semvec/pypi-packages huggingface dataset:
alex-nn 0.1.11
Network analysis
Requires Python: >=3.6

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<!-- ;; Created: fre jul 17 19:11:39 2020 (+0200) -->
<!-- ;; Last-Updated: sön jun 20 11:37:44 2021 (+0200)
<!-- ;; Filename: README.md -->
<!-- ;; Author: Yinan Yu -->
<!-- ;; Description:  -->
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---
**Stay tuned!**
More examples and documentation to come

---
# Alex - a neural network analyzer

<img src="https://github.com/yinan16/alex/blob/master/images/logo.png" width="150" alt="Alex logo" title="Alex">

Alex is a Domain Specific Language (DSL) for declaring, representing, analyzing and generating neural networks.
This DSL can be considered as a configuration file to define a neural network.

### How to install:
+ `pip install alex-nn`

## How to run

### Command Line Interface
`alex-nn`

### Python

## Motivation

### Alex the little chef
Deep learning is like cooking. You have a bunch of ingredients and you put them in a pot. You have some intuitions and somewhat limited theoretical understanding of why this soup tastes better than that one, but you don't really know what's going on. Alex is a little chef that helps you distill and refine your networks. Alex is committed to helping you find better recipes for your use case.

### What does Alex do?
tunable 0.0.1.dev8
tunable manager

# tunable

A tiny library to support tunable parameters (think: configuration values) as they often occur in scientific analyses.
A main aspect differing from many other config approaches is, that the tunables are defined as classes where needed
throughout the source, with default value, type, optional documentation etc., which makes later access fast,
IDE-supported (code completion)
and prevents running it with "missing defaults" or misspelled keys leading to runtime errors.

Theoretically, all Python types instantiable from strings are supported, it is however discouraged to use anything but
`{bool, int, float, str, bytes}` for portability reasons.

There are some more features and concepts, which will hopefully be documented correctly in the future.

## Example

Example test.py:

```python
import argparse
from tunable import Tunable, TunableManager

# tunable values are just defined as classes
# inheriting from Tunable, their documentation
# being normal inline documentation

# their type is derived from their default value

class MyValue(Tunable):
    """An important tunable."""
    default = 42.0


def main():

    # TunableManager provides some argparser-actions
    # to allow users to interact with tunables
    p = argparse.ArgumentParser()
    TunableManager.register_argparser(p)
    p.parse_args()

    # Access to the value is straightforward:
    print(MyValue.value)

    # will be accessed once dynamically with checks,
    # afterwards as a simple lookup

if __name__ == '__main__':
    main()

```

Without arguments:
```bash
> python test.py
42.0
```

Functionality can automatically be registered with argparser.
```bash
> python test.py --help
usage: test.py [-h] [--tunables-show] [-t TUNABLE]
               [--tunables-load TUNABLES_LOAD] [--tunables-save TUNABLES_SAVE]

optional arguments:
  -h, --help            show this help message and exit
  --tunables-show
  -t TUNABLE, --tunable TUNABLE
  --tunables-load TUNABLES_LOAD
  --tunables-save TUNABLES_SAVE
```
Show set tunables:
```bash
> python test.py --tunables-show
### Tunables ###

# An important tunable.
# type: float
MyValue=42.0

```

Changing a tunable via command line:
(See how int(21) was upcasted to float)
```bash
> python test.py -t MyValue=21
21.0
```

Tunables can be saved/loaded from files, currently supported are key=value style config files, JSON, DER, YAML or XML.

To help reproducibility, a hash of all tunables currently set can be generated:
```python
print(TunableManager.get_hash())
```
```
VERSION:1:SHA256:3aHPIn6bRgA15+dNRPNhltMA1hibYMcs7dnUwqVjg0s=
```
Cryptographic hashing is based upon the SHA-256 hash of a canonicalized DER based serialization of the tunables.

## Stability
Warning, this library is beta software, whose interface is subject to change without notice!

No guarantees yet on binary formats, hash stability or such!

## License

MIT