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": [
    "# Imports here\n",
    "\n",
    "import torch\n",
    "import json\n",
    "import time\n",
    "from torchvision import datasets, transforms, models\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import OrderedDict\n",
    "from workspace_utils import active_session\n",
    "from PIL import Image\n"
   ]
  },
  {
   "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",
    "data_transforms = {'train_transform': 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",
    "                  'valid_test_transform': 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",
    "image_datasets = {'train_dataset': datasets.ImageFolder(train_dir, transform=data_transforms['train_transform']),\n",
    "                 'valid_dataset': datasets.ImageFolder(valid_dir, transform=data_transforms['valid_test_transform']),\n",
    "                 'test_dataset': datasets.ImageFolder(test_dir, transform=data_transforms['valid_test_transform'])}\n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "trainloader = torch.utils.data.DataLoader(image_datasets['train_dataset'], batch_size=64, shuffle=True)\n",
    "validloader = torch.utils.data.DataLoader(image_datasets['valid_dataset'], batch_size=64)\n",
    "testloader = torch.utils.data.DataLoader(image_datasets['test_dataset'], 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": [
    "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": 5,
   "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, 96627048.39it/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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Build and train your network\n",
    "\n",
    "# Import pre-trained network, print out parameters\n",
    "\n",
    "model = models.vgg16(pretrained=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# freeze feature detector parameters\n",
    "\n",
    "for parameter in model.parameters():\n",
    "    parameter.requires_grad = False"
   ]
  },
  {
   "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",
       "    (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): Sequential(\n",
       "      (fc1): Linear(in_features=4096, out_features=256, bias=True)\n",
       "      (relu): ReLU()\n",
       "      (dropout): Dropout(p=0.5)\n",
       "      (fc2): Linear(in_features=256, out_features=102, bias=True)\n",
       "      (output): LogSoftmax()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build new classifier, slap new classifier onto last step of pretrained network, print out parameters to confirm change\n",
    "\n",
    "new_classifier = nn.Sequential(OrderedDict([('fc1', nn.Linear(4096, 256)),\n",
    "                                       ('relu', nn.ReLU()),\n",
    "                                       ('dropout', nn.Dropout()),\n",
    "                                       ('fc2', nn.Linear(256, 102)),\n",
    "                                       ('output', nn.LogSoftmax(dim=1))]))\n",
    "\n",
    "model.classifier[6] = new_classifier\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "    (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): Sequential(\n",
       "      (fc1): Linear(in_features=4096, out_features=256, bias=True)\n",
       "      (relu): ReLU()\n",
       "      (dropout): Dropout(p=0.5)\n",
       "      (fc2): Linear(in_features=256, out_features=102, bias=True)\n",
       "      (output): LogSoftmax()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now that classifier is built, setup loss function and optimizer\n",
    "\n",
    "criterion = nn.NLLLoss()\n",
    "\n",
    "optimizer = optim.Adam(model.classifier[6].parameters())\n",
    "    \n",
    "# setup device for GPU usage when enabled\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "--------------------\n",
      "\n",
      "                    Step: 6..\n",
      "                    Training Loss: 4.593...\n",
      "                    Training Accuracy: 0.109\n",
      "                    Validation Loss: 4.437...\n",
      "                    Validation Accuracy: 0.064\n",
      "                    Time Elapsed: 0m 27s\n",
      "                \n",
      "\n",
      "                    Step: 11..\n",
