edfb0596e7
change the args for genome.forward. Origin: (state, inputs, transformed) New: (state, transformed, inputs)
118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
import jax, jax.numpy as jnp
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from utils import State
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from .. import BaseAlgorithm
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from .species import *
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from .ga import *
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from .genome import *
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class NEAT(BaseAlgorithm):
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def __init__(
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self,
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species: BaseSpecies,
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):
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self.species = species
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self.genome = species.genome
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def setup(self, state=State()):
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state = self.species.setup(state)
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state = state.register(
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generation=jnp.array(0.0),
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next_node_key=jnp.array(
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max(*self.genome.input_idx, *self.genome.output_idx) + 2,
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dtype=jnp.float32,
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),
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)
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return state
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def ask(self, state: State):
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return self.species.ask(state)
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def tell(self, state: State, fitness):
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k1, k2, randkey = jax.random.split(state.randkey, 3)
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state = state.update(generation=state.generation + 1, randkey=randkey)
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state, winner, loser, elite_mask = self.species.update_species(state, fitness)
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state = self.create_next_generation(state, winner, loser, elite_mask)
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state = self.species.speciate(state)
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return state
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def transform(self, state, individual):
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"""transform the genome into a neural network"""
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nodes, conns = individual
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return self.genome.transform(state, nodes, conns)
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def restore(self, state, transformed):
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return self.genome.restore(state, transformed)
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def forward(self, state, transformed, inputs):
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return self.genome.forward(state, transformed, inputs)
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def update_by_batch(self, state, batch_input, transformed):
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return self.genome.update_by_batch(state, batch_input, transformed)
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@property
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def num_inputs(self):
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return self.genome.num_inputs
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@property
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def num_outputs(self):
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return self.genome.num_outputs
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@property
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def pop_size(self):
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return self.species.pop_size
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def create_next_generation(self, state, winner, loser, elite_mask):
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# prepare random keys
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pop_size = self.species.pop_size
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new_node_keys = jnp.arange(pop_size) + state.next_node_key
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k1, k2, randkey = jax.random.split(state.randkey, 3)
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crossover_randkeys = jax.random.split(k1, pop_size)
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mutate_randkeys = jax.random.split(k2, pop_size)
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wpn, wpc = state.pop_nodes[winner], state.pop_conns[winner]
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lpn, lpc = state.pop_nodes[loser], state.pop_conns[loser]
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# batch crossover
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n_nodes, n_conns = jax.vmap(
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self.genome.execute_crossover, in_axes=(None, 0, 0, 0, 0, 0)
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)(
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state, crossover_randkeys, wpn, wpc, lpn, lpc
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) # new_nodes, new_conns
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# batch mutation
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m_n_nodes, m_n_conns = jax.vmap(
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self.genome.execute_mutation, in_axes=(None, 0, 0, 0, 0)
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)(
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state, mutate_randkeys, n_nodes, n_conns, new_node_keys
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) # mutated_new_nodes, mutated_new_conns
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# elitism don't mutate
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pop_nodes = jnp.where(elite_mask[:, None, None], n_nodes, m_n_nodes)
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pop_conns = jnp.where(elite_mask[:, None, None], n_conns, m_n_conns)
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# update next node key
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all_nodes_keys = pop_nodes[:, :, 0]
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max_node_key = jnp.max(
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jnp.where(jnp.isnan(all_nodes_keys), -jnp.inf, all_nodes_keys)
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)
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next_node_key = max_node_key + 1
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return state.update(
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randkey=randkey,
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pop_nodes=pop_nodes,
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pop_conns=pop_conns,
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next_node_key=next_node_key,
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)
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def member_count(self, state: State):
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return state.member_count
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def generation(self, state: State):
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# to analysis the algorithm
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return state.generation
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