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",
    "%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",
    "\n",
    "from tqdm import tqdm\n",
    "from PIL import Image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/64216189/kernel-keeps-dying-when-plotting-a-graph-after-importing-the-torch-library\n",
    "# Avoid plt plot error\n",
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\""
   ]
  },
  {
   "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.RandomRotation(30),\n",
    "                                       transforms.RandomResizedCrop(224),\n",
    "                                       transforms.RandomHorizontalFlip(),\n",
    "                                       transforms.ToTensor(),\n",
    "                                       transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                            [0.229, 0.224, 0.225])])\n",
    "\n",
    "val_transforms = transforms.Compose([transforms.Resize(255),\n",
    "                                      transforms.CenterCrop(224),\n",
    "                                      transforms.ToTensor(),\n",
    "                                      transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                           [0.229, 0.224, 0.225])])\n",
    "\n",
    "test_transforms = transforms.Compose([transforms.Resize(255),\n",
    "                                      transforms.CenterCrop(224),\n",
    "                                      transforms.ToTensor(),\n",
    "                                      transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                           [0.229, 0.224, 0.225])])\n",
    "\n",
    "# TODO: Load the datasets with ImageFolder\n",
    "train_data = datasets.ImageFolder(train_dir, transform=train_transforms)\n",
    "val_data = datasets.ImageFolder(valid_dir, transform=val_transforms)\n",
    "test_data = datasets.ImageFolder(test_dir, transform=test_transforms)\n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\n",
    "valloader = torch.utils.data.DataLoader(val_data, batch_size=64)\n",
    "testloader = torch.utils.data.DataLoader(test_data, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label mapping\n",
    "\n",
    "You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cherry\\anaconda3\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Cherry\\anaconda3\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG11_Weights.IMAGENET1K_V1`. You can also use `weights=VGG11_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\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=True)\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (6): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (7): ReLU(inplace=True)\n",
       "    (8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (9): ReLU(inplace=True)\n",
       "    (10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (11): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (12): ReLU(inplace=True)\n",
       "    (13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (14): ReLU(inplace=True)\n",
       "    (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (17): ReLU(inplace=True)\n",
       "    (18): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (19): ReLU(inplace=True)\n",
       "    (20): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
       "  (classifier): Sequential(\n",
       "    (fc1): Linear(in_features=25088, out_features=500, bias=True)\n",
       "    (relu): ReLU()\n",
       "    (fc2): Linear(in_features=500, out_features=102, bias=True)\n",
       "    (output): LogSoftmax(dim=1)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Build and train your network\n",
    "model = models.vgg11(pretrained=True)\n",
    "\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "from collections import OrderedDict\n",
    "classifier = nn.Sequential(OrderedDict([\n",
    "                          ('fc1', nn.Linear(25088, 500)),\n",
    "                          ('relu', nn.ReLU()),\n",
    "                          ('fc2', nn.Linear(500, 102)),\n",
    "                          ('output', nn.LogSoftmax(dim=1))\n",
    "                          ]))\n",
    "    \n",
    "model.classifier = classifier\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n",