      "                    Training Loss: 4.491...\n",
      "                    Training Accuracy: 0.078\n",
      "                    Validation Loss: 4.256...\n",
      "                    Validation Accuracy: 0.099\n",
      "                    Time Elapsed: 0m 53s\n",
      "                \n",
      "\n",
      "                    Step: 16..\n",
      "                    Training Loss: 4.272...\n",
      "                    Training Accuracy: 0.109\n",
      "                    Validation Loss: 4.071...\n",
      "                    Validation Accuracy: 0.166\n",
      "                    Time Elapsed: 1m 19s\n",
      "                \n",
      "\n",
      "                    Step: 21..\n",
      "                    Training Loss: 4.209...\n",
      "                    Training Accuracy: 0.125\n",
      "                    Validation Loss: 3.877...\n",
      "                    Validation Accuracy: 0.189\n",
      "                    Time Elapsed: 1m 44s\n",
      "                \n",
      "\n",
      "                    Step: 26..\n",
      "                    Training Loss: 4.090...\n",
      "                    Training Accuracy: 0.156\n",
      "                    Validation Loss: 3.692...\n",
      "                    Validation Accuracy: 0.268\n",
      "                    Time Elapsed: 2m 10s\n",
      "                \n",
      "\n",
      "                    Step: 31..\n",
      "                    Training Loss: 3.898...\n",
      "                    Training Accuracy: 0.125\n",
      "                    Validation Loss: 3.499...\n",
      "                    Validation Accuracy: 0.286\n",
      "                    Time Elapsed: 2m 35s\n",
      "                \n",
      "\n",
      "                    Step: 36..\n",
      "                    Training Loss: 3.635...\n",
      "                    Training Accuracy: 0.219\n",
      "                    Validation Loss: 3.296...\n",
      "                    Validation Accuracy: 0.331\n",
      "                    Time Elapsed: 3m 1s\n",
      "                \n",
      "\n",
      "                    Step: 41..\n",
      "                    Training Loss: 3.551...\n",
      "                    Training Accuracy: 0.188\n",
      "                    Validation Loss: 3.085...\n",
      "                    Validation Accuracy: 0.351\n",
      "                    Time Elapsed: 3m 27s\n",
      "                \n",
      "\n",
      "                    Step: 46..\n",
      "                    Training Loss: 3.410...\n",
      "                    Training Accuracy: 0.375\n",
      "                    Validation Loss: 2.910...\n",
      "                    Validation Accuracy: 0.381\n",
      "                    Time Elapsed: 3m 52s\n",
      "                \n",
      "\n",
      "                    Step: 51..\n",
      "                    Training Loss: 3.089...\n",
      "                    Training Accuracy: 0.375\n",
      "                    Validation Loss: 2.740...\n",
      "                    Validation Accuracy: 0.414\n",
      "                    Time Elapsed: 4m 18s\n",
      "                \n",
      "\n",
      "                    Step: 56..\n",
      "                    Training Loss: 3.149...\n",
      "                    Training Accuracy: 0.219\n",
      "                    Validation Loss: 2.578...\n",
      "                    Validation Accuracy: 0.450\n",
      "                    Time Elapsed: 4m 44s\n",
      "                \n",
      "\n",
      "                    Step: 61..\n",
      "                    Training Loss: 2.852...\n",
      "                    Training Accuracy: 0.453\n",
      "                    Validation Loss: 2.428...\n",
      "                    Validation Accuracy: 0.503\n",
      "                    Time Elapsed: 5m 9s\n",
      "                \n",
      "\n",
      "                    Step: 66..\n",
      "                    Training Loss: 2.913...\n",
      "                    Training Accuracy: 0.297\n",
      "                    Validation Loss: 2.310...\n",
      "                    Validation Accuracy: 0.518\n",
      "                    Time Elapsed: 5m 34s\n",
