Image-Classifier-Project.ipynb

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    "# Developing an AI application\n",
    "\n",
    "Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. \n",
    "\n",
    "In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below. \n",
    "\n",
    "<img src='assets/Flowers.png' width=500px>\n",
    "\n",
    "The project is broken down into multiple steps:\n",
    "\n",
    "* Load and preprocess the image dataset\n",
    "* Train the image classifier on your dataset\n",
    "* Use the trained classifier to predict image content\n",
    "\n",
    "We'll lead you through each part which you'll implement in Python.\n",
    "\n",
    "When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.\n",
    "\n",
    "First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here."
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    "# Data processing and augmentation\n",
    "import torch\n",
    "from torchvision import datasets, transforms\n",
    "from PIL import Image\n",
    "\n",
    "# Model building and training\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import models\n",
    "from collections import OrderedDict\n",
    "\n",
    "# For visualizing the results\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Utility functions\n",
    "import json\n",
    "import os"
   ]
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   "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",
    " "
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      "--2024-07-24 16:28:40--  https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 54.231.235.88, 54.231.232.24, 52.216.29.222, ...\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|54.231.235.88|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 344873452 (329M) [application/x-gzip]\n",
      "Saving to: 'flower_data.tar.gz.1'\n",
      "\n",
      "flower_data.tar.gz. 100%[===================>] 328.90M  74.3MB/s    in 4.5s    \n",
      "\n",
      "2024-07-24 16:28:44 (72.6 MB/s) - 'flower_data.tar.gz.1' saved [344873452/344873452]\n",
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      "mkdir: cannot create directory 'flowers': File exists\n",
      "mv: cannot move 'test' to 'flowers/test': File exists\n",
      "mv: cannot move 'train' to 'flowers/train': File exists\n",
      "mv: cannot move 'valid' to 'flowers/valid': File exists\n"
     ]
    }
   ],
   "source": [
    "# Download the dataset and extract\n",
    "!wget https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz\n",
    "\n",
    "# Extract the downloaded file\n",
    "!tar -xvzf flower_data.tar.gz\n",
    "\n",
    "# Arrange files in correct folders\n",
    "!mkdir flowers\n",
    "!mv test train valid flowers/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "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": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train': 6552, 'valid': 818, 'test': 819}\n",
      "['1', '10', '100', '101', '102', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '3', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '4', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '5', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '6', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '7', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '8', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '9', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99']\n"
     ]
    }
   ],
   "source": [
    "# Define your transforms for the training, validation, and testing sets\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'valid': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'test': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "# Load the datasets with ImageFolder\n",
    "image_datasets = {\n",
    "    'train': datasets.ImageFolder(train_dir, transform=data_transforms['train']),\n",
    "    'valid': datasets.ImageFolder(valid_dir, transform=data_transforms['valid']),\n",
    "    'test': datasets.ImageFolder(test_dir, transform=data_transforms['test']),\n",
    "}\n",
    "\n",
    "# Using the image datasets and the transforms, define the dataloaders\n",
    "dataloaders = {\n",
    "    'train': torch.utils.data.DataLoader(image_datasets['train'], batch_size=32, shuffle=True),\n",
    "    'valid': torch.utils.data.DataLoader(image_datasets['valid'], batch_size=32, shuffle=True),\n",
    "    'test': torch.utils.data.DataLoader(image_datasets['test'], batch_size=32, shuffle=True),\n",
    "}\n",
    "\n",
    "# Optionally, you can also define the dataset sizes and class names\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid', 'test']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "print(dataset_sizes)\n",
