caispp


Namecaispp JSON
Version 0.4.14 PyPI version JSON
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home_pagehttps://github.com/zanedurante/caispp
SummaryHigh level ML library used in CAIS++ Curriculum
upload_time2020-08-02 00:54:42
maintainer
docs_urlNone
authorZane Durante
requires_python
license
keywords
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requirements No requirements were recorded.
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            # Caispp

## About
This package allows for high level ML model creation.  It uses Keras with a Tensorflow backend, and was originally created to be used for the curriculum of USC's CAIS++ (Center for AI in Society, Student Branch).  

## Use Cases
The package currently supports Image Classification.

## Installation
To install run `pip install caispp`.  This package uses Tensorflow 2.0. 

## Example usage

You can see a jupyter notebook with ouputs in the `examples/` directory.  The notebook runs the code below:

```
from caispp import ImageDataset, ImageClassifier, Path

path = Path('example_dataset/') # Path to dataset
dataset = ImageDataset(path, show_distribution=True)

classifier = ImageClassifier(dataset)
classifier.train(epochs=10)

classifier.show_history()

classifier.test(show_distribution=True)
```
## Dataset directory structure
```
├── example_dataset         
│   ├── test
│   │   ├── class1      # Directory with images of class1
│   │   ├── class2      # Directory with images of class2
│   │   └── ...       
│   ├── train
│   │   ├── class1      # Directory with images of class1
│   │   ├── class2      # Directory with images of class2
│   │   └── ...         
│   ├── valid           # Optional validation set    
│   │   ├── class1
│   │   ├── class2
│   │   └── ... 
└──  
```
Each of the `test/`, `train/`, and `valid/` directories contain subdirectories for each class.  In those subdirectories, put the images files of that class.  

## Build the package

To build the package run the `build.sh` script in the directory.  The output is stored in `dist/`.
            

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    "description": "# Caispp\n\n## About\nThis package allows for high level ML model creation.  It uses Keras with a Tensorflow backend, and was originally created to be used for the curriculum of USC's CAIS++ (Center for AI in Society, Student Branch).  \n\n## Use Cases\nThe package currently supports Image Classification.\n\n## Installation\nTo install run `pip install caispp`.  This package uses Tensorflow 2.0. \n\n## Example usage\n\nYou can see a jupyter notebook with ouputs in the `examples/` directory.  The notebook runs the code below:\n\n```\nfrom caispp import ImageDataset, ImageClassifier, Path\n\npath = Path('example_dataset/') # Path to dataset\ndataset = ImageDataset(path, show_distribution=True)\n\nclassifier = ImageClassifier(dataset)\nclassifier.train(epochs=10)\n\nclassifier.show_history()\n\nclassifier.test(show_distribution=True)\n```\n## Dataset directory structure\n```\n\u251c\u2500\u2500 example_dataset         \n\u2502   \u251c\u2500\u2500 test\n\u2502   \u2502   \u251c\u2500\u2500 class1      # Directory with images of class1\n\u2502   \u2502   \u251c\u2500\u2500 class2      # Directory with images of class2\n\u2502   \u2502   \u2514\u2500\u2500 ...       \n\u2502   \u251c\u2500\u2500 train\n\u2502   \u2502   \u251c\u2500\u2500 class1      # Directory with images of class1\n\u2502   \u2502   \u251c\u2500\u2500 class2      # Directory with images of class2\n\u2502   \u2502   \u2514\u2500\u2500 ...         \n\u2502   \u251c\u2500\u2500 valid           # Optional validation set    \n\u2502   \u2502   \u251c\u2500\u2500 class1\n\u2502   \u2502   \u251c\u2500\u2500 class2\n\u2502   \u2502   \u2514\u2500\u2500 ... \n\u2514\u2500\u2500  \n```\nEach of the `test/`, `train/`, and `valid/` directories contain subdirectories for each class.  In those subdirectories, put the images files of that class.  \n\n## Build the package\n\nTo build the package run the `build.sh` script in the directory.  The output is stored in `dist/`.",
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