    "criterion = nn.NLLLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2.. Train loss: 9.628.. Val loss: 9.255.. Val accuracy: 0.145..Train accuracy: 0.047\n",
      "Epoch 1/2.. Train loss: 8.095.. Val loss: 5.532.. Val accuracy: 0.120..Train accuracy: 0.069\n",
      "Epoch 1/2.. Train loss: 4.528.. Val loss: 3.730.. Val accuracy: 0.188..Train accuracy: 0.169\n",
      "Epoch 1/2.. Train loss: 3.843.. Val loss: 3.526.. Val accuracy: 0.213..Train accuracy: 0.159\n",
      "Epoch 1/2.. Train loss: 3.497.. Val loss: 3.037.. Val accuracy: 0.363..Train accuracy: 0.237\n",
      "Epoch 1/2.. Train loss: 3.202.. Val loss: 2.546.. Val accuracy: 0.398..Train accuracy: 0.287\n",
      "Epoch 1/2.. Train loss: 2.773.. Val loss: 2.219.. Val accuracy: 0.456..Train accuracy: 0.322\n",
      "Epoch 1/2.. Train loss: 2.479.. Val loss: 2.088.. Val accuracy: 0.453..Train accuracy: 0.419\n",
      "Epoch 1/2.. Train loss: 2.446.. Val loss: 1.812.. Val accuracy: 0.533..Train accuracy: 0.384\n",
      "Epoch 1/2.. Train loss: 2.134.. Val loss: 1.783.. Val accuracy: 0.553..Train accuracy: 0.500\n",
      "Epoch 1/2.. Train loss: 2.009.. Val loss: 1.589.. Val accuracy: 0.612..Train accuracy: 0.497\n",
      "Epoch 1/2.. Train loss: 1.926.. Val loss: 1.473.. Val accuracy: 0.616..Train accuracy: 0.528\n",
      "Epoch 1/2.. Train loss: 1.859.. Val loss: 1.362.. Val accuracy: 0.632..Train accuracy: 0.534\n",
      "Epoch 1/2.. Train loss: 1.894.. Val loss: 1.209.. Val accuracy: 0.657..Train accuracy: 0.525\n",
      "Epoch 1/2.. Train loss: 1.726.. Val loss: 1.208.. Val accuracy: 0.656..Train accuracy: 0.556\n",
      "Epoch 1/2.. Train loss: 1.600.. Val loss: 1.199.. Val accuracy: 0.663..Train accuracy: 0.609\n",
      "Epoch 1/2.. Train loss: 1.641.. Val loss: 1.177.. Val accuracy: 0.659..Train accuracy: 0.550\n",
      "Epoch 1/2.. Train loss: 1.656.. Val loss: 1.048.. Val accuracy: 0.713..Train accuracy: 0.537\n",
      "Epoch 1/2.. Train loss: 1.438.. Val loss: 1.172.. Val accuracy: 0.676..Train accuracy: 0.631\n",
      "Epoch 1/2.. Train loss: 1.561.. Val loss: 1.027.. Val accuracy: 0.714..Train accuracy: 0.569\n",
      "Epoch 2/2.. Train loss: 1.152.. Val loss: 1.094.. Val accuracy: 0.713..Train accuracy: 0.679\n",
      "Epoch 2/2.. Train loss: 1.191.. Val loss: 0.908.. Val accuracy: 0.746..Train accuracy: 0.697\n",
      "Epoch 2/2.. Train loss: 1.324.. Val loss: 0.920.. Val accuracy: 0.749..Train accuracy: 0.650\n",
      "Epoch 2/2.. Train loss: 1.249.. Val loss: 0.933.. Val accuracy: 0.754..Train accuracy: 0.688\n",
      "Epoch 2/2.. Train loss: 1.356.. Val loss: 0.851.. Val accuracy: 0.764..Train accuracy: 0.650\n",
      "Epoch 2/2.. Train loss: 1.199.. Val loss: 0.899.. Val accuracy: 0.767..Train accuracy: 0.678\n",
      "Epoch 2/2.. Train loss: 1.189.. Val loss: 0.974.. Val accuracy: 0.743..Train accuracy: 0.656\n",
      "Epoch 2/2.. Train loss: 1.277.. Val loss: 0.836.. Val accuracy: 0.754..Train accuracy: 0.644\n",
      "Epoch 2/2.. Train loss: 1.156.. Val loss: 0.865.. Val accuracy: 0.764..Train accuracy: 0.713\n",
      "Epoch 2/2.. Train loss: 1.269.. Val loss: 0.929.. Val accuracy: 0.761..Train accuracy: 0.688\n",
      "Epoch 2/2.. Train loss: 1.193.. Val loss: 0.812.. Val accuracy: 0.780..Train accuracy: 0.703\n",
      "Epoch 2/2.. Train loss: 1.297.. Val loss: 0.804.. Val accuracy: 0.774..Train accuracy: 0.616\n",
      "Epoch 2/2.. Train loss: 1.121.. Val loss: 0.811.. Val accuracy: 0.763..Train accuracy: 0.700\n",
      "Epoch 2/2.. Train loss: 1.445.. Val loss: 0.792.. Val accuracy: 0.784..Train accuracy: 0.656\n",
      "Epoch 2/2.. Train loss: 1.126.. Val loss: 0.753.. Val accuracy: 0.793..Train accuracy: 0.716\n",
      "Epoch 2/2.. Train loss: 1.158.. Val loss: 0.731.. Val accuracy: 0.795..Train accuracy: 0.691\n",
      "Epoch 2/2.. Train loss: 1.180.. Val loss: 0.694.. Val accuracy: 0.793..Train accuracy: 0.650\n",
      "Epoch 2/2.. Train loss: 1.174.. Val loss: 0.722.. Val accuracy: 0.797..Train accuracy: 0.703\n",
      "Epoch 2/2.. Train loss: 1.013.. Val loss: 0.814.. Val accuracy: 0.781..Train accuracy: 0.750\n",
      "Epoch 2/2.. Train loss: 1.368.. Val loss: 0.817.. Val accuracy: 0.784..Train accuracy: 0.637\n",
      "Epoch 2/2.. Train loss: 1.212.. Val loss: 0.811.. Val accuracy: 0.782..Train accuracy: 0.688\n"
     ]