    "print(class_names)"
   ]
  },
  {
   "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": 5,
   "metadata": {
    "tags": []
   },
   "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": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/studio-lab-user/.conda/envs/default/lib/python3.9/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",
      "/home/studio-lab-user/.conda/envs/default/lib/python3.9/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=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_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): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace=True)\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=True)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace=True)\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=True)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace=True)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace=True)\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=True)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace=True)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace=True)\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=True)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace=True)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace=True)\n",
       "    (30): 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=4096, bias=True)\n",
       "    (relu): ReLU()\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "    (fc2): Linear(in_features=4096, out_features=102, bias=True)\n",
       "    (output): LogSoftmax(dim=1)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the pre-trained VGG16 network\n",
    "model = models.vgg16(pretrained=True)\n",
    "\n",
    "# Freeze the parameters in the feature network\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout\n",
    "classifier = nn.Sequential(OrderedDict([\n",
    "    ('fc1', nn.Linear(25088, 4096)),\n",
    "    ('relu', nn.ReLU()),\n",
    "    ('dropout', nn.Dropout(p=0.5)),\n",
    "    ('fc2', nn.Linear(4096, 102)),\n",
    "    ('output', nn.LogSoftmax(dim=1))\n",
    "]))\n",
    "\n",
    "# Replace the pre-trained classifier with the new one\n",
    "model.classifier = classifier\n",
    "\n",
    "# Define the loss function and the optimizer\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
    "\n",
    "# Move the model to the GPU if available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5.. Train loss: 5.636.. Validation loss: 2.549.. Validation accuracy: 0.458\n",
      "Epoch 1/5.. Train loss: 2.675.. Validation loss: 1.464.. Validation accuracy: 0.623\n",
      "Epoch 1/5.. Train loss: 2.400.. Validation loss: 1.472.. Validation accuracy: 0.647\n",
      "Epoch 1/5.. Train loss: 2.275.. Validation loss: 1.086.. Validation accuracy: 0.720\n",
      "Epoch 1/5.. Train loss: 1.941.. Validation loss: 1.011.. Validation accuracy: 0.734\n",
      "Epoch 2/5.. Train loss: 1.774.. Validation loss: 0.890.. Validation accuracy: 0.765\n",
      "Epoch 2/5.. Train loss: 1.765.. Validation loss: 0.811.. Validation accuracy: 0.794\n",
      "Epoch 2/5.. Train loss: 1.797.. Validation loss: 0.721.. Validation accuracy: 0.818\n",
      "Epoch 2/5.. Train loss: 1.718.. Validation loss: 0.748.. Validation accuracy: 0.804\n",
      "Epoch 2/5.. Train loss: 1.653.. Validation loss: 0.700.. Validation accuracy: 0.808\n",
      "Epoch 3/5.. Train loss: 1.410.. Validation loss: 0.667.. Validation accuracy: 0.828\n",
      "Epoch 3/5.. Train loss: 1.445.. Validation loss: 0.636.. Validation accuracy: 0.819\n",
      "Epoch 3/5.. Train loss: 1.489.. Validation loss: 0.628.. Validation accuracy: 0.843\n",
      "Epoch 3/5.. Train loss: 1.643.. Validation loss: 0.688.. Validation accuracy: 0.837\n",
      "Epoch 3/5.. Train loss: 1.577.. Validation loss: 0.809.. Validation accuracy: 0.800\n",
      "Epoch 4/5.. Train loss: 1.493.. Validation loss: 0.788.. Validation accuracy: 0.812\n",
      "Epoch 4/5.. Train loss: 1.483.. Validation loss: 0.669.. Validation accuracy: 0.841\n",
      "Epoch 4/5.. Train loss: 1.489.. Validation loss: 0.672.. Validation accuracy: 0.821\n",
      "Epoch 4/5.. Train loss: 1.467.. Validation loss: 0.680.. Validation accuracy: 0.829\n",
      "Epoch 4/5.. Train loss: 1.492.. Validation loss: 0.540.. Validation accuracy: 0.856\n",
      "Epoch 5/5.. Train loss: 1.342.. Validation loss: 0.598.. Validation accuracy: 0.839\n",
      "Epoch 5/5.. Train loss: 1.287.. Validation loss: 0.571.. Validation accuracy: 0.841\n",
      "Epoch 5/5.. Train loss: 1.359.. Validation loss: 0.540.. Validation accuracy: 0.855\n",
