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add feedforward substrate and hyperneat and related example;

Browse Source 
see https://github.com/EMI-Group/tensorneat/issues/9;
fix bugs in genome visualize (add plt.close())
This commit is contained in:
wls2002
2024-11-23 15:32:28 +08:00
parent 5fdaf6806c
commit 1b7e49293a
13 changed files with 1565 additions and 1 deletions
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63
examples/func_fit/xor_hyperneat_feedforward.py Normal file
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import jax.numpy as jnp
from tensorneat.pipeline import Pipeline
from tensorneat.algorithm.neat import NEAT
from tensorneat.algorithm.hyperneat import HyperNEATFeedForward, MLPSubstrate
from tensorneat.genome import DefaultGenome
from tensorneat.common import ACT
from tensorneat.problem.func_fit import XOR3d
if __name__ == "__main__":
pipeline = Pipeline(
algorithm=HyperNEATFeedForward(
substrate=MLPSubstrate(
layers=[4, 5, 5, 5, 1], coor_range=(-5.0, 5.0, -5.0, 5.0)
),
neat=NEAT(
pop_size=10000,
species_size=20,
survival_threshold=0.01,
genome=DefaultGenome(
num_inputs=4, # size of query coors
num_outputs=1,
init_hidden_layers=(),
output_transform=ACT.tanh,
),
),
activation=ACT.tanh,
output_transform=ACT.sigmoid,
),
problem=XOR3d(),
generation_limit=1000,
fitness_target=-1e-5,
)
# initialize state
state = pipeline.setup()
# print(state)
# run until terminate
state, best = pipeline.auto_run(state)
# show result
pipeline.show(state, best)
# visualize cppn
cppn_genome = pipeline.algorithm.neat.genome
cppn_network = cppn_genome.network_dict(state, *best)
cppn_genome.visualize(cppn_network, save_path="./imgs/cppn_network.svg")
# visualize hyperneat genome
hyperneat_genome = pipeline.algorithm.hyper_genome
# use cppn to calculate the weights in hyperneat genome
# return seqs, nodes, conns, u_conns
_, hyperneat_nodes, hyperneat_conns, _ = pipeline.algorithm.transform(state, best)
# mutate the connection with weight 0 (to visualize the network rather the substrate)
hyperneat_conns = jnp.where(
hyperneat_conns[:, 2][:, None] == 0, jnp.nan, hyperneat_conns
)
hyperneat_network = hyperneat_genome.network_dict(
state, hyperneat_nodes, hyperneat_conns
)
hyperneat_genome.visualize(
hyperneat_network, save_path="./imgs/hyperneat_network.svg"
)

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imgs/cppn_network.svg Normal file
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After

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4
src/tensorneat/algorithm/hyperneat/__init__.py
View File

@@ -1,2 +1,4 @@
from .hyperneat import HyperNEAT from .hyperneat import HyperNEAT
from .substrate import BaseSubstrate, DefaultSubstrate, FullSubstrate from .hyperneat_feedforward import HyperNEATFeedForward
from .substrate import BaseSubstrate, DefaultSubstrate, FullSubstrate, MLPSubstrate

2
src/tensorneat/algorithm/hyperneat/hyperneat.py
View File

@@ -78,6 +78,8 @@ class HyperNEAT(BaseAlgorithm):
query_res query_res
), self.substrate.make_conns(query_res) ), self.substrate.make_conns(query_res)
h_nodes, h_conns = jax.device_put([h_nodes, h_conns])
return self.hyper_genome.transform(state, h_nodes, h_conns) return self.hyper_genome.transform(state, h_nodes, h_conns)
def forward(self, state, transformed, inputs): def forward(self, state, transformed, inputs):

44
src/tensorneat/algorithm/hyperneat/hyperneat_feedforward.py Normal file
View File

@@ -0,0 +1,44 @@
"""
HyperNEAT with Feedforward Substrate and genome
"""
from typing import Callable
from .substrate import *
from .hyperneat import HyperNEAT, HyperNEATNode, HyperNEATConn
from tensorneat.common import ACT, AGG
from tensorneat.algorithm import NEAT
from tensorneat.genome import DefaultGenome
class HyperNEATFeedForward(HyperNEAT):
def __init__(
self,
substrate: BaseSubstrate,
neat: NEAT,
weight_threshold: float = 0.3,
max_weight: float = 5.0,
aggregation: Callable = AGG.sum,
activation: Callable = ACT.sigmoid,
output_transform: Callable = ACT.sigmoid,
):
assert (
substrate.query_coors.shape[1] == neat.num_inputs
), "Query coors of Substrate should be equal to NEAT input size"
assert substrate.connection_type == "feedforward", "Substrate should be feedforward"
self.substrate = substrate
self.neat = neat
self.weight_threshold = weight_threshold
self.max_weight = max_weight
self.hyper_genome = DefaultGenome(
num_inputs=substrate.num_inputs,
num_outputs=substrate.num_outputs,
max_nodes=substrate.nodes_cnt,
max_conns=substrate.conns_cnt,
node_gene=HyperNEATNode(aggregation, activation),
conn_gene=HyperNEATConn(),
output_transform=output_transform,
)
self.pop_size = neat.pop_size

1
src/tensorneat/algorithm/hyperneat/substrate/__init__.py
View File

@@ -1,3 +1,4 @@
from .base import BaseSubstrate from .base import BaseSubstrate
from .default import DefaultSubstrate from .default import DefaultSubstrate
from .full import FullSubstrate from .full import FullSubstrate
from .mlp import MLPSubstrate

