{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":6846969,"sourceType":"datasetVersion","datasetId":3936235},{"sourceId":6847374,"sourceType":"datasetVersion","datasetId":3936455}],"dockerImageVersionId":30559,"isInternetEnabled":true,"language":"python","sourceType":"script","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Prepare the workspace","metadata":{"_uuid":"dd2398e0-024e-4d7c-aa11-d01bc4951a73","_cell_guid":"dbeaecac-d3f2-47ba-a728-35dfb62eaa0a","trusted":true}},{"cell_type":"code","source":"# Before you proceed, update the PATH\nimport os\nos.environ['PATH'] = f\"{os.environ['PATH']}:/root/.local/bin\"\nos.environ['PATH'] = f\"{os.environ['PATH']}:/opt/conda/lib/python3.6/site-packages\"\n# Restart the Kernel at this point.","metadata":{"_uuid":"7ce30f18-c652-48d5-affa-391c57b586d7","_cell_guid":"7d7aeec5-ff9f-40f6-8d5b-25262974d4b3","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:14:46.523642Z","iopub.execute_input":"2023-12-07T10:14:46.524745Z","iopub.status.idle":"2023-12-07T10:14:46.529896Z","shell.execute_reply.started":"2023-12-07T10:14:46.524708Z","shell.execute_reply":"2023-12-07T10:14:46.528705Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"# Do not execute the commands below unless you have restart the Kernel after updating the PATH. \n!python -m pip install torch==1.11.0","metadata":{"_uuid":"162d9186-795a-4018-a72b-b311d5d337b6","_cell_guid":"0db96a24-85f0-404a-a503-59b2d5e1a3a6","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:14:46.545110Z","iopub.execute_input":"2023-12-07T10:14:46.545547Z","iopub.status.idle":"2023-12-07T10:14:58.600366Z","shell.execute_reply.started":"2023-12-07T10:14:46.545509Z","shell.execute_reply":"2023-12-07T10:14:58.599196Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Requirement already satisfied: torch==1.11.0 in /opt/conda/lib/python3.10/site-packages (1.11.0)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch==1.11.0) (4.6.3)\n","output_type":"stream"}]},{"cell_type":"code","source":"# Check torch version and CUDA status if GPU is enabled.\nimport torch\nprint(torch.__version__)\nprint(torch.cuda.is_available()) # Should return True when GPU is enabled.","metadata":{"_uuid":"a1831fc5-a119-4fd9-9a6b-019b9ea6a2bd","_cell_guid":"3478e359-158b-4c4c-88a9-7571277af0e2","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:14:58.602620Z","iopub.execute_input":"2023-12-07T10:14:58.602957Z","iopub.status.idle":"2023-12-07T10:14:58.609204Z","shell.execute_reply.started":"2023-12-07T10:14:58.602928Z","shell.execute_reply":"2023-12-07T10:14:58.608345Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"1.11.0+cu102\nTrue\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Developing an AI application\n\nGoing 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\nIn 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\n\nThe 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\nWe'll lead you through each part which you'll implement in Python.\n\nWhen 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\nFirst 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.","metadata":{"_uuid":"7a84a163-7b7a-4371-a4f2-dea11a992c45","_cell_guid":"e65f483b-1b59-4fb1-a7f6-9ccac0ee462c","trusted":true}},{"cell_type":"code","source":"# Imports here\n%matplotlib inline\n%config InlinBackend.figure_format = 'retina'\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch import optim\nimport torch.nn.functional as F\nimport ast\nimport torchvision.transforms as transforms\nfrom torchvision import datasets, models, transforms\nimport torchvision.models as models\nfrom torch.autograd import Variable\nfrom collections import OrderedDict\nfrom PIL import Image\nimport json\nimport