https://github.com/symjax/symjax

Documentation:
https://github.com/symjax/symjax

dataset deep-learning deep-neural-networks jax lasagne numpy tensorflow theano

Last synced: 4 days ago
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Documentation:

• Host: GitHub
• URL: https://github.com/symjax/symjax
• Owner: SymJAX
• License: apache-2.0
• Created: 2019-09-04T14:23:45.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-05-22T22:41:16.000Z (over 1 year ago)
• Last Synced: 2024-09-29T10:23:39.250Z (4 days ago)
• Topics: dataset, deep-learning, deep-neural-networks, jax, lasagne, numpy, tensorflow, theano
• Language: Python
• Homepage: https://symjax.readthedocs.io/en/master/
• Size: 27.3 MB
• Stars: 119
• Watchers: 9
• Forks: 5
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        

![SymJAX logo](./docs/img/symjax_logo.png)

# SymJAX: symbolic CPU/GPU/TPU programming ![Continuous integration](https://github.com/SymJAX/SymJAX/workflows/Continuous%20integration/badge.svg) ![doctest](https://github.com/SymJAX/SymJAX/workflows/doc/badge.svg) ![license](https://img.shields.io/badge/license-Apache%202-blue) 

This is an under-development research project, not an official product, expect bugs and sharp edges; please help by trying it out, reporting bugs.

[**Reference docs**](https://symjax.readthedocs.io/en/latest/)

## What is SymJAX ?

SymJAX is a symbolic programming version of JAX simplifying graph input/output/updates and providing additional functionalities for general machine learning and deep learning applications. From an user perspective SymJAX apparents to Theano with fast graph optimization/compilation and broad hardware support, along with Lasagne-like deep learning functionalities

## Why SymJAX ?

The number of libraries topping Jax/Tensorflow/Torch is large and growing by the

day. What SymJAX offers as opposed to most is an all-in-one library with diverse

functionalities such as

- dozens of various datasets with clear descriptions and one line import

- versatile set of functions from ffts, linear algebraic tools, random variables, ...

- advanced signal processing tools such as multiple wavelet families (in time and frequency domain), multiple time-frequency representations, apodization windows, ...

- IO utilities to monitor/save/track specific statistics during graph execution through h5 files and numpy, simple and explicit graph saving allowing to save and load models without burden

- side utilities such as automatic batching of dataset, data splitting, cross-validation, ...

and most importantly, a SYMBOLIC/DECLARATIVE programming environment allowing CONCISE/EXPLICIT/OPTIMIZED computations.

For a deep network oriented imperative library built on JAX and with a JAX syntax check out [FLAX](https://github.com/google/flax).

## Examples

```python

import sys

import symjax as sj

import symjax.tensor as T

# create our variable to be optimized

mu = T.Variable(T.random.normal((), seed=1))

# create our cost

cost = T.exp(-(mu-1)**2)

# get the gradient, notice that it is itself a tensor that can then

# be manipulated as well

g = sj.gradients(cost, mu)

print(g)

# (Tensor: shape=(), dtype=float32)

# create the compiled function that will compute the cost and apply

# the update onto the variable

f = sj.function(outputs=cost, updates={mu:mu-0.2*g})

for i in range(10):

    print(f())

# 0.008471076

# 0.008201109

# 0.007946267

# ...

```

## Installation

Make sure to install all the needed GPU drivers (for GPU support, not mandatory) and install JAX as described in this [**guide**](https://symjax.readthedocs.io/en/latest/user/installation.html).