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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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43
tensorneat/genome/gene/node/bias.py
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@@ -1,5 +1,6 @@
from typing import Tuple
from typing import Union, Sequence, Callable, Optional
import numpy as np
import jax, jax.numpy as jnp
import sympy as sp
from tensorneat.common import (
@@ -12,10 +13,10 @@ from tensorneat.common import (
convert_to_sympy,
)
from . import BaseNodeGene
from . import BaseNode
class BiasNode(BaseNodeGene):
class BiasNode(BaseNode):
"""
Default node gene, with the same behavior as in NEAT-python.
The attribute response is removed.
@@ -27,31 +28,46 @@ class BiasNode(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,
aggregation_default: callable = Agg.sum,
aggregation_options: Tuple = (Agg.sum,),
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,
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_options: Tuple = (Act.sigmoid,),
activation_default: Optional[Callable] = None,
activation_options: Union[Callable, Sequence[Callable]] = Act.sigmoid,
activation_replace_rate: float = 0.1,
):
super().__init__()
if isinstance(aggregation_options, Callable):
aggregation_options = [aggregation_options]
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.aggregation_default = aggregation_options.index(aggregation_default)
self.aggregation_options = aggregation_options
self.aggregation_indices = jnp.arange(len(aggregation_options))
self.aggregation_indices = np.arange(len(aggregation_options))
self.aggregation_replace_rate = aggregation_replace_rate
self.activation_default = activation_options.index(activation_default)
self.activation_options = activation_options
self.activation_indices = jnp.arange(len(activation_options))
self.activation_indices = np.arange(len(activation_options))
self.activation_replace_rate = activation_replace_rate
def new_identity_attrs(self, state):
@@ -62,6 +78,7 @@ class BiasNode(BaseNodeGene):
def new_random_attrs(self, state, randkey):
k1, k2, k3 = jax.random.split(randkey, num=3)
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)
agg = jax.random.choice(k2, self.aggregation_indices)
act = jax.random.choice(k3, self.activation_indices)
@@ -80,7 +97,7 @@ class BiasNode(BaseNodeGene):
self.bias_mutate_rate,
self.bias_replace_rate,
)
bias = jnp.clip(bias, self.bias_lower_bound, self.bias_upper_bound)
agg = mutate_int(
k2, agg, self.aggregation_indices, self.aggregation_replace_rate
)