45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
import jax, jax.numpy as jnp
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from tensorneat.genome import DefaultGenome
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from tensorneat.common import *
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from tensorneat.common.functions import SympySigmoid
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if __name__ == "__main__":
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genome = DefaultGenome(
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num_inputs=3,
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num_outputs=1,
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max_nodes=50,
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max_conns=500,
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output_transform=ACT.sigmoid,
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)
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state = genome.setup()
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randkey = jax.random.PRNGKey(42)
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nodes, conns = genome.initialize(state, randkey)
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network = genome.network_dict(state, nodes, conns)
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input_idx, output_idx = genome.get_input_idx(), genome.get_output_idx()
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res = genome.sympy_func(state, network, sympy_input_transform=lambda x: 999*x, sympy_output_transform=SympySigmoid)
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(symbols,
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args_symbols,
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input_symbols,
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nodes_exprs,
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output_exprs,
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forward_func,) = res
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print(symbols)
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print(output_exprs[0].subs(args_symbols))
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inputs = jnp.zeros(3)
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print(forward_func(inputs))
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print(genome.forward(state, genome.transform(state, nodes, conns), inputs))
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print(AGG.sympy_module("jax"))
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print(AGG.sympy_module("numpy"))
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print(ACT.sympy_module("jax"))
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print(ACT.sympy_module("numpy")) |