Metadata-Version: 2.1
Name: n3
Version: 0.1.4
Summary: Nerual Network Notation
Home-page: https://github.com/kerryeon/n3
Author: Ho Kim
Author-email: ho.kim@gnu.ac.kr
Maintainer: Ho Kim
Maintainer-email: ho.kim@gnu.ac.kr
License: MIT
Description: # N3: Neural Network Notation
        
        [![travis-ci](https://api.travis-ci.com/kerryeon/n3.svg?token=EwDa73MhCmpxV2ZhCUmb&branch=master)](https://travis-ci.com/github/kerryeon/n3)
        [![Coverage Status](https://coveralls.io/repos/github/kerryeon/n3/badge.svg?branch=master&t=bHgSyR)](https://coveralls.io/github/kerryeon/n3?branch=master)
        
        [![PyPI version shields.io](https://img.shields.io/pypi/v/n3.svg)](https://pypi.python.org/pypi/n3/)
        [![PyPI license](https://img.shields.io/pypi/l/n3.svg)](https://pypi.python.org/pypi/n3/)
        
        This project is in construction. Please be aware of using it.
        
        ```
        node LeNet5:
            let K: kernel size = int 5
        
            let C: input channels = dim
            let W: width = dim
            let H: height = dim
        
            with Conv2D:
                set kernel size = K
                set padding = K / 2
                set stride = 2
        
            node MyConv:
                1. Conv2D
                2. Relu
        
            0. Input                    =  C, W  , H
            1. MyConv                   = 32, W/2, H/2
            2. MyConv                   = 64, W/4, H/4
            3. Transform                = 64* W/4* H/4
            4. Linear + Relu + Dropout  = 1024
            5. Linear                   = 10
        ```
        
        ## Usage
        * Training
            ```bash
            $ n3 train image-classification --model LeNet5 --data Mnist --devices cuda:0 cpu
            ```
        * Evaluating
            ```bash
            $ n3 eval image-classification --model LeNet5 --data Mnist --devices cuda:0 cpu
            ```
        * Publish
            ```bash
            $ n3 publish image-classification --model LeNet5 --data Mnist --target android:java
            ```
            * android: java, flutter
            * ios: flutter
            * universal: c++, python
        * Monitoring using Tensorboard
            ```bash
            $ n3 run tensorboard  # and, browse http://localhost::xxxx/
            ```
        * Clustering with `n3-clu`
            ```bash
            $ n3 eval image-classification --model LeNet5 --data Mnist --devices w:180:cuda:0 w:192.168.0.181 cpu
            ```
            * "w:180:cuda:0": the "cuda:0" device in "xxx.xxx.xxx.180" (local)
            * "w:192.168.0.181": automatically choose devices in "192.168.0.181"
            * These can be defined as environment variables (N3_DEVICES)
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
