Metadata-Version: 2.1
Name: torchex
Version: 0.0.16
Summary: Pytorch Extension Module.
Home-page: https://github.com/0h-n0/torchex
Author: Koji Ono
Author-email: koji.ono@exwzd.com
License: UNKNOWN
Description: [![PYTHON version](https://img.shields.io/badge/python-3.6,3.7,3.8-blue.svg)](https://github.com/0h-n0/torchex)
        [![PyPI version](https://img.shields.io/pypi/v/torchex.svg)](https://badge.fury.io/py/torchex)
        [![Downloads](https://img.shields.io/pypi/dm/torchex.svg)](https://pypi.org/project/torchex/)
        
        # (WIP) `torchex library`
        
        `torchex` library provides advanced Neural Network Layers. You can easily use them like using original pytorch.
        
        ## Installation
        
        ```
        $ pip install torchex
        ```
        
        ## Requirements
        
        * Pytorch >= 1.0
        
        ## Documentation
        
        * https://torchex.readthedocs.io/en/latest/index.html
        
        ## How to use
        
        ### Lazy Style Model Definition
        
        ```python
        import torch
        import torchex.nn as exnn
        
        net = exnn.Linear(10)
        # You don't need to give the size of input for this module.
        # This network is equivalent to `nn.Linear(100, 10)`.
        
        x = troch.randn(10, 100)
        
        y = net(x)
        ```
        
        ### torchex.nn list
        
        * `torchex.nn.Pass`
        * `torchex.nn.Flatten`
        * `torchex.nn.Linear`
          * Lazy style
        * `torchex.nn.Conv1d`
          * Lazy style
        * `torchex.nn.Conv2d`
          * Lazy style
        * `torchex.nn.Conv3d`
          * Lazy style
        * `torchex.nn.Conv2dLocal`
        * `torchex.nn.GlobalAvgPool1d`
        * `torchex.nn.GlobalAvgPool2d`
        * `torchex.nn.GlobalMaxPool1d`
        * `torchex.nn.GlobalMaxPool2d`
        * `torchex.nn.MaxAvgPool2d`
        * `torch.nn.Crop2d`
        * `torch.nn.Crop3d`
        * `torch.nn.MLPConv2d`
        * `torch.nn.UpsampleConvLayer`
        * `torch.nn.CordConv2d`
        * `torch.nn.DFT1d`
        * `torch.nn.DFT2d`
        * `torch.nn.PeriodicPad2d`
        * `torch.nn.PeriodicPad3d`
        * `torch.nn.Highway`
        * `torch.nn.Inception`
        * `torch.nn.InceptionBN`
        * `torch.nn.IndRNNCell`
        * `torch.nn.IndRNNTanhCell`
        * `torch.nn.IndRNNReLuCell`
        * `torch.nn.IndRNN`
        * `torch.nn.GraphLinear`
        * `torch.nn.GraphConv`
        * `torch.nn.SparseMM`
        * `torch.nn.GraphBatchNrom`
        
        ### torchex.data.transforms
        
        * `torchex.data.transforms.PadRandomSift`
        * `torchex.data.transforms.RandomResize`
        
        ### torchex.data.attribute
        
        for visualization
        
        * `torchex.attribute.IntegratedGradients`
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >3.5
Description-Content-Type: text/markdown
Provides-Extra: docs
