Files
tensorneat-mend/tensorneat/examples/func_fit/xor3d_hyperneat.py
wls2002 1fe5d5fca2 disable activation in the output node of network;
we recommend to use output_transform;
change hyperparameters (strong) in XOR example;
2024-05-22 11:09:25 +08:00

55 lines
1.9 KiB
Python

from pipeline import Pipeline
from algorithm.neat import *
from algorithm.hyperneat import *
from utils import Act
from problem.func_fit import XOR3d
if __name__ == '__main__':
pipeline = Pipeline(
algorithm=HyperNEAT(
substrate=FullSubstrate(
input_coors=[(-1, -1), (0.333, -1), (-0.333, -1), (1, -1)],
hidden_coors=[
(-1, -0.5), (0.333, -0.5), (-0.333, -0.5), (1, -0.5),
(-1, 0), (0.333, 0), (-0.333, 0), (1, 0),
(-1, 0.5), (0.333, 0.5), (-0.333, 0.5), (1, 0.5),
],
output_coors=[(0, 1), ],
),
neat=NEAT(
species=DefaultSpecies(
genome=DefaultGenome(
num_inputs=4, # [-1, -1, -1, 0]
num_outputs=1,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(
activation_default=Act.tanh,
activation_options=(Act.tanh,),
),
output_transform=Act.tanh, # the activation function for output node in NEAT
),
pop_size=10000,
species_size=10,
compatibility_threshold=3.5,
survival_threshold=0.03,
),
),
activation=Act.tanh,
activate_time=10,
output_transform=Act.sigmoid, # the activation function for output node in HyperNEAT
),
problem=XOR3d(),
generation_limit=300,
fitness_target=-1e-6
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
# show result
pipeline.show(state, best)