      "Epoch 5/5.. Train loss: 1.462.. Validation loss: 0.614.. Validation accuracy: 0.845\n",
      "Epoch 5/5.. Train loss: 1.371.. Validation loss: 0.607.. Validation accuracy: 0.848\n"
     ]
    }
   ],
   "source": [
    "# Training the classifier layers using backpropagation\n",
    "epochs = 5\n",
    "steps = 0\n",
    "running_loss = 0\n",
    "print_every = 40\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for inputs, labels in dataloaders['train']:\n",
    "        steps += 1\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",
    "        if steps % print_every == 0:\n",
    "            valid_loss = 0\n",
    "            accuracy = 0\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                for inputs, labels in dataloaders['valid']:\n",
    "                    inputs, labels = inputs.to(device), labels.to(device)\n",
    "                    logps = model.forward(inputs)\n",
    "                    loss = criterion(logps, labels)\n",
    "                    valid_loss += loss.item()\n",
    "                    \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\"Validation loss: {valid_loss/len(dataloaders['valid']):.3f}.. \"\n",
    "                  f\"Validation accuracy: {accuracy/len(dataloaders['valid']):.3f}\")\n",
    "            running_loss = 0\n",
    "            model.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing your network\n",
    "\n",
    "It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.597.. Test accuracy: 0.835\n"
     ]
    }
   ],
   "source": [
    "def test_model(model, dataloaders, criterion):\n",
    "    \"\"\"\n",
    "    Test the trained deep learning model on the test dataset.\n",
    "\n",
    "    Parameters:\n",
    "    model (torch.nn.Module): Trained model to be tested.\n",
    "    dataloaders (dict): Dictionary containing the DataLoader for the test dataset.\n",
    "    criterion (torch.nn.Module): Loss function to measure the performance of the model.\n",
    "\n",
    "    Returns:\n",
    "    None: Prints the test loss and accuracy.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Initialize test loss and accuracy\n",
    "    test_loss = 0\n",
    "    accuracy = 0\n",
    "    \n",
    "    # Set the model to evaluation mode\n",
    "    model.eval()\n",
    "    \n",
    "    # Disable gradient calculation\n",
    "    with torch.no_grad():\n",
    "        # Iterate over the test dataset\n",
    "        for inputs, labels in dataloaders['test']:\n",
    "            # Move inputs and labels to the device (GPU/CPU)\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            # Forward pass through the model\n",
    "            logps = model.forward(inputs)\n",
    "            \n",
    "            # Calculate the loss\n",
    "            loss = criterion(logps, labels)\n",
    "            test_loss += loss.item()\n",
    "            \n",
    "            # Calculate the probabilities\n",
    "            ps = torch.exp(logps)\n",
    "            \n",
    "            # Get the top class with the highest probability\n",
    "            top_p, top_class = ps.topk(1, dim=1)\n",
    "            \n",
    "            # Compare predicted classes with actual labels\n",
    "            equals = top_class == labels.view(*top_class.shape)\n",
    "            \n",
    "            # Calculate the accuracy\n",
    "            accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "    \n",
    "    # Print the average test loss and accuracy\n",
    "    print(f\"Test loss: {test_loss/len(dataloaders['test']):.3f}.. \"\n",
    "          f\"Test accuracy: {accuracy/len(dataloaders['test']):.3f}\")\n",
    "\n",
    "# Test the model\n",
    "test_model(model, dataloaders, 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": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.class_to_idx = image_datasets['train'].class_to_idx\n",
    "\n",
    "checkpoint = {\n",
    "    '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",
    "    'optimizer_state_dict': optimizer.state_dict(),\n",
    "    'epochs': epochs\n",
    "}\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": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def load_checkpoint(filepath):\n",
    "    \"\"\"\n",
    "    Load a trained model and its optimizer from a checkpoint file.\n",
    "\n",
    "    Parameters:\n",
    "    filepath (str): Path to the checkpoint file.\n",
    "\n",
    "    Returns:\n",
    "    model (torch.nn.Module): The model loaded from the checkpoint.\n",
    "    optimizer (torch.optim.Optimizer): The optimizer loaded from the checkpoint.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Load the checkpoint dictionary from the specified file\n",