      "                \n",
      "\n",
      "                    Step: 71..\n",
      "                    Training Loss: 2.745...\n",
      "                    Training Accuracy: 0.344\n",
      "                    Validation Loss: 2.188...\n",
      "                    Validation Accuracy: 0.536\n",
      "                    Time Elapsed: 5m 60s\n",
      "                \n",
      "\n",
      "                    Step: 76..\n",
      "                    Training Loss: 2.772...\n",
      "                    Training Accuracy: 0.422\n",
      "                    Validation Loss: 2.122...\n",
      "                    Validation Accuracy: 0.532\n",
      "                    Time Elapsed: 6m 25s\n",
      "                \n",
      "\n",
      "                    Step: 81..\n",
      "                    Training Loss: 2.569...\n",
      "                    Training Accuracy: 0.328\n",
      "                    Validation Loss: 2.020...\n",
      "                    Validation Accuracy: 0.539\n",
      "                    Time Elapsed: 6m 50s\n",
      "                \n",
      "\n",
      "                    Step: 86..\n",
      "                    Training Loss: 2.362...\n",
      "                    Training Accuracy: 0.422\n",
      "                    Validation Loss: 1.904...\n",
      "                    Validation Accuracy: 0.576\n",
      "                    Time Elapsed: 7m 16s\n",
      "                \n",
      "\n",
      "                    Step: 91..\n",
      "                    Training Loss: 2.523...\n",
      "                    Training Accuracy: 0.469\n",
      "                    Validation Loss: 1.859...\n",
      "                    Validation Accuracy: 0.574\n",
      "                    Time Elapsed: 7m 41s\n",
      "                \n",
      "\n",
      "                    Step: 96..\n",
      "                    Training Loss: 2.422...\n",
      "                    Training Accuracy: 0.391\n",
      "                    Validation Loss: 1.845...\n",
      "                    Validation Accuracy: 0.562\n",
      "                    Time Elapsed: 8m 6s\n",
      "                \n",
      "\n",
      "                    Step: 101..\n",
      "                    Training Loss: 2.299...\n",
      "                    Training Accuracy: 0.469\n",
      "                    Validation Loss: 1.756...\n",
      "                    Validation Accuracy: 0.597\n",
      "                    Time Elapsed: 8m 31s\n",
      "                \n",
      "Epoch 2/2\n",
      "--------------------\n",
      "\n",
      "                    Step: 106..\n",
      "                    Training Loss: 2.240...\n",
      "                    Training Accuracy: 0.344\n",
      "                    Validation Loss: 1.681...\n",
      "                    Validation Accuracy: 0.622\n",
      "                    Time Elapsed: 8m 55s\n",
      "                \n",
      "\n",
      "                    Step: 111..\n",
      "                    Training Loss: 2.340...\n",
      "                    Training Accuracy: 0.422\n",
      "                    Validation Loss: 1.627...\n",
      "                    Validation Accuracy: 0.628\n",
      "                    Time Elapsed: 9m 20s\n",
      "                \n",
      "\n",
      "                    Step: 116..\n",
      "                    Training Loss: 2.253...\n",
      "                    Training Accuracy: 0.406\n",
      "                    Validation Loss: 1.576...\n",
      "                    Validation Accuracy: 0.649\n",
      "                    Time Elapsed: 9m 45s\n",
      "                \n",
      "\n",
      "                    Step: 121..\n",
      "                    Training Loss: 2.264...\n",
      "                    Training Accuracy: 0.516\n",
      "                    Validation Loss: 1.558...\n",
      "                    Validation Accuracy: 0.636\n",
      "                    Time Elapsed: 10m 10s\n",
      "                \n",
      "\n",
      "                    Step: 126..\n",
      "                    Training Loss: 2.073...\n",
      "                    Training Accuracy: 0.438\n",
      "                    Validation Loss: 1.564...\n",
