45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
from tensorneat.pipeline import Pipeline
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from tensorneat.algorithm.neat import NEAT
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from tensorneat.genome import DefaultGenome, DefaultNodeGene, DefaultMutation
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from tensorneat.problem.func_fit import XOR3d
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from tensorneat.common import Act, Agg
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if __name__ == "__main__":
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pipeline = Pipeline(
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algorithm=NEAT(
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pop_size=10000,
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species_size=20,
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compatibility_threshold=2,
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survival_threshold=0.01,
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genome=DefaultGenome(
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num_inputs=3,
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num_outputs=1,
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init_hidden_layers=(),
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node_gene=DefaultNodeGene(
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activation_default=Act.tanh,
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activation_options=Act.tanh,
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aggregation_default=Agg.sum,
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aggregation_options=Agg.sum,
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),
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output_transform=Act.standard_sigmoid, # the activation function for output node
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mutation=DefaultMutation(
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node_add=0.1,
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conn_add=0.1,
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node_delete=0,
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conn_delete=0,
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),
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),
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),
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problem=XOR3d(),
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generation_limit=500,
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fitness_target=-1e-8,
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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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