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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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7
tensorneat/genome/base.py
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@@ -3,7 +3,7 @@ from typing import Callable, Sequence
import numpy as np
import jax
from jax import vmap, numpy as jnp
from .gene import BaseNodeGene, BaseConnGene
from .gene import BaseNode, BaseConn
from .operations import BaseMutation, BaseCrossover, BaseDistance
from tensorneat.common import (
State,
@@ -22,8 +22,8 @@ class BaseGenome(StatefulBaseClass):
num_outputs: int,
max_nodes: int,
max_conns: int,
node_gene: BaseNodeGene,
conn_gene: BaseConnGene,
node_gene: BaseNode,
conn_gene: BaseConn,
mutation: BaseMutation,
crossover: BaseCrossover,
distance: BaseDistance,
@@ -92,7 +92,6 @@ class BaseGenome(StatefulBaseClass):
self.output_idx = np.array(layer_indices[-1])
self.all_init_nodes = np.array(all_init_nodes)
self.all_init_conns = np.c_[all_init_conns_in_idx, all_init_conns_out_idx]
print(self.output_idx)
def setup(self, state=State()):
state = self.node_gene.setup(state)

6
tensorneat/genome/default.py
View File

@@ -6,7 +6,7 @@ import numpy as np
import sympy as sp
from .base import BaseGenome
from .gene import DefaultNodeGene, DefaultConnGene
from .gene import DefaultNode, DefaultConn
from .operations import DefaultMutation, DefaultCrossover, DefaultDistance
from .utils import unflatten_conns, extract_node_attrs, extract_conn_attrs
@@ -31,8 +31,8 @@ class DefaultGenome(BaseGenome):
num_outputs: int,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(),
conn_gene=DefaultConnGene(),
node_gene=DefaultNode(),
conn_gene=DefaultConn(),
mutation=DefaultMutation(),
crossover=DefaultCrossover(),
distance=DefaultDistance(),

4
tensorneat/genome/gene/conn/__init__.py
View File

@@ -1,2 +1,2 @@
from .base import BaseConnGene
from .default import DefaultConnGene
from .base import BaseConn
from .default import DefaultConn

5
tensorneat/genome/gene/conn/base.py
View File

@@ -1,8 +1,7 @@
import jax
from .. import BaseGene
from ..base import BaseGene
class BaseConnGene(BaseGene):
class BaseConn(BaseGene):
"Base class for connection genes."
fixed_attrs = ["input_index", "output_index"]

10
tensorneat/genome/gene/conn/default.py
View File

@@ -2,10 +2,10 @@ import jax.numpy as jnp
import jax.random
import sympy as sp
from tensorneat.common import mutate_float
from .base import BaseConnGene
from .base import BaseConn
class DefaultConnGene(BaseConnGene):
class DefaultConn(BaseConn):
"Default connection gene, with the same behavior as in NEAT-python."
custom_attrs = ["weight"]
@@ -14,9 +14,9 @@ class DefaultConnGene(BaseConnGene):
self,
weight_init_mean: float = 0.0,
weight_init_std: float = 1.0,
weight_mutate_power: float = 0.5,
weight_mutate_rate: float = 0.8,
weight_replace_rate: float = 0.1,
weight_mutate_power: float = 0.15,
weight_mutate_rate: float = 0.2,
weight_replace_rate: float = 0.015,
):
super().__init__()
self.weight_init_mean = weight_init_mean

4
tensorneat/genome/gene/node/__init__.py
View File

@@ -1,3 +1,3 @@
from .base import BaseNodeGene
from .default import DefaultNodeGene
from .base import BaseNode
from .default import DefaultNode
from .bias import BiasNode

2
tensorneat/genome/gene/node/base.py
View File

@@ -2,7 +2,7 @@ import jax, jax.numpy as jnp
from .. import BaseGene
class BaseNodeGene(BaseGene):
class BaseNode(BaseGene):
"Base class for node genes."
fixed_attrs = ["index"]

43
tensorneat/genome/gene/node/bias.py
View File

@@ -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
)

50
tensorneat/genome/gene/node/default.py
View File

@@ -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
)

4
tensorneat/genome/operations/mutation/default.py
View File

@@ -23,9 +23,9 @@ from ...utils import (
class DefaultMutation(BaseMutation):
def __init__(
self,
conn_add: float = 0.2,
conn_add: float = 0.1,
conn_delete: float = 0,
node_add: float = 0.2,
node_add: float = 0.1,
node_delete: float = 0,
):
self.conn_add = conn_add

6
tensorneat/genome/recurrent.py
View File

@@ -3,7 +3,7 @@ from jax import vmap, numpy as jnp
from .utils import unflatten_conns
from .base import BaseGenome
from .gene import DefaultNodeGene, DefaultConnGene
from .gene import DefaultNode, DefaultConn
from .operations import DefaultMutation, DefaultCrossover, DefaultDistance
from .utils import unflatten_conns, extract_node_attrs, extract_conn_attrs
@@ -20,8 +20,8 @@ class RecurrentGenome(BaseGenome):
num_outputs: int,
max_nodes=50,
max_conns=100,
node_gene=DefaultNodeGene(),
conn_gene=DefaultConnGene(),
node_gene=DefaultNode(),
conn_gene=DefaultConn(),
mutation=DefaultMutation(),
crossover=DefaultCrossover(),
distance=DefaultDistance(),