    }
   ],
   "source": [
    "epochs = 1\n",
    "steps = 0\n",
    "\n",
    "running_loss = 0\n",
    "running_accuracy = 0\n",
    "\n",
    "print_every = 5\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for inputs, labels in trainloader:\n",
    "        steps += 1\n",
    "        # Move input and label tensors to the default device\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        logps = model.forward(inputs)\n",
    "        loss = criterion(logps, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += 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",
    "        running_accuracy += torch.mean(equals.type(torch.FloatTensor)).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 inputs, labels in valloader:\n",
    "                    inputs, labels = inputs.to(device), labels.to(device)\n",
    "                    logps = model.forward(inputs)\n",
    "                    batch_loss = criterion(logps, labels)\n",
    "                    \n",
    "                    test_loss += batch_loss.item()\n",
    "                    \n",
    "                    # Calculate accuracy\n",
    "                    ps = torch.exp(logps)\n",
    "                    top_p, top_class = ps.topk(1, dim=1)\n",
    "                    equals = top_class == labels.view(*top_class.shape)\n",
    "                    accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "                    \n",
    "            print(f\"Epoch {epoch+1}/{epochs}.. \"\n",
    "                  f\"Train loss: {running_loss/print_every:.3f}.. \"\n",
    "                  f\"Val loss: {test_loss/len(valloader):.3f}.. \"\n",
    "                  f\"Val accuracy: {accuracy/len(valloader):.3f}..\"\n",
    "                 f\"Train accuracy: {running_accuracy/print_every:.3f}\")\n",
    "            running_loss = 0\n",
    "            running_accuracy = 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": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val loss: 1.095.. Val accuracy: 0.735..\n"
     ]
    }
   ],
   "source": [
    "# TODO: Do validation on the test set\n",
    "\n",
    "test_loss = 0\n",
    "accuracy = 0\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in testloader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        logps = model.forward(inputs)\n",
    "        batch_loss = criterion(logps, labels)\n",
    "                    \n",
    "        test_loss += batch_loss.item()\n",
    "                    \n",
    "        # Calculate accuracy\n",
    "        ps = torch.exp(logps)\n",
    "        top_p, top_class = ps.topk(1, dim=1)\n",
    "        equals = top_class == labels.view(*top_class.shape)\n",
    "        accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "        \n",
    "model.train()\n",
    "        \n",
    "print( f\"Val loss: {test_loss/len(testloader):.3f}.. \"\n",
    "    f\"Val accuracy: {accuracy/len(testloader):.3f}..\")"
   ]
  },
  {
   "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": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Save the checkpoint \n",
    "model.class_to_idx = train_data.class_to_idx\n",
    "checkpoint = {'input_size': 25088,\n",
    "              'output_size': 102,\n",
    "              'classifier' : model.classifier,\n",
    "              'state_dict': model.state_dict(),\n",
    "             'class_to_idx': model.class_to_idx}\n",
    "\n",
    "torch.save(checkpoint, 'checkpoint.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the checkpoint\n",
    "\n",
    "At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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=True)\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (6): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (7): ReLU(inplace=True)\n",
       "    (8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (9): ReLU(inplace=True)\n",
       "    (10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (11): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (12): ReLU(inplace=True)\n",
       "    (13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (14): ReLU(inplace=True)\n",
       "    (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (17): ReLU(inplace=True)\n",
       "    (18): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (19): ReLU(inplace=True)\n",
