1b7e49293a
see https://github.com/EMI-Group/tensorneat/issues/9; fix bugs in genome visualize (add plt.close())
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
import jax.numpy as jnp
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from tensorneat.pipeline import Pipeline
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from tensorneat.algorithm.neat import NEAT
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from tensorneat.algorithm.hyperneat import HyperNEATFeedForward, MLPSubstrate
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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=HyperNEATFeedForward(
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substrate=MLPSubstrate(
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layers=[4, 5, 5, 5, 1], coor_range=(-5.0, 5.0, -5.0, 5.0)
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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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output_transform=ACT.sigmoid,
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),
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problem=XOR3d(),
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generation_limit=1000,
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fitness_target=-1e-5,
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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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# visualize cppn
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cppn_genome = pipeline.algorithm.neat.genome
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cppn_network = cppn_genome.network_dict(state, *best)
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cppn_genome.visualize(cppn_network, save_path="./imgs/cppn_network.svg")
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# visualize hyperneat genome
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hyperneat_genome = pipeline.algorithm.hyper_genome
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# use cppn to calculate the weights in hyperneat genome
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# return seqs, nodes, conns, u_conns
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_, hyperneat_nodes, hyperneat_conns, _ = pipeline.algorithm.transform(state, best)
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# mutate the connection with weight 0 (to visualize the network rather the substrate)
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hyperneat_conns = jnp.where(
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hyperneat_conns[:, 2][:, None] == 0, jnp.nan, hyperneat_conns
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)
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hyperneat_network = hyperneat_genome.network_dict(
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state, hyperneat_nodes, hyperneat_conns
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)
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hyperneat_genome.visualize(
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hyperneat_network, save_path="./imgs/hyperneat_network.svg"
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)
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