tensorneat-mend

TensorNEAT is a JAX-based libaray for NeuroEvolution of Augmenting Topologies (NEAT) algorithms, focused on harnessing GPU acceleration to enhance the efficiency of evolving neural network structures for complex tasks. Its core mechanism involves the tensorization of network topologies, enabling parallel processing and significantly boosting computational speed and scalability by leveraging modern hardware accelerators. TensorNEAT is compatible with the EvoX framewrok.

Key Features

JAX-based network for neuroevolution:
- Batch inference across networks with different architectures, GPU-accelerated.
- Evolve networks with irregular structures and fully customize their behavior.
- Visualize the network and represent it in mathematical formulas or codes.
GPU-accelerated NEAT implementation:
- Run NEAT and HyperNEAT on GPUs.
- Achieve 500x speedup compared to CPU-based NEAT libraries.
Rich in extended content:
- Compatible with EvoX for multi-device and distributed support.
- Test neuroevolution algorithms on advanced RL tasks (Brax, Gymnax).

Basic API Usage

Start your journey with TensorNEAT in a few simple steps:

Import necessary modules:

from tensorneat.pipeline import Pipeline
from tensorneat import algorithm, genome, problem, common

Configure the NEAT algorithm and define a problem:

algorithm = algorithm.NEAT(
    pop_size=10000,
    species_size=20,
    survival_threshold=0.01,
    genome=genome.DefaultGenome(
        num_inputs=3,
        num_outputs=1,
        output_transform=common.ACT.sigmoid,
    ),
)
problem = problem.XOR3d()

Initialize the pipeline and run:

pipeline = Pipeline(
    algorithm,
    problem,
    generation_limit=200,
    fitness_target=-1e-6,
    seed=42,
)
state = pipeline.setup()
# run until termination
state, best = pipeline.auto_run(state)
# show results
pipeline.show(state, best)

Obtain result in a few generations:

Fitness limit reached!
input: [0. 0. 0.], target: [0.], predict: [0.00037953]
input: [0. 0. 1.], target: [1.], predict: [0.9990619]
input: [0. 1. 0.], target: [1.], predict: [0.9991497]
input: [0. 1. 1.], target: [0.], predict: [0.0004661]
input: [1. 0. 0.], target: [1.], predict: [0.998262]
input: [1. 0. 1.], target: [0.], predict: [0.00077246]
input: [1. 1. 0.], target: [0.], predict: [0.00082464]
input: [1. 1. 1.], target: [1.], predict: [0.99909043]
loss: 8.861396736392635e-07

Visualize the best network:

network = algorithm.genome.network_dict(state, *best)
algorithm.genome.visualize(network, save_path="./imgs/xor_network.svg")

Transform the network to latex formulas or python codes:

from tensorneat.common.sympy_tools import to_latex_code, to_python_code

sympy_res = algorithm.genome.sympy_func(
    state, network, sympy_output_transform=ACT.obtain_sympy(ACT.sigmoid)
)
latex_code = to_latex_code(*sympy_res)
print(latex_code)

python_code = to_python_code(*sympy_res)
print(python_code)

Obtain latex formulas:

\begin{align}
h_{0} &= \frac{1}{2.83 e^{5.66 h_{1} - 6.08 h_{2} - 3.03 i_{2}} + 1}\newline
h_{1} &= \frac{1}{0.3 e^{- 4.8 h_{2} + 9.22 i_{0} + 8.09 i_{1} - 10.24 i_{2}} + 1}\newline
h_{2} &= \frac{1}{0.27 e^{4.28 i_{1}} + 1}\newline
o_{0} &= \frac{1}{0.68 e^{- 20.86 h_{0} + 11.12 h_{1} + 14.22 i_{0} - 1.96 i_{2}} + 1}\newline
\end{align}

Obtain python codes:

h = np.zeros(3)
o = np.zeros(1)
h[0] = 1/(2.825013*exp(5.660946*h[1] - 6.083459*h[2] - 3.033361*i[2]) + 1)
h[1] = 1/(0.300038*exp(-4.802896*h[2] + 9.215506*i[0] + 8.091845*i[1] - 10.241107*i[2]) + 1)
h[2] = 1/(0.269965*exp(4.279962*i[1]) + 1)
o[0] = 1/(0.679321*exp(-20.860441*h[0] + 11.122242*h[1] + 14.216276*i[0] - 1.961642*i[2]) + 1)

Installation

Install tensorneat from the GitHub source code:

pip install git+https://github.com/EMI-Group/tensorneat.git

Community & Support

Engage in discussions and share your experiences on GitHub Discussion Board.
Join our QQ group (ID: 297969717).

Citing TensorNEAT

If you use TensorNEAT in your research and want to cite it in your work, please use:

@article{tensorneat,
  title = {{Tensorized} {NeuroEvolution} of {Augmenting} {Topologies} for {GPU} {Acceleration}},
  author = {Wang, Lishuang and Zhao, Mengfei and Liu, Enyu and Sun, Kebin and Cheng, Ran},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)},
  year = {2024}
}