       "    (20): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
       "  (classifier): Sequential(\n",
       "    (fc1): Linear(in_features=25088, out_features=500, bias=True)\n",
       "    (relu): ReLU()\n",
       "    (fc2): Linear(in_features=500, out_features=102, bias=True)\n",
       "    (output): LogSoftmax(dim=1)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "# https://discuss.pytorch.org/t/missing-keys-unexpected-keys-in-state-dict-when-loading-self-trained-model/22379/5\n",
    "def load_checkpoint(path):\n",
    "    checkpoint = torch.load(path)\n",
    "    model = models.vgg11(pretrained=True)\n",
    "\n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "        \n",
    "    model.classifier = checkpoint['classifier']    \n",
    "    model.load_state_dict(checkpoint['state_dict'], strict=False)\n",
    "    model.class_to_idx = checkpoint['class_to_idx']\n",
    "    #model.load_state_dict(checkpoint['state_dict'])\n",
    "    \n",
    "    return model\n",
    "    \n",
    "new_model = load_checkpoint('./checkpoint.pth')\n",
    "new_model"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_image(image):\n",
    "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
    "        returns an Numpy array\n",
    "    '''\n",
    "    \n",
    "    # TODO: Process a PIL image for use in a PyTorch model\n",
    "    img_pil = Image.open(image)\n",
    "    img_transforms = transforms.Compose([transforms.Resize(256),\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",
    "    image = img_transforms(img_pil)\n",
    "    return image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your `process_image` function works, running the output through this function should return the original image (except for the cropped out portions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 417,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(process_image('./flowers/test/20/image_04912.jpg'))"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(image_path, model, topk=5):\n",
    "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
    "    '''\n",
    "    \n",
    "    # TODO: Implement the code to predict the class from an image file\n",
    "    img = process_image(image_path).unsqueeze(0)\n",
    "    \n",
    "    model.to(device)\n",
    "    img = img.to(device)\n",
    "    \n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        logps = model(img)\n",
    "        ps = torch.exp(logps)\n",
    "        top_p, top_class = ps.topk(topk, dim=1)\n",
    "    model.train()\n",
    "    \n",
    "    indices={val: key for key, val in model.class_to_idx.items()}\n",
    "    top_labels = [indices[ind] for ind in top_class[0].cpu().detach().numpy()]\n",
    "    #top_flowers=[cat_to_name[key] for key in top_labels]\n",
    "    top_p = top_p[0].cpu().detach().numpy()\n",
    "    top_class = top_labels\n",
    "    return top_p, top_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.21685176 0.15659901 0.13505433 0.09430999 0.07349497]\n",
      "['39', '28', '20', '94', '8']\n"
     ]
    }
   ],
   "source": [
    "image_path = \"flowers/test/9/image_06410.jpg\"\n",
    "probs, classes = predict(image_path, new_model)\n",
    "print(probs)\n",
    "print(classes)"
   ]
  },
  {
   "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": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 410,
       "width": 389
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 634
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "plt.figure()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "path = test_dir + \"/6/image_07181.jpg\"\n",
    "index = path.split('/')[2]\n",
    "probs, classes = predict(path, new_model)\n",
    "image = process_image(path)\n",
    "\n",
    "ax1 = imshow(image, ax = plt)\n",
    "ax1.axis('off')\n",
    "ax1.title(cat_to_name[str(index)])\n",
    "ax1.show()\n",
    "\n",
    "top_classes = [cat_to_name[index] for index in classes]\n",
    "\n",
    "\n",
    "fig, ax2 = plt.subplots()\n",
    "y_pos = np.arange(5)\n",
    "ax2.set_yticks(y_pos)\n",
    "ax2.set_yticklabels(top_classes)\n",
    "ax2.invert_yaxis()\n",
    "ax2.barh(y_pos, probs)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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