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A Symbolic JAX software

Project description

SymJAX logo

SymJAX: symbolic CPU/GPU/TPU programming Continuous integration doctest license Code style: black

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

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.

Examples

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.

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