time\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"_uuid":"840e4ef7-5c0e-426c-a217-865755662155","_cell_guid":"535909bd-89a9-4fbe-b02b-3b511a5499d4","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:14:58.610262Z","iopub.execute_input":"2023-12-07T10:14:58.610538Z","iopub.status.idle":"2023-12-07T10:14:58.629317Z","shell.execute_reply.started":"2023-12-07T10:14:58.610512Z","shell.execute_reply":"2023-12-07T10:14:58.628506Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"markdown","source":"## Load the data\n\nHere 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).","metadata":{"_uuid":"135a0828-82fa-49df-bc92-cd748ec13df9","_cell_guid":"e09ff372-6581-4279-a259-dffa978946e7","trusted":true}},{"cell_type":"markdown","source":"If you do not find the `flowers/` dataset in the current directory, **/workspace/home/aipnd-project/**, you can download it using the following commands. \n\n```bash\n!wget 'https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz'\n!unlink flowers\n!mkdir flowers && tar -xzf flower_data.tar.gz -C flowers\n```","metadata":{"_uuid":"4e8c9ba7-d868-43be-a89a-87bb46c53c62","_cell_guid":"875a3aed-e00d-4402-8f6a-cd3d6e3643ff","trusted":true}},{"cell_type":"markdown","source":"## Data Description\nThe 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\nThe 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\nThe 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.","metadata":{"_uuid":"8355a1fa-5011-4450-9c70-201171cb0435","_cell_guid":"d5c2af06-3297-4fb0-a864-3578276b8812","trusted":true}},{"cell_type":"code","source":"!wget 'https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz'\n#!unlink flowers\n!mkdir flowers && tar -xzf flower_data.tar.gz -C flowers","metadata":{"_uuid":"af4ed138-4168-471f-9268-a57a20f1c77d","_cell_guid":"9112bc32-78e0-41ae-9084-b6b19c6e6cd0","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:14:58.632095Z","iopub.execute_input":"2023-12-07T10:14:58.632714Z","iopub.status.idle":"2023-12-07T10:15:07.023645Z","shell.execute_reply.started":"2023-12-07T10:14:58.632689Z","shell.execute_reply":"2023-12-07T10:15:07.022538Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stdout","text":"--2023-12-07 10:14:59-- https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz\nResolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.162.165, 52.217.138.112, 54.231.234.72, ...\nConnecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.162.165|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 344873452 (329M) [application/x-gzip]\nSaving to: ‘flower_data.tar.gz.2’\n\nflower_data.tar.gz. 100%[===================>] 328.90M 55.4MB/s in 6.0s \n\n2023-12-07 10:15:05 (54.4 MB/s) - ‘flower_data.tar.gz.2’ saved [344873452/344873452]\n\nmkdir: cannot create directory ‘flowers’: File exists\n","output_type":"stream"}]},{"cell_type":"code","source":"data_dir = 'flowers'\ntrain_dir = data_dir + '/train'\nvalid_dir = data_dir + '/valid'\ntest_dir = data_dir + '/test'","metadata":{"_uuid":"0a583a63-ddc6-4355-bb5a-a8de36e88d13","_cell_guid":"f4013f5e-5c3f-4a75-bd6a-6211e7a0cb61","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:15:07.025151Z","iopub.execute_input":"2023-12-07T10:15:07.025592Z","iopub.status.idle":"2023-12-07T10:15:07.030361Z","shell.execute_reply.started":"2023-12-07T10:15:07.025557Z","shell.execute_reply":"2023-12-07T10:15:07.029522Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"# TODO: Define your transforms for the training, validation, and testing sets\n\n# Define transforms\ntrain_transforms = 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\nval_test_transforms = 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# TODO: Load the datasets with ImageFolder\n\n# Load datasets \ntrain_ds = datasets.ImageFolder(train_dir, train_transforms)\nvalid_ds = datasets.ImageFolder(valid_dir, val_test_transforms)\ntest_ds = datasets.ImageFolder(test_dir, val_test_transforms)\n\n# TODO: Using the image datasets and the trainforms, define the dataloaders\n\n# Create dataloaders\ntrain_loader = torch.utils.data.DataLoader(train_ds, batch_size=64, shuffle=True)\nvalid_loader = torch.utils.data.DataLoader(valid_ds, batch_size=64)\ntest_loader = torch.utils.data.DataLoader(test_ds, batch_size=64)","metadata":{"_uuid":"5356a5e6-a838-42b9-8eda-4f03f0b7b12c","_cell_guid":"e4186edb-09f5-4886-861b-720d933bca67","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:15:07.031789Z","iopub.execute_input":"2023-12-07T10:15:07.032075Z","iopub.status.idle":"2023-12-07T10:15:07.092551Z","shell.execute_reply.started":"2023-12-07T10:15:07.032051Z","shell.execute_reply":"2023-12-07T10:15:07.091810Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"markdown","source":"### Label mapping\n\nYou'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.","metadata":{"_uuid":"89611dc3-9b15-4afa-8a87-4c3456153d21","_cell_guid":"38a47a3a-a1a3-4ae8-abc4-24ea0e462d53","trusted":true}},{"cell_type":"code","source":"with open('/kaggle/input/cat-to-name/cat_to_name.json', 'r') as f:\n cat_to_name = json.load(f)","metadata":{"_uuid":"3a7f792a-4ace-41ce-99b0-ce047357a9dd","_cell_guid":"8a70867d-ab72-4f06-ae03-d21cb3179072","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:15:07.093528Z","iopub.execute_input":"2023-12-07T10:15:07.093770Z","iopub.status.idle":"2023-12-07T10:15:07.110178Z","shell.execute_reply.started":"2023-12-07T10:15:07.093749Z","shell.execute_reply":"2023-12-07T10:15:07.109523Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"markdown","source":"# Building and training the classifier\n\nNow 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\nWe'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\nWe'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\nWhen 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\nOne 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\nGPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.\n\n## Note for Workspace users: \nIf 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.","metadata":{"_uuid":"7874cb5a-4740-4266-9efb-615e99dfc7fe","_cell_guid":"1a812ab7-8611-4690-ac9c-7ab98a1fd9b7","trusted":true}},{"cell_type":"code","source":"# TODO: Build and train your network\n\n# VGG16 Model\nmodel = models.vgg16(pretrained=True)\nfor param in model.parameters():\n param.requires_grad = False \n\n# Classifier \nmodel.classifier = nn.Sequential(\n nn.Linear(25088, 1024),\n nn.ReLU(),\n nn.Dropout(0.2),\n nn.Linear(1024, 512),\n nn.ReLU(),\n nn.Dropout(0.2), \n nn.Linear(512, 102), \n nn.LogSoftmax(dim=1)\n)\n\n# Move model to GPU\nmodel = model.to('cuda')\n\n# Loss and Optimizer\ncriterion = nn.NLLLoss()\noptimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n\n# Train the model\nepochs = 10\nprint_every = 100\nsteps = 0\n\nfor e in range(epochs):\n running_loss = 0\n for inputs, labels in train_loader:\n steps += 1\n inputs, labels = inputs.to('cuda'), labels.to('cuda')\n \n # Forward\n outputs = model(inputs)\n loss = criterion(outputs, labels)\n \n # Backward \n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n running_loss += loss.item()\n \n if steps % print_every == 0:\n model.eval() \n # Validation\n val_loss = 0\n accuracy = 0\n \n for inputs, labels in valid_loader:\n inputs, labels = inputs.to('cuda'), labels.to('cuda')\n outputs = model(inputs)\n loss = criterion(outputs, labels)\n \n val_loss += loss.item()\n \n ps = torch.exp(outputs)\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))\n \n print(f\"Epoch {e+1}/{epochs}.. \"\n f\"Step {steps}/{len(train_loader)}.. \"\n f\"Loss: {running_loss/print_every:.3f}.. \" \n f\"Validation Loss: {val_loss/len(valid_loader):.3f}..