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This is my solution for the dog breed classification problem using convolutional neural networks. This was done as part of theUdacity Machine Learning Engineer Nanodegree program.

Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience!

1. Clone the repository and navigate to the downloaded folder.

git clone https://github.com/udacity/dog-project.gitcd dog-project

2. Download thedog dataset. Unzip the folder and place it in the repo, at locationpath/to/dog-project/dogImages.

3. Download thehuman dataset. Unzip the folder and place it in the repo, at locationpath/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use7zip to extract the folder.

4. Donwload theVGG-16 bottleneck features for the dog dataset. Place it in the repo, at locationpath/to/dog-project/bottleneck_features.

5. (Optional)If you plan to install TensorFlow with GPU support on your local machine, followthe guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step.

6. (Optional)If you are running the project on your local machine (and not using AWS), create (and activate) a new environment.

   • Linux (to install withGPU support, changerequirements/dog-linux.yml torequirements/dog-linux-gpu.yml):

   conda env create -f requirements/dog-linux.ymlsource activate dog-project

   • Mac (to install withGPU support, changerequirements/dog-mac.yml torequirements/dog-mac-gpu.yml):

   conda env create -f requirements/dog-mac.ymlsource activate dog-project

   NOTE: Some Mac users may need to install a different version of OpenCV

   conda install --channel https://conda.anaconda.org/menpo opencv3

   • Windows (to install withGPU support, changerequirements/dog-windows.yml torequirements/dog-windows-gpu.yml):

   conda env create -f requirements/dog-windows.ymlactivate dog-project

7. (Optional)If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try thisalternative step to create your environment.

   • Linux orMac (to install withGPU support, changerequirements/requirements.txt torequirements/requirements-gpu.txt):

   conda create --name dog-project python=3.5source activate dog-projectpip install -r requirements/requirements.txt

   NOTE: Some Mac users may need to install a different version of OpenCV

   conda install --channel https://conda.anaconda.org/menpo opencv3

   • Windows (to install withGPU support, changerequirements/requirements.txt torequirements/requirements-gpu.txt):

   conda create --name dog-project python=3.5activate dog-projectpip install -r requirements/requirements.txt

8. (Optional)If you are using AWS, install Tensorflow.

sudo python3 -m pip install -r requirements/requirements-gpu.txt

9. SwitchKeras backend to TensorFlow.

   • Linux orMac:

      KERAS_BACKEND=tensorflow python -c "from keras import backend"

   • Windows:

      set KERAS_BACKEND=tensorflow python -c "from keras import backend"

10. (Optional)If you are running the project on your local machine (and not using AWS), create anIPython kernel for thedog-project environment.

python -m ipykernel install --user --name dog-project --display-name "dog-project"

11. Open the notebook.

jupyter notebook dog_app.ipynb

12. (Optional)If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook.

NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook.Unless requested, do not modify code that has already been included.

Your project will be reviewed by a Udacity reviewer against the CNN projectrubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass.

When you are ready to submit your project, collect the following files and compress them into a single archive for upload:

• Thedog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered.
• An HTML or PDF export of the project notebook with the namereport.html orreport.pdf.
• Any additional images used for the project that were not supplied to you for the project.Please do not include the project data sets in thedogImages/ orlfw/ folders. Likewise, please do not include thebottleneck_features/ folder.

Alternatively, your submission could consist of the GitHub link to your repository.