44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
from tensorneat.pipeline import Pipeline
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from tensorneat.algorithm.neat import NEAT
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from tensorneat.algorithm.hyperneat import HyperNEAT, FullSubstrate
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from tensorneat.genome import DefaultGenome
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from tensorneat.common import ACT
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from tensorneat.problem.func_fit import XOR3d
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if __name__ == "__main__":
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pipeline = Pipeline(
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algorithm=HyperNEAT(
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substrate=FullSubstrate(
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input_coors=((-1, -1), (-0.33, -1), (0.33, -1), (1, -1)),
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hidden_coors=((-1, 0), (0, 0), (1, 0)),
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output_coors=((0, 1),),
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),
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neat=NEAT(
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pop_size=10000,
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species_size=20,
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survival_threshold=0.01,
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genome=DefaultGenome(
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num_inputs=4, # size of query coors
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num_outputs=1,
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init_hidden_layers=(),
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output_transform=ACT.tanh,
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),
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),
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activation=ACT.tanh,
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activate_time=10,
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output_transform=ACT.sigmoid,
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),
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problem=XOR3d(),
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generation_limit=300,
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fitness_target=-1e-6,
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)
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# initialize state
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state = pipeline.setup()
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# print(state)
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# run until terminate
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state, best = pipeline.auto_run(state)
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# show result
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pipeline.show(state, best)
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