\"\n f\"Accuracy: {accuracy/len(valid_loader):.3f}\")\n running_loss = 0\n model.train()","metadata":{"_uuid":"f3986cbc-151e-44fd-9bab-27a1c5e95e25","_cell_guid":"38580579-2091-4577-8deb-677c6622c46b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:15:07.111196Z","iopub.execute_input":"2023-12-07T10:15:07.111425Z","iopub.status.idle":"2023-12-07T10:26:39.952267Z","shell.execute_reply.started":"2023-12-07T10:15:07.111404Z","shell.execute_reply":"2023-12-07T10:26:39.951269Z"},"trusted":true},"execution_count":38,"outputs":[{"name":"stdout","text":"Epoch 1/10.. Step 100/103.. Loss: 2.561.. Validation Loss: 0.969..Accuracy: 0.760\nEpoch 2/10.. Step 200/103.. Loss: 1.205.. Validation Loss: 0.581..Accuracy: 0.851\nEpoch 3/10.. Step 300/103.. Loss: 0.886.. Validation Loss: 0.502..Accuracy: 0.855\nEpoch 4/10.. Step 400/103.. Loss: 0.790.. Validation Loss: 0.550..Accuracy: 0.849\nEpoch 5/10.. Step 500/103.. Loss: 0.676.. Validation Loss: 0.406..Accuracy: 0.891\nEpoch 6/10.. Step 600/103.. Loss: 0.634.. Validation Loss: 0.383..Accuracy: 0.899\nEpoch 7/10.. Step 700/103.. Loss: 0.541.. Validation Loss: 0.385..Accuracy: 0.887\nEpoch 8/10.. Step 800/103.. Loss: 0.498.. Validation Loss: 0.433..Accuracy: 0.888\nEpoch 9/10.. Step 900/103.. Loss: 0.440.. Validation Loss: 0.373..Accuracy: 0.900\nEpoch 10/10.. Step 1000/103.. Loss: 0.446.. Validation Loss: 0.337..Accuracy: 0.903\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Testing your network\n\nIt'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.","metadata":{"_uuid":"ad9829b3-9cdc-4ea2-90e2-1337c104dad1","_cell_guid":"d448647c-b423-4809-8815-0331789cd2b6","trusted":true}},{"cell_type":"code","source":"# TODO: Do validation on the test set\n\n# Load model \nmodel.eval()\n\n# Testing loop\ntest_loss = 0 \naccuracy = 0\n\nwith torch.no_grad():\n for inputs, labels in test_loader:\n inputs, labels = inputs.to('cuda'), labels.to('cuda')\n \n logps = model(inputs) \n batch_loss = criterion(logps, labels)\n \n test_loss += batch_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))\n\n# Print test loss and accuracy \ntest_loss = test_loss / len(test_loader)\naccuracy = accuracy / len(test_loader)\n\nprint(f\"Test Loss: {test_loss:.3f}.. \")\nprint(f\"Test Accuracy: {accuracy:.3f}\")","metadata":{"_uuid":"eb317077-974c-4f54-90b3-2d2135ca6f3c","_cell_guid":"8fc1e356-fa0e-43f1-923d-c4df3fb4d156","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:39.953657Z","iopub.execute_input":"2023-12-07T10:26:39.953954Z","iopub.status.idle":"2023-12-07T10:26:48.925696Z","shell.execute_reply.started":"2023-12-07T10:26:39.953930Z","shell.execute_reply":"2023-12-07T10:26:48.924725Z"},"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"Test Loss: 0.499.. \nTest Accuracy: 0.874\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Save the checkpoint\n\nNow 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\nRemember 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.","metadata":{"_uuid":"a690def0-be02-4b75-bebc-01e2e6470aca","_cell_guid":"87582c10-74ec-4e43-aff7-7e3beed2cd58","trusted":true}},{"cell_type":"code","source":"# TODO: Save the checkpoint \n\n# Model class_to_idx\nmodel.class_to_idx = train_ds.class_to_idx\n\ncheckpoint = {'input_size': 25088,\n 'output_size': 102,\n 'arch': 'vgg16',\n 'learning_rate': 0.001,\n 'batch_size': 64, \n 'epochs': epochs,\n 'optimizer': optimizer.state_dict(),\n 'classifier': model.classifier,\n 'state_dict': model.state_dict(),\n 'class_to_idx': model.class_to_idx}\n\ntorch.save(checkpoint, 