      "                    Validation Accuracy: 0.619\n",
      "                    Time Elapsed: 10m 34s\n",
      "                \n",
      "\n",
      "                    Step: 131..\n",
      "                    Training Loss: 2.141...\n",
      "                    Training Accuracy: 0.500\n",
      "                    Validation Loss: 1.551...\n",
      "                    Validation Accuracy: 0.619\n",
      "                    Time Elapsed: 10m 59s\n",
      "                \n",
      "\n",
      "                    Step: 136..\n",
      "                    Training Loss: 1.988...\n",
      "                    Training Accuracy: 0.438\n",
      "                    Validation Loss: 1.502...\n",
      "                    Validation Accuracy: 0.625\n",
      "                    Time Elapsed: 11m 24s\n",
      "                \n",
      "\n",
      "                    Step: 141..\n",
      "                    Training Loss: 2.152...\n",
      "                    Training Accuracy: 0.469\n",
      "                    Validation Loss: 1.420...\n",
      "                    Validation Accuracy: 0.680\n",
      "                    Time Elapsed: 11m 49s\n",
      "                \n",
      "\n",
      "                    Step: 146..\n",
      "                    Training Loss: 2.003...\n",
      "                    Training Accuracy: 0.484\n",
      "                    Validation Loss: 1.374...\n",
      "                    Validation Accuracy: 0.679\n",
      "                    Time Elapsed: 12m 13s\n",
      "                \n",
      "\n",
      "                    Step: 151..\n",
      "                    Training Loss: 2.024...\n",
      "                    Training Accuracy: 0.328\n",
      "                    Validation Loss: 1.357...\n",
      "                    Validation Accuracy: 0.678\n",
      "                    Time Elapsed: 12m 38s\n",
      "                \n",
      "\n",
      "                    Step: 156..\n",
      "                    Training Loss: 1.960...\n",
      "                    Training Accuracy: 0.438\n",
      "                    Validation Loss: 1.330...\n",
      "                    Validation Accuracy: 0.680\n",
      "                    Time Elapsed: 13m 3s\n",
      "                \n",
      "\n",
      "                    Step: 161..\n",
      "                    Training Loss: 1.997...\n",
      "                    Training Accuracy: 0.469\n",
      "                    Validation Loss: 1.341...\n",
      "                    Validation Accuracy: 0.659\n",
      "                    Time Elapsed: 13m 28s\n",
      "                \n",
      "\n",
      "                    Step: 166..\n",
      "                    Training Loss: 2.024...\n",
      "                    Training Accuracy: 0.531\n",
      "                    Validation Loss: 1.299...\n",
      "                    Validation Accuracy: 0.681\n",
      "                    Time Elapsed: 13m 52s\n",
      "                \n",
      "\n",
      "                    Step: 171..\n",
      "                    Training Loss: 1.884...\n",
      "                    Training Accuracy: 0.516\n",
      "                    Validation Loss: 1.261...\n",
      "                    Validation Accuracy: 0.697\n",
      "                    Time Elapsed: 14m 17s\n",
      "                \n",
      "\n",
      "                    Step: 176..\n",
      "                    Training Loss: 1.900...\n",
      "                    Training Accuracy: 0.438\n",
      "                    Validation Loss: 1.243...\n",
      "                    Validation Accuracy: 0.713\n",
      "                    Time Elapsed: 14m 42s\n",
      "                \n",
      "\n",
      "                    Step: 181..\n",
      "                    Training Loss: 1.787...\n",
      "                    Training Accuracy: 0.594\n",
      "                    Validation Loss: 1.235...\n",
      "                    Validation Accuracy: 0.707\n",
      "                    Time Elapsed: 15m 6s\n",
      "                \n",
      "\n",
      "                    Step: 186..\n",
      "                    Training Loss: 2.085...\n",
