[](https://github.com/0h-n0/torchex)
[](https://github.com/0h-n0/torchex)
[](https://badge.fury.io/py/torchex)
[](https://circleci.com/gh/0h-n0/torchex)
[](https://travis-ci.org/0h-n0/torchex)
[](https://codecov.io/gh/0h-n0/torchex)
[](https://torchex.readthedocs.io/en/latest/?badge=latest)
[](https://codeclimate.com/github/0h-n0/torchex/maintainability)
[](https://codeclimate.com/github/0h-n0/torchex/test_coverage)
[](https://bettercodehub.com/)
[](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`
[](https://github.com/0h-n0/torchex)
[](https://badge.fury.io/py/torchex)
[](https://circleci.com/gh/0h-n0/torchex)
[](https://travis-ci.org/0h-n0/torchex)
[](https://codecov.io/gh/0h-n0/torchex)
[](https://torchex.readthedocs.io/en/latest/?badge=latest)
[](https://codeclimate.com/github/0h-n0/torchex/maintainability)
[](https://codeclimate.com/github/0h-n0/torchex/test_coverage)
[](https://bettercodehub.com/)
[](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`
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