'checkpoint.pth')","metadata":{"_uuid":"82245aa8-364d-42a0-b3fd-7f06fa02637d","_cell_guid":"8e53e796-1480-48d4-b8e9-fe57a9c5d500","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:48.928711Z","iopub.execute_input":"2023-12-07T10:26:48.929053Z","iopub.status.idle":"2023-12-07T10:26:50.179205Z","shell.execute_reply.started":"2023-12-07T10:26:48.929025Z","shell.execute_reply":"2023-12-07T10:26:50.178064Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"markdown","source":"## Loading the checkpoint\n\nAt 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.","metadata":{"_uuid":"e397f917-d7b3-4806-a302-e5b5019ec89b","_cell_guid":"d59a92c5-76f0-43cf-998e-df0ba9b65635","trusted":true}},{"cell_type":"code","source":"# TODO: Write a function that loads a checkpoint and rebuilds the model\n\ndef load_checkpoint(filepath):\n checkpoint = torch.load(filepath)\n model = models.vgg16(pretrained=True) \n for param in model.parameters():\n param.requires_grad = False\n \n model.class_to_idx = checkpoint['class_to_idx']\n \n model.classifier = checkpoint['classifier']\n model.load_state_dict(checkpoint['state_dict'])\n \n return model\n\n# Usage:\nmodel = load_checkpoint('checkpoint.pth')","metadata":{"_uuid":"f250779b-7a81-4357-94cc-089ea5697d0d","_cell_guid":"03d8a298-a457-4878-a02a-5e975abc9755","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:50.180564Z","iopub.execute_input":"2023-12-07T10:26:50.180851Z","iopub.status.idle":"2023-12-07T10:26:51.943993Z","shell.execute_reply.started":"2023-12-07T10:26:50.180825Z","shell.execute_reply":"2023-12-07T10:26:51.942697Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"markdown","source":"# Inference for classification\n\nNow 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\nprobs, classes = predict(image_path, model)\nprint(probs)\nprint(classes)\n> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]\n> ['70', '3', '45', '62', '55']\n```\n\nFirst you'll need to handle processing the input image such that it can be used in your network. \n\n## Image Preprocessing\n\nYou'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\nFirst, 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\nColor 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\nAs 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\nAnd 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.","metadata":{"_uuid":"72afc9a6-fa86-44c9-af24-f3db108b30f6","_cell_guid":"379c6b19-b0be-4a7a-9763-9cc719d30782","trusted":true}},{"cell_type":"code","source":"def process_image(image_path):\n \"\"\"Scales, crops, and normalizes a PIL image for a PyTorch model\"\"\"\n # TODO: Process a PIL image for use in a PyTorch model\n\n img = Image.open(image_path)\n \n transformations = transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) \n ])\n \n img_tensor = transformations(img)\n \n return img_tensor","metadata":{"_uuid":"67d37625-affe-46d0-a428-c9210392ac5c","_cell_guid":"c42d85a1-4a48-4f86-9373-31cdf23f1146","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:51.945502Z","iopub.execute_input":"2023-12-07T10:26:51.945822Z","iopub.status.idle":"2023-12-07T10:26:51.951771Z","shell.execute_reply.started":"2023-12-07T10:26:51.945794Z","shell.execute_reply":"2023-12-07T10:26:51.950881Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"markdown","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).","metadata":{"_uuid":"32cfe687-732e-42ec-8101-0b42f078814b","_cell_guid":"7b6989bb-e3de-4dc6-98b5-888782f24404","trusted":true}},{"cell_type":"code","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","metadata":{"_uuid":"5d504883-dddc-4af6-8e7e-52a167b84849","_cell_guid":"50265c17-dd80-407c-8319-a98c5a1fab52","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:51.954086Z","iopub.execute_input":"2023-12-07T10:26:51.954356Z","iopub.status.idle":"2023-12-07T10:26:51.974663Z","shell.execute_reply.started":"2023-12-07T10:26:51.954332Z","shell.execute_reply":"2023-12-07T10:26:51.973794Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"markdown","source":"## Class