      "                    Training Accuracy: 0.328\n",
      "                    Validation Loss: 1.230...\n",
      "                    Validation Accuracy: 0.711\n",
      "                    Time Elapsed: 15m 31s\n",
      "                \n",
      "\n",
      "                    Step: 191..\n",
      "                    Training Loss: 1.890...\n",
      "                    Training Accuracy: 0.578\n",
      "                    Validation Loss: 1.214...\n",
      "                    Validation Accuracy: 0.715\n",
      "                    Time Elapsed: 15m 56s\n",
      "                \n",
      "\n",
      "                    Step: 196..\n",
      "                    Training Loss: 1.788...\n",
      "                    Training Accuracy: 0.609\n",
      "                    Validation Loss: 1.191...\n",
      "                    Validation Accuracy: 0.712\n",
      "                    Time Elapsed: 16m 20s\n",
      "                \n",
      "\n",
      "                    Step: 201..\n",
      "                    Training Loss: 1.969...\n",
      "                    Training Accuracy: 0.453\n",
      "                    Validation Loss: 1.174...\n",
      "                    Validation Accuracy: 0.712\n",
      "                    Time Elapsed: 16m 45s\n",
      "                \n",
      "\n",
      "                    Step: 206..\n",
      "                    Training Loss: 1.968...\n",
      "                    Training Accuracy: 0.547\n",
      "                    Validation Loss: 1.159...\n",
      "                    Validation Accuracy: 0.720\n",
      "                    Time Elapsed: 17m 10s\n",
      "                \n"
     ]
    }
   ],
   "source": [
    "# Train new network, calculate loss and accuracy\n",
    "\n",
    "# Build in a way to save checkpoints if current batch settings produce best accuracy\n",
    "\n",
    "# Build in early stopping by killing training loop if 3 consecutive batches don't result in higher accuracy\n",
    "\n",
    "# Turn training and testing into separate functions\n",
    "\n",
    "with active_session():\n",
    "\n",
    "    since = time.time()\n",
    "    \n",
    "    epochs = 2\n",
    "    steps = 0\n",
    "    \n",
    "    running_loss = 0\n",
    "    updates = 5\n",
    "\n",
    "    train_losses, valid_losses = [], []\n",
    "\n",
    "    for e in range(epochs):\n",
    "        print(f\"Epoch {e+1}/{epochs}\")\n",
    "        print('-'*20)\n",
    "        for images, labels in trainloader:\n",
    "            steps += 1\n",
    "            acc_train = 0\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            log_ps = model(images)\n",
    "            \n",
    "            _, preds = torch.max(log_ps.data, 1)\n",
    "            \n",
    "            loss = criterion(log_ps, labels)\n",
    "            \n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss.item()\n",
    "            acc_train += torch.sum(preds == labels.data).item()/64\n",
    "\n",
    "            if steps % updates == 0:\n",
    "                valid_loss = 0\n",
    "                valid_accuracy = 0\n",
    "                model.eval()\n",
    "\n",
    "                with torch.no_grad():\n",
    "                    for images, labels in validloader:\n",
    "                        images, labels = images.to(device), labels.to(device)\n",
    "                        log_ps = model(images)\n",
    "                        batch_loss = criterion(log_ps, labels)\n",
    "                        valid_loss += batch_loss.item()\n",
    "\n",
    "                        ps = torch.exp(log_ps)\n",
    "                        top_p, top_class = ps.topk(1, dim=1)\n",
    "                        equals = top_class == labels.view(*top_class.shape)\n",
    "                        valid_accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "\n",
    "                train_losses.append(running_loss/len(trainloader))\n",
    "                valid_losses.append(valid_loss/len(validloader))\n",
    "\n",
    "                train_loss_out = running_loss/updates\n",
    "                train_accuracy_out = acc_train\n",
    "                valid_loss_out = valid_loss/len(validloader)\n",