    "    checkpoint = torch.load(filepath)\n",
    "    \n",
    "    # Initialize a pre-trained VGG16 model\n",
    "    model = models.vgg16(pretrained=True)\n",
    "    \n",
    "    # Replace the model's classifier with the one from the checkpoint\n",
    "    model.classifier = checkpoint['classifier']\n",
    "    \n",
    "    # Load the model state dictionary\n",
    "    model.load_state_dict(checkpoint['state_dict'])\n",
    "    \n",
    "    # Load the class-to-index mapping\n",
    "    model.class_to_idx = checkpoint['class_to_idx']\n",
    "    \n",
    "    # Initialize the optimizer for the model's classifier parameters\n",
    "    optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
    "    \n",
    "    # Load the optimizer state dictionary\n",
    "    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "    \n",
    "    return model, optimizer\n",
    "\n",
    "# Load the model and optimizer from the checkpoint file\n",
    "model, optimizer = load_checkpoint('checkpoint.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference for classification\n",
    "\n",
    "Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called `predict` that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like \n",
    "\n",
    "```python\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']\n",
    "```\n",
    "\n",
    "First you'll need to handle processing the input image such that it can be used in your network. \n",
    "\n",
    "## Image Preprocessing\n",
    "\n",
    "You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training. \n",
    "\n",
    "First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.\n",
    "\n",
    "Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.\n",
    "\n",
    "As before, the network expects the images to be normalized in a specific way. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`. You'll want to subtract the means from each color channel, then divide by the standard deviation. \n",
    "\n",
    "And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html). The color channel needs to be first and retain the order of the other two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def process_image(image_path):\n",
    "    \"\"\"\n",
    "    Scales, crops, and normalizes a PIL image for a PyTorch model.\n",
    "    \n",
    "    Parameters:\n",
    "    image_path (str): Path to the image file.\n",
    "\n",
    "    Returns:\n",
    "    np_image (numpy.ndarray): Processed image as a Numpy array, suitable for model input.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Load the image from the specified path\n",
    "    image = Image.open(image_path)\n",
    "    \n",
    "    # Resize the image to 256x256 pixels\n",
    "    image = image.resize((256, 256))\n",
    "    \n",
    "    # Center crop the image to 224x224 pixels\n",
    "    image = image.crop((16, 16, 240, 240))\n",
    "    \n",
    "    # Convert the image to a numpy array and scale pixel values to [0, 1]\n",
    "    np_image = np.array(image) / 255.0\n",
    "    \n",
    "    # Normalize the image with the mean and standard deviation values used in pre-trained models\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    np_image = (np_image - mean) / std\n",
    "    \n",
    "    # Reorder dimensions to (C, H, W) for PyTorch model input\n",
    "    np_image = np_image.transpose((2, 0, 1))\n",
    "    \n",
    "    return np_image\n",
    "\n",
    "# Check the function works correctly with a sample image\n",
    "image_path = 'flowers/test/1/image_06743.jpg'\n",
    "img = process_image(image_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Prediction"
   ]
  },
  {
   "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": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def imshow(image, ax=None, title=None):\n",
    "    \"\"\"\n",
    "    Displays an image tensor using matplotlib.\n",
    "\n",
    "    Parameters:\n",
    "    image (torch.Tensor): The image tensor to be displayed. Assumed to be in the format (C, H, W).\n",
    "    ax (matplotlib.axes.Axes, optional): Matplotlib axis to plot on. If None, a new figure and axis are created.\n",
    "    title (str, optional): Title for the plot. If provided, it is set as the title of the plot.\n",
    "\n",
    "    Returns:\n",
    "    ax (matplotlib.axes.Axes): The axis with the plotted image.\n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    # Convert image tensor from (C, H, W) format to (H, W, C) format for matplotlib\n",
    "    image = image.numpy().transpose((1, 2, 0))\n",
    "    \n",
