tony/tensorneat-mend
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix bugs

Browse Source
This commit is contained in:
root
2024-07-12 02:25:57 +08:00
parent 3194678a15
commit 5fc63fdaf1
28 changed files with 351 additions and 142 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
examples/brax/halfcheetah.py
View File

@@ -3,7 +3,7 @@ from tensorneat.algorithm.neat import NEAT
from tensorneat.genome import DefaultGenome, BiasNode
from tensorneat.problem.rl import BraxEnv
from tensorneat.common import Act, Agg
from tensorneat.common import ACT, AGG
import jax
@@ -26,10 +26,10 @@ if __name__ == "__main__":
num_outputs=6,
init_hidden_layers=(),
node_gene=BiasNode(
activation_options=Act.tanh,
aggregation_options=Agg.sum,
activation_options=ACT.tanh,
aggregation_options=AGG.sum,
),
output_transform=Act.standard_tanh,
output_transform=ACT.standard_tanh,
),
),
problem=BraxEnv(

8
examples/brax/walker2d.py
View File

@@ -3,7 +3,7 @@ from tensorneat.algorithm.neat import NEAT
from tensorneat.genome import DefaultGenome, BiasNode
from tensorneat.problem.rl import BraxEnv
from tensorneat.common import Act, Agg
from tensorneat.common import ACT, AGG
import jax, jax.numpy as jnp
@@ -26,10 +26,10 @@ if __name__ == "__main__":
num_outputs=6,
init_hidden_layers=(),
node_gene=BiasNode(
activation_options=Act.tanh,
aggregation_options=Agg.sum,
activation_options=ACT.tanh,
aggregation_options=AGG.sum,
),
output_transform=Act.standard_tanh,
output_transform=ACT.standard_tanh,
),
),
problem=BraxEnv(

8
examples/func_fit/custom_func_fit.py
View File

@@ -4,7 +4,7 @@ from tensorneat.pipeline import Pipeline
from tensorneat.algorithm.neat import NEAT
from tensorneat.genome import DefaultGenome, DefaultNode, DefaultMutation, BiasNode
from tensorneat.problem.func_fit import CustomFuncFit
from tensorneat.common import Act, Agg
from tensorneat.common import ACT, AGG
def pagie_polynomial(inputs):
@@ -35,10 +35,10 @@ if __name__ == "__main__":
num_outputs=1,
init_hidden_layers=(),
node_gene=BiasNode(
activation_options=[Act.identity, Act.inv, Act.square],
aggregation_options=[Agg.sum, Agg.product],
activation_options=[ACT.identity, ACT.inv, ACT.square],
aggregation_options=[AGG.sum, AGG.product],
),
output_transform=Act.identity,
output_transform=ACT.identity,
),
),
problem=custom_problem,

4
examples/func_fit/xor.py
View File

@@ -2,7 +2,7 @@ from tensorneat.pipeline import Pipeline
from tensorneat.algorithm.neat import NEAT
from tensorneat.genome import DefaultGenome
from tensorneat.problem.func_fit import XOR3d
from tensorneat.common import Act
from tensorneat.common import ACT
if __name__ == "__main__":
pipeline = Pipeline(
@@ -14,7 +14,7 @@ if __name__ == "__main__":
num_inputs=3,
num_outputs=1,
init_hidden_layers=(),
output_transform=Act.standard_sigmoid,
output_transform=ACT.standard_sigmoid,
),
),
problem=XOR3d(),

8
examples/func_fit/xor_hyperneat.py
View File

@@ -2,7 +2,7 @@ from tensorneat.pipeline import Pipeline
from tensorneat.algorithm.neat import NEAT
from tensorneat.algorithm.hyperneat import HyperNEAT, FullSubstrate
from tensorneat.genome import DefaultGenome
from tensorneat.common import Act
from tensorneat.common import ACT
from tensorneat.problem.func_fit import XOR3d
@@ -22,12 +22,12 @@ if __name__ == "__main__":
num_inputs=4, # size of query coors
num_outputs=1,
init_hidden_layers=(),
output_transform=Act.standard_tanh,
output_transform=ACT.standard_tanh,
),
),
activation=Act.tanh,
activation=ACT.tanh,
activate_time=10,
output_transform=Act.standard_sigmoid,
output_transform=ACT.standard_sigmoid,
),
problem=XOR3d(),
generation_limit=300,

4
examples/func_fit/xor_recurrent.py
View File

@@ -2,7 +2,7 @@ from tensorneat.pipeline import Pipeline
from tensorneat.algorithm.neat import NEAT
from tensorneat.genome import RecurrentGenome
from tensorneat.problem.func_fit import XOR3d
from tensorneat.common import Act, Agg
from tensorneat.common import ACT, AGG
if __name__ == "__main__":
pipeline = Pipeline(
@@ -14,7 +14,7 @@ if __name__ == "__main__":
num_inputs=3,
num_outputs=1,
init_hidden_layers=(),
output_transform=Act.standard_sigmoid,
output_transform=ACT.standard_sigmoid,
activate_time=10,
),
),