    "                valid_accuracy_out = valid_accuracy/len(validloader)\n",
    "                \n",
    "                # save model checkpoint if these are best results\n",
    "                \n",
    "                #if valid_accuracy_out > best_acc:\n",
    "                    \n",
    "\n",
    "                print(f\"\"\"\n",
    "                    Step: {steps+1}..\n",
    "                    Training Loss: {train_loss_out:.3f}...\n",
    "                    Training Accuracy: {train_accuracy_out:.3f}\n",
    "                    Validation Loss: {valid_loss_out:.3f}...\n",
    "                    Validation Accuracy: {valid_accuracy_out:.3f}\n",
    "                    Time Elapsed: {((time.time() - since)//60):.0f}m {((time.time() - since) % 60):.0f}s\n",
    "                \"\"\")\n",
    "\n",
    "                running_loss = 0\n",
    "                acc_train = 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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Do validation on the test set\n",
    "\n",
    "def test_model(model, loader, criterion):\n",
    "    with active_session():\n",
    "        \n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        \n",
    "        model.to(device)\n",
    "        \n",
    "        model.eval()\n",
    "        \n",
    "        since = time.time()\n",
    "        \n",
    "        test_loss = 0\n",
    "        test_acc = 0\n",
    "        steps = 0        \n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for images, labels in testloader:\n",
    "                steps += 1\n",
    "                images, labels = images.to(device), labels.to(device)\n",
    "                log_ps = model(images)\n",
    "                batch_loss = criterion(log_ps, labels)\n",
    "                test_loss += batch_loss.item()\n",
    "\n",
    "                ps = torch.exp(log_ps)\n",
    "                top_p, top_class = ps.topk(1, dim=1)\n",
    "                equals = top_class == labels.view(*top_class.shape)\n",
    "                test_acc += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "        \n",
    "        print(f\"\"\"\n",
    "            Step: {steps+1}..\n",
    "            Test Loss: {(test_loss/len(loader)):.3f}...\n",
    "            Test Accuracy: {(test_acc/len(loader)):.3f}\n",
    "            Time Elapsed: {((time.time() - since)//60):.0f}m {((time.time() - since) % 60):.0f}s\n",
    "        \"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "            Step: 14..\n",
      "            Test Loss: 1.225...\n",
      "            Test Accuracy: 0.703\n",
      "            Time Elapsed: 0m 20s\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "test_model(model, testloader, criterion)"
   ]
  },
  {
   "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",
    "\n",
    "# Save model structure after modifying classifier as a class..? \n",
    "\n",
    "def save_checkpoint(model, criterion, optimizer, epochs, filepath):\n",
    "\n",
    "    model.class_to_idx = image_datasets['train_dataset'].class_to_idx\n",
    "\n",
    "    checkpoint = {'criterion': criterion.state_dict(),\n",
    "                  'num_epochs': epochs,\n",
    "                  'state_dict': model.state_dict(),\n",
    "                  'optimizer': optimizer.state_dict(),\n",
    "                  'class_to_idx': model.class_to_idx\n",
    "                 }\n",
    "\n",
    "    torch.save(checkpoint, filepath)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_checkpoint(model, criterion, optimizer, epochs, 'developmental_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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "\n",
    "# Improvement: Is there a way to take a trained model object and output its structure as a class to simplify this function? \n",
    "\n",
    "def load_flowerfier_train(filepath):\n",
    "# First, build up the model structure from scratch\n",
    "    model = models.vgg16(pretrained=True)\n",
    "    \n",
    "    for parameter in model.parameters():\n",
    "        parameter.requires_grad = False\n",
    "    \n",
    "    new_classifier = nn.Sequential(OrderedDict([('fc1', nn.Linear(4096, 256)),\n",
