 3.229.. Validation loss: 2.739.. Vaildation accuracy: 0.377\n",
      "Epoch 1/15.. Train loss: 3.079.. Validation loss: 2.737.. Vaildation accuracy: 0.362\n",
      "Epoch 1/15.. Train loss: 2.935.. Validation loss: 2.520.. Vaildation accuracy: 0.409\n",
      "Epoch 1/15.. Train loss: 2.788.. Validation loss: 2.372.. Vaildation accuracy: 0.436\n",
      "Epoch 1/15.. Train loss: 2.438.. Validation loss: 2.304.. Vaildation accuracy: 0.420\n",
      "Epoch 1/15.. Train loss: 2.700.. Validation loss: 2.131.. Vaildation accuracy: 0.447\n",
      "Epoch 1/15.. Train loss: 2.424.. Validation loss: 2.170.. Vaildation accuracy: 0.439\n",
      "Epoch 1/15.. Train loss: 2.346.. Validation loss: 2.017.. Vaildation accuracy: 0.483\n",
      "Epoch 1/15.. Train loss: 2.652.. Validation loss: 1.918.. Vaildation accuracy: 0.493\n",
      "Epoch 2/15.. Train loss: 2.412.. Validation loss: 1.922.. Vaildation accuracy: 0.504\n",
      "Epoch 2/15.. Train loss: 2.040.. Validation loss: 1.811.. Vaildation accuracy: 0.533\n",
      "Epoch 2/15.. Train loss: 2.260.. Validation loss: 1.811.. Vaildation accuracy: 0.518\n",
      "Epoch 2/15.. Train loss: 2.135.. Validation loss: 1.873.. Vaildation accuracy: 0.505\n",
      "Epoch 2/15.. Train loss: 2.084.. Validation loss: 1.590.. Vaildation accuracy: 0.570\n",
      "Epoch 2/15.. Train loss: 2.039.. Validation loss: 1.670.. Vaildation accuracy: 0.530\n",
      "Epoch 2/15.. Train loss: 1.895.. Validation loss: 1.511.. Vaildation accuracy: 0.583\n",
      "Epoch 2/15.. Train loss: 1.706.. Validation loss: 1.691.. Vaildation accuracy: 0.540\n",
      "Epoch 2/15.. Train loss: 1.896.. Validation loss: 1.497.. Vaildation accuracy: 0.579\n",
      "Epoch 2/15.. Train loss: 1.851.. Validation loss: 1.434.. Vaildation accuracy: 0.595\n",
      "Epoch 2/15.. Train loss: 1.727.. Validation loss: 1.503.. Vaildation accuracy: 0.584\n",
      "Epoch 2/15.. Train loss: 1.777.. Validation loss: 1.372.. Vaildation accuracy: 0.620\n",
      "Epoch 2/15.. Train loss: 1.933.. Validation loss: 1.387.. Vaildation accuracy: 0.598\n",
      "Epoch 2/15.. Train loss: 1.746.. Validation loss: 1.416.. Vaildation accuracy: 0.615\n",
      "Epoch 2/15.. Train loss: 1.677.. Validation loss: 1.198.. Vaildation accuracy: 0.663\n",
      "Epoch 2/15.. Train loss: 1.497.. Validation loss: 1.284.. Vaildation accuracy: 0.651\n",
      "Epoch 2/15.. Train loss: 1.812.. Validation loss: 1.196.. Vaildation accuracy: 0.662\n",
      "Epoch 2/15.. Train loss: 1.658.. Validation loss: 1.207.. Vaildation accuracy: 0.645\n",
      "Epoch 2/15.. Train loss: 1.620.. Validation loss: 1.270.. Vaildation accuracy: 0.643\n",
      "Epoch 2/15.. Train loss: 1.557.. Validation loss: 1.310.. Vaildation accuracy: 0.634\n",
      "Epoch 2/15.. Train loss: 1.590.. Validation loss: 1.171.. Vaildation accuracy: 0.666\n",
      "Epoch 3/15.. Train loss: 1.479.. Validation loss: 1.103.. Vaildation accuracy: 0.679\n",
      "Epoch 3/15.. Train loss: 1.620.. Validation loss: 1.188.. Vaildation accuracy: 0.657\n",
      "Epoch 3/15.. Train loss: 1.424.. Validation loss: 1.141.. Vaildation accuracy: 0.680\n",
      "Epoch 3/15.. Train loss: 1.464.. Validation loss: 1.161.. Vaildation accuracy: 0.671\n",
      "Epoch 3/15.. Train loss: 1.459.. Validation loss: 1.058.. Vaildation accuracy: 0.680\n",
      "Epoch 3/15.. Train loss: 1.690.. Validation loss: 1.200.. Vaildation accuracy: 0.659\n",
      "Epoch 3/15.. Train loss: 1.540.. Validation loss: 1.073.. Vaildation accuracy: 0.689\n",
      "Epoch 3/15.. Train loss: 1.338.. Validation loss: 1.043.. Vaildation accuracy: 0.695\n",
      "Epoch 3/15.. Train loss: 1.480.. Validation loss: 1.040.. Vaildation accuracy: 0.697\n",
      "Epoch 3/15.. Train loss: 1.532.. Validation loss: 1.004.. Vaildation accuracy: 0.702\n",
      "Epoch 3/15.. Train loss: 1.525.. Validation loss: 1.063.. Vaildation accuracy: 0.686\n",