6
examples/gymnax/arcbot.py
View File

@@ -5,7 +5,7 @@ from tensorneat.algorithm.neat import NEAT
from tensorneat.genome import DefaultGenome, BiasNode
from tensorneat.problem.rl import GymNaxEnv
from tensorneat.common import Act, Agg
from tensorneat.common import ACT, AGG
@@ -24,8 +24,8 @@ if __name__ == "__main__":
num_outputs=3,
init_hidden_layers=(),
node_gene=BiasNode(
activation_options=Act.tanh,
aggregation_options=Agg.sum,
activation_options=ACT.tanh,
aggregation_options=AGG.sum,
),
output_transform=jnp.argmax,
),

6
examples/gymnax/cartpole.py
View File

@@ -5,7 +5,7 @@ from tensorneat.algorithm.neat import NEAT
from tensorneat.genome import DefaultGenome, BiasNode
from tensorneat.problem.rl import GymNaxEnv
from tensorneat.common import Act, Agg
from tensorneat.common import ACT, AGG
@@ -24,8 +24,8 @@ if __name__ == "__main__":
num_outputs=2,
init_hidden_layers=(),
node_gene=BiasNode(
activation_options=Act.tanh,
aggregation_options=Agg.sum,
activation_options=ACT.tanh,
aggregation_options=AGG.sum,
),
output_transform=jnp.argmax,
),

6
examples/gymnax/cartpole_hyperneat.py
View File

@@ -4,7 +4,7 @@ from tensorneat.pipeline import Pipeline
from tensorneat.algorithm.neat import NEAT
from tensorneat.algorithm.hyperneat import HyperNEAT, FullSubstrate
from tensorneat.genome import DefaultGenome
from tensorneat.common import Act
from tensorneat.common import ACT
from tensorneat.problem import GymNaxEnv
@@ -27,10 +27,10 @@ if __name__ == "__main__":
num_inputs=4, # size of query coors
num_outputs=1,
init_hidden_layers=(),
output_transform=Act.standard_tanh,
output_transform=ACT.standard_tanh,
),
),
activation=Act.tanh,
activation=ACT.tanh,
activate_time=10,
output_transform=jnp.argmax,
),

8
examples/gymnax/mountain_car_continuous.py
View File

@@ -5,7 +5,7 @@ from tensorneat.algorithm.neat import NEAT
from tensorneat.genome import DefaultGenome, BiasNode
from tensorneat.problem.rl import GymNaxEnv
from tensorneat.common import Act, Agg
from tensorneat.common import ACT, AGG
@@ -21,10 +21,10 @@ if __name__ == "__main__":
num_outputs=1,
init_hidden_layers=(),
node_gene=BiasNode(
activation_options=Act.tanh,
aggregation_options=Agg.sum,
activation_options=ACT.tanh,
aggregation_options=AGG.sum,
),
output_transform=Act.standard_tanh,
output_transform=ACT.standard_tanh,
),
),
problem=GymNaxEnv(

12
examples/interpret_visualize/genome_sympy.ipynb
View File

@@ -11,7 +11,7 @@
"from algorithm.neat.genome.advance import AdvanceInitialize\n",
"from algorithm.neat.gene.node.default_without_response import NodeGeneWithoutResponse\n",
"from utils.graph import topological_sort_python\n",
"from tensorneat.utils import Act, Agg\n",
"from tensorneat.utils import ACT, AGG\n",
"\n",
"import numpy as np"
],
@@ -36,11 +36,11 @@
" max_nodes=30,\n",
" max_conns=50,\n",
" node_gene=NodeGeneWithoutResponse(\n",
" activation_default= Act.identity,\n",
" aggregation_default=Agg.sum,\n",
" # activation_options=(Act.tanh, Act.sigmoid, Act.identity, Act.clamped),\n",
" activation_options=( Act.identity, ),\n",
" aggregation_options=(Agg.sum,),\n",
" activation_default= ACT.identity,\n",
" aggregation_default=AGG.sum,\n",
" # activation_options=(ACT.tanh, ACT.sigmoid, ACT.identity, ACT.clamped),\n",
" activation_options=( ACT.identity, ),\n",
" aggregation_options=(AGG.sum,),\n",
" ),\n",
" # output_transform=jnp.tanh,\n",
")\n",

12
examples/interpret_visualize/visualize_genome.ipynb
View File

File diff suppressed because one or more lines are too long

8
examples/with_evox/example.py
View File

@@ -7,7 +7,7 @@ from tensorneat.examples.with_evox.evox_algorithm_adaptor import EvoXAlgorithmAd
from tensorneat.examples.with_evox.tensorneat_monitor import TensorNEATMonitor
from tensorneat.algorithm import NEAT
from tensorneat.algorithm.neat import DefaultSpecies, DefaultGenome, DefaultNodeGene
from tensorneat.common import Act
from tensorneat.common import ACT
neat_algorithm = NEAT(
species=DefaultSpecies(
@@ -17,10 +17,10 @@ neat_algorithm = NEAT(
max_nodes=200,
max_conns=500,
node_gene=DefaultNodeGene(
activation_options=(Act.standard_tanh,),
activation_default=Act.standard_tanh,
activation_options=(ACT.standard_tanh,),
activation_default=ACT.standard_tanh,
),
output_transform=Act.tanh,
output_transform=ACT.tanh,
),
pop_size=10000,
species_size=10,