    "    # Undo preprocessing normalization\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",
    "    # Clip the image to ensure pixel values are between 0 and 1\n",
    "    image = np.clip(image, 0, 1)\n",
    "    \n",
    "    ax.imshow(image)\n",
    "    \n",
    "    # Set title if provided\n",
    "    if title:\n",
    "        ax.set_title(title)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class Prediction\n",
    "\n",
    "Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n",
    "\n",
    "To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n",
    "\n",
    "Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n",
    "\n",
    "```python\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.9061579e-01 5.5955090e-03 3.0378839e-03 3.4165717e-04 1.3408558e-04]\n",
      "['pink primrose', 'hibiscus', 'petunia', 'mexican petunia', 'balloon flower']\n"
     ]
    }
   ],
   "source": [
    "def predict(image_path, model, topk=5):\n",
    "    \"\"\"\n",
    "    Predict the class (or classes) of an image using a trained deep learning model.\n",
    "\n",
    "    Parameters:\n",
    "    image_path (str): Path to the image file to be predicted.\n",
    "    model (torch.nn.Module): The trained deep learning model.\n",
    "    topk (int, optional): The number of top classes to return. Default is 5.\n",
    "\n",
    "    Returns:\n",
    "    tuple: A tuple containing:\n",
    "        - top_p (numpy.ndarray): Array of probabilities for the top K classes.\n",
    "        - top_flowers (list): List of flower names corresponding to the top K classes.\n",
    "    \"\"\"\n",
    "    # Process the image and convert to tensor\n",
    "    img = process_image(image_path)\n",
    "    img = torch.from_numpy(img).float()  # Convert numpy array to FloatTensor\n",
    "    img = img.unsqueeze(0)  # Add batch dimension\n",
    "    \n",
    "    # Move model and image to GPU if available\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "    img = img.to(device)\n",
    "    \n",
    "    # Predict the class\n",
    "    model.eval()  # Set model to evaluation mode\n",
    "    with torch.no_grad():\n",
    "        output = model(img)\n",
    "    \n",
    "    # Calculate probabilities and get top K predictions\n",
    "    ps = torch.exp(output)\n",
    "    top_p, top_class = ps.topk(topk, dim=1)\n",
    "    \n",
    "    # Map indices to class names and flower names\n",
    "    idx_to_class = {v: k for k, v in model.class_to_idx.items()}\n",
    "    top_class = [idx_to_class[i] for i in top_class.cpu().numpy()[0]]\n",
    "    top_flowers = [cat_to_name[str(c)] for c in top_class]\n",
    "    \n",
    "    return top_p.cpu().numpy()[0], top_flowers\n",
    "\n",
    "# Example usage\n",
    "probs, classes = predict(image_path, 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": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display_image_predictions(image_path, model):\n",
    "    \"\"\"\n",
    "    Display an image and its top K predicted classes with probabilities.\n",
    "\n",
    "    Parameters:\n",
    "    image_path (str): Path to the image file to be predicted.\n",
    "    model (torch.nn.Module): The trained deep learning model.\n",
    "    \"\"\"\n",
    "    # Process the image and convert to tensor\n",
    "    img = process_image(image_path)\n",
    "    img = torch.from_numpy(img).float()  # Convert numpy array to FloatTensor\n",
    "    \n",
    "    # Predict the class and obtain probabilities and class names\n",
    "    probs, classes = predict(image_path, model)\n",
    "    \n",
    "    # Create a figure with two subplots\n",
    "    fig, (ax1, ax2) = plt.subplots(figsize=(6, 9), nrows=2)\n",
    "    \n",
    "    # Display the image\n",
    "    imshow(img, ax=ax1)\n",
    "    ax1.axis('off')  # Hide the axes\n",
    "    ax1.set_title(classes[0])  # Set the title as the top predicted class\n",
    "    \n",
    "    # Display the top K class probabilities\n",
    "    y_pos = np.arange(len(classes))\n",
    "    ax2.barh(y_pos, probs)\n",
    "    ax2.set_yticks(y_pos)\n",
    "    ax2.set_yticklabels(classes)\n",
    "    ax2.invert_yaxis()  # Invert y-axis to have the highest probability at the top\n",
    "    \n",
    "    plt.tight_layout()  # Adjust layout to prevent overlap\n",
    "\n",
    "# Example usage\n",
    "image_path = 'flowers/test/1/image_06743.jpg'  # Path to the image\n",
    "display_image_predictions(image_path, model)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".conda-default:Python",
   "language": "python",
   "name": "conda-env-.conda-default-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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