      "Epoch 3/15.. Train loss: 1.462.. Validation loss: 0.975.. Vaildation accuracy: 0.715\n",
      "Epoch 3/15.. Train loss: 1.276.. Validation loss: 1.083.. Vaildation accuracy: 0.698\n",
      "Epoch 3/15.. Train loss: 1.455.. Validation loss: 1.026.. Vaildation accuracy: 0.719\n",
      "Epoch 3/15.. Train loss: 1.316.. Validation loss: 0.949.. Vaildation accuracy: 0.731\n",
      "Epoch 3/15.. Train loss: 1.499.. Validation loss: 0.905.. Vaildation accuracy: 0.754\n",
      "Epoch 3/15.. Train loss: 1.378.. Validation loss: 0.932.. Vaildation accuracy: 0.736\n",
      "Epoch 3/15.. Train loss: 1.261.. Validation loss: 1.119.. Vaildation accuracy: 0.681\n",
      "Epoch 3/15.. Train loss: 1.340.. Validation loss: 0.937.. Vaildation accuracy: 0.728\n",
      "Epoch 3/15.. Train loss: 1.345.. Validation loss: 0.950.. Vaildation accuracy: 0.731\n",
      "Epoch 4/15.. Train loss: 1.359.. Validation loss: 1.042.. Vaildation accuracy: 0.703\n",
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      "Epoch 5/15.. Train loss: 1.132.. Validation loss: 0.741.. Vaildation accuracy: 0.790\n",
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      "Epoch 5/15.. Train loss: 1.200.. Validation loss: 0.801.. Vaildation accuracy: 0.785\n",
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      "Epoch 5/15.. Train loss: 1.055.. Validation loss: 0.766.. Vaildation accuracy: 0.789\n",
      "Epoch 5/15.. Train loss: 0.889.. Validation loss: 0.822.. Vaildation accuracy: 0.780\n",
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      "Epoch 5/15.. Train loss: 1.201.. Validation loss: 0.745.. Vaildation accuracy: 0.796\n",
      "Epoch 5/15.. Train loss: 0.974.. Validation loss: 0.713.. Vaildation accuracy: 0.788\n",
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      "Epoch 5/15.. Train loss: 1.042.. Validation loss: 0.789.. Vaildation accuracy: 0.790\n",
      "Epoch 5/15.. Train loss: 1.166.. Validation loss: 0.813.. Vaildation accuracy: 0.784\n",
      "Epoch 6/15.. Train loss: 1.188.. Validation loss: 0.750.. Vaildation accuracy: 0.780\n",
      "Epoch 6/15.. Train loss: 0.894.. Validation loss: 0.757.. Vaildation accuracy: 0.788\n",
      "Epoch 6/15.. Train loss: 1.000.. Validation loss: 0.769.. Vaildation accuracy: 0.795\n",
      "Epoch 6/15.. Train loss: 1.255.. Validation loss: 0.727.. Vaildation accuracy: 0.801\n",
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      "Epoch 6/15.. Train loss: 1.123.. Validation loss: 0.721.. Vaildation accuracy: 0.798\n",
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      "Epoch 6/15.. Train loss: 1.120.. Validation loss: 0.702.. Vaildation accuracy: 0.787\n",
      "Epoch 7/15.. Train loss: 1.073.. Validation loss: 0.703.. Vaildation accuracy: 0.807\n",
      "Epoch 7/15.. Train loss: 0.989.. Validation loss: 0.742.. Vaildation accuracy: 0.790\n",
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      "Epoch 7/15.. Train loss: 0.950.. Validation loss: 0.631.. Vaildation accuracy: 0.818\n",
      "Epoch 7/15.. Train loss: 0.980.. Validation loss: 0.593.. Vaildation accuracy: 0.837\n",
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      "Epoch 7/15.. Train loss: 0.970.. Validation loss: 0.727.. Vaildation accuracy: 0.799\n",
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      "Epoch 7/15.. Train loss: 1.076.. Validation loss: 0.733.. Vaildation accuracy: 0.798\n",
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      "Epoch 7/15.. Train loss: 0.909.. Validation loss: 0.672.. Vaildation accuracy: 0.820\n",
      "Epoch 8/15.. Train loss: 0.970.. Validation loss: 0.704.. Vaildation accuracy: 0.808\n",
      "Epoch 8/15.. Train loss: 0.857.. Validation loss: 0.733.. Vaildation accuracy: 0.801\n",
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      "Epoch 8/15.. Train loss: 0.992.. Validation loss: 0.685.. Vaildation accuracy: 0.805\n",
