https://github.com/cjimti/aipnd-project
AIML Programming with PyTorch
https://github.com/cjimti/aipnd-project
machine-learning pytorch pytorch-implmention udacity udacity-nanodegree
Last synced: 5 months ago
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AIML Programming with PyTorch
- Host: GitHub
- URL: https://github.com/cjimti/aipnd-project
- Owner: cjimti
- License: mit
- Created: 2018-07-20T16:57:41.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-07-24T06:43:04.000Z (about 7 years ago)
- Last Synced: 2025-05-05T04:53:07.491Z (5 months ago)
- Topics: machine-learning, pytorch, pytorch-implmention, udacity, udacity-nanodegree
- Language: HTML
- Homepage: https://mk.imti.co
- Size: 129 MB
- Stars: 10
- Watchers: 3
- Forks: 11
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AI image classification and machine learning utilizing the PyTorch framework
Project assets:
- `Image Classifier Project.ipynb` Jupyter Notebook
- `Image Classifier Project.html` HTML export of the Jupyter Notebook above.
- `train.py` to train a new network on a data set.
- `predict.py` to predict flower name from an image.
## Example Image for Training
Image categories are found in [cat_to_name.json] and flower images can be downloaded in the gziped tar file [flower_data.tar.gz] from Udacity.
Get flower images:
```bash
mkdir -p flowers && cd flowers
curl https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz | tar xz
```
You should now have **test**, **train** and **valid** directories containing classification directories and flower images under the **flowers** directory.
## Examples train.py
Help:
```bash
python ./train.py -h
```
Train on **CPU** with default **vgg16**:
```bash
python ./train.py ./flowers/train/
```
Train on **GPU** with **densenet121** with one **500** node layer:
```bash
python ./train.py ./flowers/train --gpu --arch "densenet121" --hidden_units 500 --epochs5
```
Additional hidden layers with checkpoint saved to densenet201 directory.
```bash
python ./train.py ./flowers/train --gpu --arch=densenet201 --hidden_units 1280 640 --save_dir densenet201
```
## Examples train.py
Help
```bash
python ./predict.py -h
```
Basic Prediction
```bash
python ./predict.py flowers/valid/5/image_05192.jpg checkpoint.pth
```
Prediction with Top 10 Probabilities
```bash
python ./predict.py flowers/valid/5/image_05192.jpg checkpoint.pth --tok_k 10
```
Prediction with GPU
```bash
python ./predict.py flowers/valid/5/image_05192.jpg checkpoint.pth --gpu
```
## Part 1 Jupyter Notebook
### [Image Classifier Project.ipynb]
To review the [Image Classifier Project.ipynb] notebook, launch **Jupyter Notebook** from the project root:
```bash
jupyter notebook
```
## Part 2 Scripts
### [train.py]
**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 512 256 --epochs 20`
- Use GPU for training
- `python train.py data_dir --gpu`
**Help** - `python ./train.py -h`:
```plain
usage: python ./train.py ./flowers/train --gpu --learning_rate 0.001 --hidden_units 3136 --epochs 5
Train and save an image classification model.
positional arguments:
data_directory
optional arguments:
-h, --help show this help message and exit
--save_dir SAVE_DIR Directory to save training checkpoint file (default:
.)
--save_name SAVE_NAME
Checkpoint filename. (default: checkpoint)
--categories_json CATEGORIES_JSON
Path to file containing the categories. (default:
cat_to_name.json)
--arch ARCH Supported architectures: vgg11, vgg13, vgg16, vgg19,
densenet121, densenet169, densenet161, densenet201
(default: vgg16)
--gpu Use GPU (default: False)
hyperparameters:
--learning_rate LEARNING_RATE
Learning rate (default: 0.001)
--hidden_units HIDDEN_UNITS [HIDDEN_UNITS ...], -hu HIDDEN_UNITS [HIDDEN_UNITS ...]
Hidden layer units (default: [3136, 784])
--epochs EPOCHS Epochs (default: 1)
```
### [predict.py]
- 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 name
- `python predict.py input checkpoint --category_names cat_to_name.json`
- Use GPU for inference
- `python predict.py input checkpoint --gpu`
**Help** - `python ./predict.py -h`:
```plain
usage: python ./predict.py /path/to/image.jpg checkpoint.pth
Image prediction.
positional arguments:
path_to_image Path to image file.
checkpoint_file Path to checkpoint file.
optional arguments:
-h, --help show this help message and exit
--save_dir SAVE_DIR Directory to save training checkpoint file (default:
.)
--top_k TOP_K Return top KK most likely classes. (default: 5)
--category_names CATEGORIES_JSON
Path to file containing the categories. (default:
cat_to_name.json)
--gpu Use GPU (default: False)
```
[flower_data.tar.gz]:https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz
[Image Classifier Project.ipynb]:https://github.com/cjimti/aipnd-project/blob/master/Image%20Classifier%20Project.ipynb
[train.py]:https://github.com/cjimti/aipnd-project/blob/master/train.py
[predict.py]:https://github.com/cjimti/aipnd-project/blob/master/Image%20Classifier%20Project.html
[cat_to_name.json]:https://github.com/cjimti/aipnd-project/blob/master/cat_to_name.json