    "                                       ('relu', nn.ReLU()),\n",
    "                                       ('dropout', nn.Dropout()),\n",
    "                                       ('fc2', nn.Linear(256, 102)),\n",
    "                                       ('output', nn.LogSoftmax(dim=1))]))\n",
    "    model.classifier[6] = new_classifier\n",
    "# Now load checkpoint and apply saved parameters\n",
    "    checkpoint = torch.load(filepath)\n",
    "    model.load_state_dict(checkpoint['state_dict'])\n",
    "    model.class_to_idx = checkpoint['class_to_idx']\n",
    "\n",
    "    optimizer = optim.Adam(model.classifier[6].parameters())\n",
    "    \n",
    "    optimizer.load_state_dict(checkpoint['optimizer'])\n",
    "    \n",
    "    criterion = nn.NLLLoss()\n",
    "    \n",
    "    criterion.load_state_dict(checkpoint['criterion'])\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    return model, optimizer, criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model, loaded_optimizer, loaded_criterion = load_flowerfier_train('developmental_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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# why build custom function when these operations in PyTorch? Speed?\n",
    "\n",
    "def process_image(image):\n",
    "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
    "        returns an Numpy array\n",
    "    '''\n",
    "    \n",
    "    im = Image.open(image)\n",
    "    width, height = im.size\n",
    "    means = np.array([0.485, 0.456, 0.406])\n",
    "    stds = np.array([0.229, 0.224, 0.225])\n",
    "    #select shorter side from im.size, find factor that will give 256 pixels. find resize values, apply thumbnail method\n",
    "\n",
    "    short_side_len = min(im.size)    \n",
    "    factor = 256/short_side_len\n",
    "\n",
    "    resize_w = width*factor\n",
    "    resize_h = height*factor\n",
    "\n",
    "    im.copy()\n",
    "\n",
    "    im.thumbnail((resize_w, resize_h))\n",
    "\n",
    "    # find coordinates for crop\n",
    "\n",
    "    left = (resize_w - 224)/2\n",
    "    top = (resize_h - 224)/2\n",
    "    right = (resize_w + 224)/2\n",
    "    bottom = (resize_h + 224)/2\n",
    "\n",
    "    cropped = im.crop((left, top, right, bottom))\n",
    "\n",
    "    np_image = np.array(cropped)\n",
    "\n",
    "    np_image = np_image/255\n",
    "\n",
    "    np_image = (np_image - means)/stds\n",
    "\n",
    "    out_array = np_image.transpose((2,0,1))\n",
    "    \n",
    "    tensor = torch.from_numpy(out_array).float()\n",
    "    \n",
    "    return tensor\n",
    "\n",
    "processed = process_image('flowers/test/1/image_06743.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-1.7583, -1.7240, -1.7240,  ..., -2.0323, -1.9638, -1.9467],\n",
       "         [-1.3473, -1.2959, -1.4843,  ..., -1.9809, -1.9809, -1.9809],\n",
       "         [-1.8439, -1.8610, -1.8439,  ..., -1.9295, -1.9638, -1.9638],\n",
       "         ...,\n",
       "         [-1.5357, -1.5870, -1.6042,  ..., -1.2274, -1.4843, -1.3130],\n",
       "         [-1.5870, -1.6042, -1.5870,  ..., -1.4843, -1.5185, -1.2274],\n",
       "         [-1.8097, -1.7069, -1.6555,  ..., -1.9980, -1.3987, -1.1075]],\n",
       "\n",
       "        [[-1.4580, -1.4230, -1.4055,  ..., -1.9307, -1.8957, -1.8782],\n",
       "         [-1.0903, -1.0378, -1.2129,  ..., -1.8782, -1.8782, -1.8606],\n",
       "         [-1.6506, -1.6506, -1.6506,  ..., -1.8081, -1.7906, -1.7906],\n",
       "         ...,\n",
       "         [-1.1779, -1.1779, -1.1779,  ..., -0.7577, -0.9853, -0.8102],\n",
       "         [-1.3004, -1.2479, -1.2129,  ..., -0.9503, -0.9678, -0.6702],\n",
       "         [-1.6155, -1.4230, -1.3004,  ..., -1.4755, -0.8277, -0.5476]],\n",
       "\n",
       "        [[-1.4733, -1.4559, -1.4384,  ..., -1.7696, -1.7522, -1.7347],\n",
       "         [-1.0201, -0.9678, -1.1596,  ..., -1.7347, -1.7522, -1.7696],\n",
       "         [-1.5081, -1.5256, -1.5081,  ..., -1.6476, -1.7173, -1.7522],\n",
       "         ...,\n",
       "         [-1.5953, -1.6824, -1.7522,  ..., -1.7696, -1.8044, -1.6650],\n",
       "         [-1.5604, -1.5953, -1.6650,  ..., -1.6999, -1.7347, -1.4384],\n",