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      "Epoch 8/15.. Train loss: 1.041.. Validation loss: 0.676.. Vaildation accuracy: 0.815\n",
      "Epoch 8/15.. Train loss: 0.950.. Validation loss: 0.769.. Vaildation accuracy: 0.797\n",
      "Epoch 8/15.. Train loss: 0.975.. Validation loss: 0.612.. Vaildation accuracy: 0.818\n",
      "Epoch 9/15.. Train loss: 0.959.. Validation loss: 0.625.. Vaildation accuracy: 0.830\n",
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      "Epoch 10/15.. Train loss: 0.952.. Validation loss: 0.645.. Vaildation accuracy: 0.829\n",
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      "Epoch 11/15.. Train loss: 0.738.. Validation loss: 0.546.. Vaildation accuracy: 0.837\n",
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      "Epoch 11/15.. Train loss: 1.020.. Validation loss: 0.562.. Vaildation accuracy: 0.836\n",
      "Epoch 12/15.. Train loss: 0.804.. Validation loss: 0.610.. Vaildation accuracy: 0.827\n",
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      "Epoch 13/15.. Train loss: 0.783.. Validation loss: 0.679.. Vaildation accuracy: 0.817\n",
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      "Epoch 14/15.. Train loss: 0.814.. Validation loss: 0.591.. Vaildation accuracy: 0.831\n",
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      "Epoch 14/15.. Train loss: 0.782.. Validation loss: 0.581.. Vaildation accuracy: 0.845\n",
      "Epoch 14/15.. Train loss: 0.824.. Validation loss: 0.583.. Vaildation accuracy: 0.844\n",
      "Epoch 14/15.. Train loss: 0.718.. Validation loss: 0.623.. Vaildation accuracy: 0.833\n",
      "Epoch 14/15.. Train loss: 0.819.. Validation loss: 0.607.. Vaildation accuracy: 0.844\n",
      "Epoch 14/15.. Train loss: 0.884.. Validation loss: 0.557.. Vaildation accuracy: 0.849\n",
      "Epoch 14/15.. Train loss: 0.821.. Validation loss: 0.570.. Vaildation accuracy: 0.844\n",
      "Epoch 14/15.. Train loss: 0.906.. Validation loss: 0.576.. Vaildation accuracy: 0.837\n",
      "Epoch 14/15.. Train loss: 0.831.. Validation loss: 0.534.. Vaildation accuracy: 0.855\n",
      "Epoch 14/15.. Train loss: 0.800.. Validation loss: 0.565.. Vaildation accuracy: 0.833\n",
      "Epoch 14/15.. Train loss: 0.878.. Validation loss: 0.549.. Vaildation accuracy: 0.839\n",
      "Epoch 15/15.. Train loss: 0.802.. Validation loss: 0.578.. Vaildation accuracy: 0.838\n",
      "Epoch 15/15.. Train loss: 0.741.. Validation loss: 0.539.. Vaildation accuracy: 0.845\n",
      "Epoch 15/15.. Train loss: 0.685.. Validation loss: 0.534.. Vaildation accuracy: 0.852\n",
      "Epoch 15/15.. Train loss: 0.847.. Validation loss: 0.572.. Vaildation accuracy: 0.842\n",
      "Epoch 15/15.. Train loss: 0.706.. Validation loss: 0.611.. Vaildation accuracy: 0.836\n",
      "Epoch 15/15.. Train loss: 0.728.. Validation loss: 0.535.. Vaildation accuracy: 0.847\n",
      "Epoch 15/15.. Train loss: 0.679.. Validation loss: 0.560.. Vaildation accuracy: 0.844\n",
      "Epoch 15/15.. Train loss: 0.593.. Validation loss: 0.578.. Vaildation accuracy: 0.843\n",
      "Epoch 15/15.. Train loss: 0.830.. Validation loss: 0.578.. Vaildation accuracy: 0.845\n",
      "Epoch 15/15.. Train loss: 0.745.. Validation loss: 0.565.. Vaildation accuracy: 0.841\n",
      "Epoch 15/15.. Train loss: 0.699.. Validation loss: 0.539.. Vaildation accuracy: 0.843\n",
      "Epoch 15/15.. Train loss: 0.763.. Validation loss: 0.541.. Vaildation accuracy: 0.852\n",
      "Epoch 15/15.. Train loss: 0.752.. Validation loss: 0.577.. Vaildation accuracy: 0.844\n",
      "Epoch 15/15.. Train loss: 0.848.. Validation loss: 0.599.. Vaildation accuracy: 0.840\n",
      "Epoch 15/15.. Train loss: 0.848.. Validation loss: 0.612.. Vaildation accuracy: 0.835\n",
      "Epoch 15/15.. Train loss: 0.865.. Validation loss: 0.523.. Vaildation accuracy: 0.850\n",
