https://github.com/sarthak-1408/udacity-project
This is my Udacity Nanodegree Repository and i just complete my second project
https://github.com/sarthak-1408/udacity-project
udacity udacity-machine-learning-nanodegree udacity-nanodegree udacity-self-driving-car
Last synced: 7 months ago
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This is my Udacity Nanodegree Repository and i just complete my second project
- Host: GitHub
- URL: https://github.com/sarthak-1408/udacity-project
- Owner: Sarthak-1408
- License: bsd-3-clause
- Created: 2022-11-24T19:19:02.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-11-24T19:49:33.000Z (almost 3 years ago)
- Last Synced: 2025-01-16T13:59:22.945Z (9 months ago)
- Topics: udacity, udacity-machine-learning-nanodegree, udacity-nanodegree, udacity-self-driving-car
- Language: HTML
- Homepage:
- Size: 194 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Introduction to Machine Learning Nanodegree
## Deep Learning
### Create your own Image Classifier
#### Overview
Project code for Udacity's AI Programming with Python Nanodegree program. In this project, students first develop code for an image classifier built with PyTorch, then convert it into a command line application.
#### Install
This project requires Python 2.7 and the following Python libraries installed:
- PyTorch
- ArgParse
- Jason
- PIL
- NumPy
- Pandas
- matplotlib
- scikit-learn You will also need to have software installed to run and execute an iPython Notebook
We recommend students install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.
#### Run
In a terminal or command window, navigate to the top-level project directory / (that contains this README) and run one of the following commands:
ipython notebook Image Classifier Project.ipynb or
jupyter notebook Image Classifier Project.ipynb This will open the iPython Notebook software and project file in your browser.
**Or for Command Line**
In a terminal or command window, navigate to the top-level project directory / (that contains this README) and run one of the following commands:
- Train a new network on a data set with train.py
- Basic usage: python train.py data_directory
- Prints out training loss, validation loss, and validation accuracy as the network trains
- Options:
- Set directory to save checkpoints: python train.py data_dir --save_dir save_directory
- Choose architecture: python train.py data_dir --arch "vgg13"
- Set hyperparameters: python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 20
- Use GPU for training: python train.py data_dir --gpu
- Predict flower name from an image with predict.py along with the probability of that name. That is, you'll pass in a single image /path/to/image and return the flower name and class probability.
- Basic usage: python predict.py /path/to/image checkpoint
- Options:
- Return top KK most likely classes: python predict.py input checkpoint --top_k 3
- Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_to_name.json
- Use GPU for inference: python predict.py input checkpoint --gpu