       "         [-1.7347, -1.6476, -1.6476,  ..., -1.8044, -1.4559, -1.2119]]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed"
   ]
  },
  {
   "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": 19,
   "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": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fda93e4fd30>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda92cd8cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(processed)"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(image_path, model_checkpoint_path, topk=5):\n",
    "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
    "    '''\n",
    "    \n",
    "    # TODO: Implement the code to predict the class from an image file\n",
    "    \n",
    "    # Process the image\n",
    "    \n",
    "    image = process_image(image_path)\n",
    "    \n",
    "    # Build up the model structure from scratch\n",
    "    model = models.vgg16(pretrained=True)\n",
    "    \n",
    "    for parameter in model.parameters():\n",
    "        parameter.requires_grad = False\n",
    "    \n",
    "    new_classifier = nn.Sequential(OrderedDict([('fc1', nn.Linear(4096, 256)),\n",
    "                                       ('relu', nn.ReLU()),\n",
    "                                       ('dropout', nn.Dropout()),\n",
    "                                       ('fc2', nn.Linear(256, 102)),\n",
    "                                       ('output', nn.LogSoftmax(dim=1))]))\n",
    "    model.classifier[6] = new_classifier\n",
    "    \n",
    "    # Now load checkpoint and apply saved parameters\n",
    "    checkpoint = torch.load(model_checkpoint_path)\n",
    "    model.load_state_dict(checkpoint['state_dict'])\n",
    "    model.class_to_idx = checkpoint['class_to_idx']\n",
    "    \n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    \n",
    "    model, image = model.to(device), image.to(device)\n",
    "\n",
    "    # Add batch dimension since only passing one image\n",
    "    \n",
    "    image.unsqueeze_(0)\n",
    "    \n",
    "    model.eval()\n",
    "    \n",
    "    log_ps = model(image)\n",
    "    ps = torch.exp(log_ps)\n",
    "    probs, classes = ps.topk(topk, dim=1)\n",
    "    \n",
    "    # convert tensor to list\n",
    "    probs, classes = probs[0].tolist(), classes[0].tolist()\n",
    "    \n",
    "    # flip key/values class_to_index    \n",
    "    idx_to_class = {v: k for k, v in model.class_to_idx.items()}\n",
    "\n",
    "    # map index to class, then class to name\n",
    "    top_classes = [idx_to_class[cls] for cls in classes]\n",
    "    top_names = [cat_to_name[cls] for cls in top_classes]\n",
    "    \n",
    "    return probs, top_classes, top_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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6325603127479553, 0.16254158318042755, 0.06648609042167664, 0.0379260890185833, 0.015116708353161812]\n",
      "['1', '83', '51', '86', '91']\n",
      "['pink primrose', 'hibiscus', 'petunia', 'tree mallow', 'hippeastrum']\n"
     ]
    }
   ],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "image_path = 'flowers/test/1/image_06743.jpg'\n",
    "model_path = 'model_checkpoint.pth'\n",
    "\n",
    "\n",
    "probs, classes, names = predict(image_path, model_path)\n",
    "print(probs)\n",
    "print(classes)\n",
    "print(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sanity_check(image_path, model_path):\n",
    "    probs, classes, names = predict(image_path, model_path)\n",
    "    \n",
    "    plt.figure(figsize=(10,10))\n",
    "    ax = plt.subplot(2,1,1)\n",
    "    \n",
    "    # Title based on top predicted name\n",
    "    title_ = names[0]\n",
    "    \n",
    "    im = process_image(image_path)\n",
    "    imshow(im, ax, title=title_)\n",
    "    \n",
    "    probs, classes, names = predict(image_path, model_path)\n",
    "    \n",
    "    plt.subplot(2,1,2)\n",
    "    sns.barplot(x=probs, y=names)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda92cd8be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sanity_check(image_path, model_path)"
   ]
  }
 ],
 "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
}