      "Epoch 15/15.. Train loss: 0.873.. Validation loss: 0.513.. Vaildation accuracy: 0.857\n",
      "Epoch 15/15.. Train loss: 0.805.. Validation loss: 0.502.. Vaildation accuracy: 0.852\n",
      "Epoch 15/15.. Train loss: 0.690.. Validation loss: 0.543.. Vaildation accuracy: 0.851\n",
      "Epoch 15/15.. Train loss: 0.650.. Validation loss: 0.539.. Vaildation accuracy: 0.851\n",
      "Epoch 15/15.. Train loss: 0.767.. Validation loss: 0.515.. Vaildation accuracy: 0.862\n"
     ]
    }
   ],
   "source": [
    "# TODO: Build and train your network\n",
    "epochs = 15\n",
    "steps = 0\n",
    "running_loss = 0\n",
    "running_losses = 0\n",
    "print_every = 5\n",
    "\n",
    "for epoch in keep_awake(range(epochs)):\n",
    "    for images, labels in train_dataloader:\n",
    "        steps += 1\n",
    "        \n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        logps = model.forward(images)\n",
    "        loss = criterion(logps,labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        running_losses += loss.item()\n",
    "        \n",
    "        if steps % print_every == 0:\n",
    "            test_loss = 0\n",
    "            accuracy = 0\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                for images, labels in valid_dataloader:\n",
    "                    images, labels = images.to(device), labels.to(device)\n",
    "                    logps = model.forward(images)\n",
    "                    batch_loss = criterion(logps,labels)\n",
    "                    \n",
    "                    test_loss += batch_loss.item() \n",
    "                    \n",
    "                    # Calculate accuracy\n",
    "                    ps = torch.exp(logps)\n",
    "                    top_p, top_class = ps.topk(1, dim=1)\n",
    "                    equals = top_class == labels.view(*top_class.shape)\n",
    "                    accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "                  \n",
    "            \n",
    "            print(f\"Epoch {epoch+1}/{epochs}.. \"\n",
    "                  f\"Train loss: {running_loss/print_every:.3f}.. \"\n",
    "                  f\"Validation loss: {test_loss/len(valid_dataloader):.3f}.. \"\n",
    "                  f\"Vaildation accuracy: {accuracy/len(valid_dataloader):.3f}\")\n",
    "            running_loss = 0\n",
    "            model.train()                       "
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy is: 85.96%\n"
     ]
    }
   ],
   "source": [
    "# # TODO: Do validation on the test set\n",
    "\n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for images, labels in test_dataloader: \n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        output = model(images)\n",
    "        _, predicted = torch.max(output.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "\n",
    "print(\"Test accuracy is: \"+\"{:.2%}\".format(correct / total))\n",
    "# print('Test accuracy is {}'.format(correct / total));    "
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Save the checkpoint \n",
    "def save ():\n",
    "    checkpoint = {'input_size': 25088,\n",
    "                  'output_size': 102,\n",
    "                  'dropout': 0.3,\n",
    "                  'lr': 0.001,\n",
    "                  'arch': 'vgg16',   \n",
    "                  'hidden_units': [120,90,80],\n",
    "                  'optimzer_state': optimizer.state_dict,\n",
    "                  'class_to_idx':  train_datasets.class_to_idx,\n",
    "                  'state_dict': model.classifier.state_dict()}\n",
    "\n",
    "    torch.save(checkpoint, 'checkpoint.pth')\n",
    "    \n",
    "save()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "model = models.vgg16(pretrained=True)\n",
    "\n",
    "def load_checkpoint(filepath):\n",
    "    checkpoint = torch.load(filepath)\n",