Prediction\n\nOnce 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\nTo 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\nAgain, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n\n```python\nprobs, classes = predict(image_path, model)\nprint(probs)\nprint(classes)\n> [ 0.01558163 0.01541934 0.01452626 0.01443549 0.01407339]\n> ['70', '3', '45', '62', '55']\n```","metadata":{"_uuid":"dc5b987e-50c4-4d5b-893b-12e678e48f34","_cell_guid":"b1aca8ce-7ce7-4fe6-baa2-c097854ba95f","trusted":true}},{"cell_type":"code","source":"def predict(image_path, model, topk=5):\n \"\"\"Make a prediction for an image using a trained model\n \n Params\n --------\n image_path (str): path to the image\n model (PyTorch model): trained model for inference\n topk (int): number of top predictions to return\n \n Returns\n --------\n probs (list): top prediction probabilities\n classes (list): top predicted classes\n \"\"\"\n\n # Ensure model and image tensor on same device\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model.to(device)\n\n img_tensor = process_image(image_path)\n img_tensor = img_tensor.unsqueeze_(0).to(device)\n \n with torch.no_grad(): \n output = model.forward(img_tensor)\n ps = torch.exp(output).topk(topk)\n \n # Move preds back to CPU for processing \n probs = ps[0].tolist()[0]\n classes = ps[1].cpu().tolist()[0]\n \n return probs, classes","metadata":{"_uuid":"b58df97a-2b89-41a5-bae8-84da27529843","_cell_guid":"6f242a20-4f33-4b66-929c-a1818f4d60d3","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:51.975566Z","iopub.execute_input":"2023-12-07T10:26:51.975823Z","iopub.status.idle":"2023-12-07T10:26:51.987218Z","shell.execute_reply.started":"2023-12-07T10:26:51.975794Z","shell.execute_reply":"2023-12-07T10:26:51.986531Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":"## Sanity Checking\n\nNow 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\n\nYou 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.","metadata":{"_uuid":"6c8fb14e-28b6-498a-9f83-cef21cd71a77","_cell_guid":"66086928-7d84-42d4-95f3-0aab90df8f81","trusted":true}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef sanity_check(img_path, model):\n \"\"\"Display image and preditions from model\"\"\"\n \n probs, classes = predict(img_path, model)\n\n # Get classes to names mapping\n class_to_name = {val:key for key, val in model.class_to_idx.items()} \n \n # Open image\n img = Image.open(img_path)\n \n # Plot \n fig, (ax1, ax2) = plt.subplots(figsize=(6,10), ncols=1, nrows=2)\n ax1.imshow(img)\n ax1.set_title(class_to_name[classes[0]])\n ax1.axis('off')\n \n ypos = np.arange(len(probs))\n ax2.barh(ypos, probs)\n ax2.set_yticks(ypos)\n ax2.set_yticklabels([class_to_name[x] for x in classes])\n ax2.invert_yaxis()\n \n ax2.set_title('Class Probability') \n fig.tight_layout()\n plt.show()","metadata":{"_uuid":"2d4ae588-71e4-4a54-a601-bb4a85b9a6c3","_cell_guid":"648166b1-561b-4e1f-aca0-b08d61395774","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:51.988367Z","iopub.execute_input":"2023-12-07T10:26:51.988936Z","iopub.status.idle":"2023-12-07T10:26:52.004646Z","shell.execute_reply.started":"2023-12-07T10:26:51.988901Z","shell.execute_reply":"2023-12-07T10:26:52.003764Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"img_path = '/kaggle/working/flowers/test/11/image_03151.jpg' \n\nsanity_check(img_path, model)","metadata":{"_uuid":"57e58987-e47c-4a12-b6ac-82b5a4921f3b","_cell_guid":"9bbbf1bc-d5fd-4256-8c29-af96e083fe36","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:52.005680Z","iopub.execute_input":"2023-12-07T10:26:52.006001Z","iopub.status.idle":"2023-12-07T10:26:52.566226Z","shell.execute_reply.started":"2023-12-07T10:26:52.005936Z","shell.execute_reply":"2023-12-07T10:26:52.565279Z"},"trusted":true},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef sanity_check(img_path, model):\n\n probs, classes = predict(img_path, model)\n \n class_to_name = {val:key for key, val in model.class_to_idx.items()}\n flower_names = [class_to_name[x] for x in classes]\n \n actual_flower = img_path.split('/')[-2]\n predicted = flower_names[0]\n correct = 'yes' if predicted == actual_flower else 'no'\n\n fig, (ax1, ax2) = plt.subplots(figsize=(6,10), ncols=1, nrows=2)\n\n ax1.imshow(Image.open(img_path))\n ax1.axis('off')\n \n ax2.barh(flower_names, probs)\n ax2.set_yticks(np.arange(len(probs)))\n ax2.set_yticklabels(flower_names)\n ax2.invert_yaxis()\n\n fig.suptitle(\"Actual Class: {} \\n Predicted Class: {} \\n Correctly Classified: {}\".format(\n actual_flower, predicted, correct), fontsize=14)\n\n plt.tight_layout()\n plt.show()\n print(flower_names)","metadata":{"_uuid":"bd6e5379-bfa6-40c3-9ffd-bd6739cd9f0a","_cell_guid":"1c10fa92-aa4d-4f2f-9a01-8ef8f030e9fd","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:52.567652Z","iopub.execute_input":"2023-12-07T10:26:52.567998Z","iopub.status.idle":"2023-12-07T10:26:52.578539Z","shell.execute_reply.started":"2023-12-07T10:26:52.567966Z","shell.execute_reply":"2023-12-07T10:26:52.577655Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"# Display an image along with the top 5 classes\ndef display_image(image_path):\n img = process_image(image_path)\n imshow(img)\n\nmodel = load_checkpoint('checkpoint.pth')\n\n# Testing with a random image from the test set\ntest_image_path = np.random.choice(test_ds.imgs)[0]\ndisplay_image(test_image_path)\n\nprobs, classes = predict(test_image_path, model)\n\nclass_names = [cat_to_name[cls] for cls in classes]\n\nprint(\"Probabilities:\", probs)\nprint(\"Classes:\", class_names)\n\n# Sanity check with a few random images from the test set\nfor i in range(5):\n test_image_path = np.random.choice(test_ds.imgs)[0]\n display_image(test_image_path)\n\n probs, classes = predict(test_image_path, model)\n\n class_names = [cat_to_name[cls] for cls in classes]\n\n print(\"Probabilities:\", probs)\n print(\"Classes:\", class_names)","metadata":{"_uuid":"0fa599e9-adda-4420-a447-2193f78d8813","_cell_guid":"82896b53-8291-47ce-8bcb-17e6ecd02e19","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-12-07T10:26:52.579841Z","iopub.execute_input":"2023-12-07T10:26:52.580152Z","iopub.status.idle":"2023-12-07T10:26:54.443024Z","shell.execute_reply.started":"2023-12-07T10:26:52.580127Z","shell.execute_reply":"2023-12-07T10:26:54.441868Z"},"trusted":true},"execution_count":48,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[48], line 9\u001b[0m\n\u001b[1;32m 6\u001b[0m model \u001b[38;5;241m=\u001b[39m load_checkpoint(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcheckpoint.pth\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Testing with a random image from the test set\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m test_image_path \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimgs\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 10\u001b[0m display_image(test_image_path)\n\u001b[1;32m 12\u001b[0m probs, classes \u001b[38;5;241m=\u001b[39m predict(test_image_path, model)\n","File \u001b[0;32mmtrand.pyx:911\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: a must be 1-dimensional"],"ename":"ValueError","evalue":"a must be 1-dimensional","output_type":"error"}]}]}