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adjust default parameter; successful run recurrent-xor example

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root
2024-07-11 10:57:43 +08:00
parent 4a631f9464
commit 9bad577d89
18 changed files with 118 additions and 136 deletions
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50
tensorneat/genome/gene/node/default.py
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@@ -1,4 +1,4 @@
from typing import Tuple, Union, Sequence, Callable
from typing import Optional, Union, Sequence, Callable
import numpy as np
import jax, jax.numpy as jnp
@@ -14,10 +14,10 @@ from tensorneat.common import (
convert_to_sympy,
)
from . import BaseNodeGene
from .base import BaseNode
class DefaultNodeGene(BaseNodeGene):
class DefaultNode(BaseNode):
"Default node gene, with the same behavior as in NEAT-python."
custom_attrs = ["bias", "response", "aggregation", "activation"]
@@ -26,18 +26,22 @@ class DefaultNodeGene(BaseNodeGene):
self,
bias_init_mean: float = 0.0,
bias_init_std: float = 1.0,
bias_mutate_power: float = 0.5,
bias_mutate_rate: float = 0.7,
bias_replace_rate: float = 0.1,
bias_mutate_power: float = 0.15,
bias_mutate_rate: float = 0.2,
bias_replace_rate: float = 0.015,
bias_lower_bound: float = -5,
bias_upper_bound: float = 5,
response_init_mean: float = 1.0,
response_init_std: float = 0.0,
response_mutate_power: float = 0.5,
response_mutate_rate: float = 0.7,
response_replace_rate: float = 0.1,
aggregation_default: Callable = Agg.sum,
response_mutate_power: float = 0.15,
response_mutate_rate: float = 0.2,
response_replace_rate: float = 0.015,
response_lower_bound: float = -5,
response_upper_bound: float = 5,
aggregation_default: Optional[Callable] = None,
aggregation_options: Union[Callable, Sequence[Callable]] = Agg.sum,
aggregation_replace_rate: float = 0.1,
activation_default: Callable = Act.sigmoid,
activation_default: Optional[Callable] = None,
activation_options: Union[Callable, Sequence[Callable]] = Act.sigmoid,
activation_replace_rate: float = 0.1,
):
@@ -48,17 +52,26 @@ class DefaultNodeGene(BaseNodeGene):
if isinstance(activation_options, Callable):
activation_options = [activation_options]
if len(aggregation_options) == 1 and aggregation_default is None:
aggregation_default = aggregation_options[0]
if len(activation_options) == 1 and activation_default is None:
activation_default = activation_options[0]
self.bias_init_mean = bias_init_mean
self.bias_init_std = bias_init_std
self.bias_mutate_power = bias_mutate_power
self.bias_mutate_rate = bias_mutate_rate
self.bias_replace_rate = bias_replace_rate
self.bias_lower_bound = bias_lower_bound
self.bias_upper_bound = bias_upper_bound
self.response_init_mean = response_init_mean
self.response_init_std = response_init_std
self.response_mutate_power = response_mutate_power
self.response_mutate_rate = response_mutate_rate
self.response_replace_rate = response_replace_rate
self.reponse_lower_bound = response_lower_bound
self.response_upper_bound = response_upper_bound
self.aggregation_default = aggregation_options.index(aggregation_default)
self.aggregation_options = aggregation_options
@@ -71,16 +84,21 @@ class DefaultNodeGene(BaseNodeGene):
self.activation_replace_rate = activation_replace_rate
def new_identity_attrs(self, state):
return jnp.array(
[0, 1, self.aggregation_default, -1]
) # activation=-1 means Act.identity
bias = 0
res = 1
agg = self.aggregation_default
act = self.activation_default
return jnp.array([bias, res, agg, act]) # activation=-1 means Act.identity
def new_random_attrs(self, state, randkey):
k1, k2, k3, k4 = jax.random.split(randkey, num=4)
bias = jax.random.normal(k1, ()) * self.bias_init_std + self.bias_init_mean
bias = jnp.clip(bias, self.bias_lower_bound, self.bias_upper_bound)
res = (
jax.random.normal(k2, ()) * self.response_init_std + self.response_init_mean
)
res = jnp.clip(res, self.reponse_lower_bound, self.response_upper_bound)
agg = jax.random.choice(k3, self.aggregation_indices)
act = jax.random.choice(k4, self.activation_indices)
@@ -98,7 +116,7 @@ class DefaultNodeGene(BaseNodeGene):
self.bias_mutate_rate,
self.bias_replace_rate,
)
bias = jnp.clip(bias, self.bias_lower_bound, self.bias_upper_bound)
res = mutate_float(
k2,
res,
@@ -108,7 +126,7 @@ class DefaultNodeGene(BaseNodeGene):
self.response_mutate_rate,
self.response_replace_rate,
)
res = jnp.clip(res, self.reponse_lower_bound, self.response_upper_bound)
agg = mutate_int(
k4, agg, self.aggregation_indices, self.aggregation_replace_rate
)