    "    model = nn.Sequential(OrderedDict([\n",
    "                          ('fc1',nn.Linear(checkpoint['input_size'],checkpoint['hidden_layers'][0])),\n",
    "                          ('relu',nn.ReLU()),\n",
    "                          ('dropout',nn.Dropout(checkpoint['dropout'])),\n",
    "                          ('fc2',nn.Linear(checkpoint['hidden_layers'][0],checkpoint['hidden_layers'][1])),\n",
    "                          ('relu2',nn.ReLU()),\n",
    "                          ('fc3',nn.Linear(checkpoint['hidden_layers'][1],checkpoint['hidden_layers'][2])),\n",
    "                          ('relu3',nn.ReLU()),\n",
    "                          ('fc4',nn.Linear(checkpoint['hidden_layers'][2],checkpoint['output_size'])),\n",
    "                          ('output', nn.LogSoftmax(dim=1))\n",
    "                          ]))\n",
    "    \n",
    "                           \n",
    "    model.load_state_dict(checkpoint['state_dict'])\n",
    "    model.class_to_idx = checkpoint['class_to_idx']\n",
    "    model.to(device)\n",
    "    \n",
    "    optimizer = checkpoint['optimzer_state']\n",
    "    \n",
    "    return model\n",
    "\n",
    "model.classifier =  load_checkpoint('checkpoint.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 14,
   "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",
    "    im = Image.open(image)\n",
    "    im.resize((256,256))\n",
    "    processed_image = transforms.Compose([transforms.CenterCrop(224),\n",
    "                        transforms.ToTensor(),\n",
    "                        transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                            [0.229, 0.224, 0.225])])\n",
    "    return processed_image(im)\n",
    "    \n",
    "    \n",
    "    # TODO: Process a PIL image for use in a PyTorch model"
   ]
  },
  {
   "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": 15,
   "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",
    "    \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": "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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def predict(image_path, model, topk=5):\n",
    "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
    "    '''\n",
    "    \n",
    "    # TODO: Implement the code to predict the class from an image file\n",
    "    image = process_image(image_path)\n",
    "    image = image.unsqueeze_(0)\n",
    "    image = image.cuda().float()\n",
    "    \n",
    "    model.eval()\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        output = model(image)\n",
    "        prob, idxs = torch.topk(output, topk)\n",
    "       \n",
    "    \n",
    "        idxs = np.array(idxs)            \n",
    "        idx_to_class = {val:key for key, val in model.class_to_idx.items()}\n",
    "        classes = [idx_to_class[idx] for idx in idxs[0]]\n",
    "        \n",
    "        \n",
    "        names = []\n",
    "        for cls in classes:\n",
    "            names.append(cat_to_name[str(cls)])\n",
    "        \n",
    "        return prob, names"
   ]
  },
  {
   "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73181c45f8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 259
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73181c4668>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 336
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "img_path = test_dir+'/100'+'/image_07896.jpg'\n",
    "x_pos, y_pos = predict(img_path, model, topk=5)\n",
    "\n",
    "ax_img = imshow(process_image(img_path))\n",
    "ax_img.set_title(y_pos[0])\n",
    "ax_img.axis('off')\n",
    "\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.barh(range(len(y_pos)), np.exp(x_pos[0]))\n",
    "plt.yticks(range(len(y_pos)), y_pos)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}