2
src/tensorneat/algorithm/hyperneat/substrate/base.py
View File

@@ -3,6 +3,8 @@ from tensorneat.common import StatefulBaseClass
class BaseSubstrate(StatefulBaseClass): class BaseSubstrate(StatefulBaseClass):
connection_type = None
def make_nodes(self, query_res): def make_nodes(self, query_res):
raise NotImplementedError raise NotImplementedError

3
src/tensorneat/algorithm/hyperneat/substrate/default.py
View File

@@ -6,6 +6,9 @@ from tensorneat.genome.utils import set_conn_attrs
class DefaultSubstrate(BaseSubstrate): class DefaultSubstrate(BaseSubstrate):
connection_type = "recurrent"
def __init__(self, num_inputs, num_outputs, coors, nodes, conns): def __init__(self, num_inputs, num_outputs, coors, nodes, conns):
self.inputs = num_inputs self.inputs = num_inputs
self.outputs = num_outputs self.outputs = num_outputs

4
src/tensorneat/algorithm/hyperneat/substrate/full.py
View File

@@ -3,6 +3,9 @@ from .default import DefaultSubstrate
class FullSubstrate(DefaultSubstrate): class FullSubstrate(DefaultSubstrate):
connection_type = "recurrent"
def __init__( def __init__(
self, self,
input_coors=((-1, -1), (0, -1), (1, -1)), input_coors=((-1, -1), (0, -1), (1, -1)),
@@ -60,6 +63,7 @@ def analysis_substrate(input_coors, output_coors, hidden_coors):
) # input_idx, output_idx, weight ) # input_idx, output_idx, weight
conns[:, :2] = correspond_keys conns[:, :2] = correspond_keys
print(query_coors, nodes, conns)
return query_coors, nodes, conns return query_coors, nodes, conns

94
src/tensorneat/algorithm/hyperneat/substrate/mlp.py Normal file
View File

@@ -0,0 +1,94 @@
from typing import List, Tuple
import numpy as np
from .default import DefaultSubstrate
class MLPSubstrate(DefaultSubstrate):
connection_type = "feedforward"
def __init__(self, layers: List[int], coor_range: Tuple[float] = (-1, 1, -1, 1)):
"""
layers: list of integers, the number of neurons in each layer
coor_range: tuple of 4 floats, the range of the substrate. (x_min, x_max, y_min, y_max)
"""
assert len(layers) >= 2, "The number of layers should be at least 2"
for layer in layers:
assert layer > 0, "The number of neurons in each layer should be positive"
assert coor_range[0] < coor_range[1], "x_min should be less than x_max"
assert coor_range[2] < coor_range[3], "y_min should be less than y_max"
num_inputs = layers[0]
num_outputs = layers[-1]
query_coors, nodes, conns = analysis_substrate(layers, coor_range)
super().__init__(num_inputs, num_outputs, query_coors, nodes, conns)
def analysis_substrate(layers, coor_range):
x_min, x_max, y_min, y_max = coor_range
layer_cnt = len(layers)
y_interval = (y_max - y_min) / (layer_cnt - 1)
# prepare nodes indices and coordinates
node_coors = {}
input_indices = list(range(layers[0]))
input_coors = cal_coors(layers[0], x_min, x_max, y_min)
output_indices = list(range(layers[0], layers[0] + layers[-1]))
output_coors = cal_coors(layers[-1], x_min, x_max, y_max)
if layer_cnt == 2: # only input and output layers
node_layers = [input_indices, output_indices]
node_coors = [*input_coors, *output_coors]
else:
hidden_indices, hidden_coors = [], []
hidden_idx = layers[0] + layers[-1]
hidden_layers = []
for layer_idx in range(1, layer_cnt - 1):
y_coor = y_min + layer_idx * y_interval
indices = list(range(hidden_idx, hidden_idx + layers[layer_idx]))
coors = cal_coors(layers[layer_idx], x_min, x_max, y_coor)
hidden_layers.append(indices)
hidden_indices.extend(indices)
hidden_coors.extend(coors)
hidden_idx += layers[layer_idx]
node_layers = [
input_indices,
*hidden_layers,
output_indices,
] # the layers of hyperneat network
node_coors = [*input_coors, *output_coors, *hidden_coors]
# prepare connections
query_coors, correspond_keys = [], []
for layer_idx in range(layer_cnt - 1):
for i in range(layers[layer_idx]):
for j in range(layers[layer_idx + 1]):
neuron1 = node_layers[layer_idx][i]
neuron2 = node_layers[layer_idx + 1][j]
query_coors.append((*node_coors[neuron1], *node_coors[neuron2]))
correspond_keys.append((neuron1, neuron2))
# nodes order in TensorNEAT must be input->output->hidden
ordered_nodes = [*node_layers[0], *node_layers[-1]]
for layer in node_layers[1:-1]:
ordered_nodes.extend(layer)
nodes = np.array(ordered_nodes)[:, np.newaxis]
conns = np.zeros(
(len(correspond_keys), 3), dtype=np.float32
) # input_idx, output_idx, weight
conns[:, :2] = correspond_keys
query_coors = np.array(query_coors)
return query_coors, nodes, conns
def cal_coors(neuron_cnt, x_min, x_max, y_coor):
if neuron_cnt == 1: # only one neuron in this layer
return [((x_min + x_max) / 2, y_coor)]
x_interval = (x_max - x_min) / (neuron_cnt - 1)
return [(x_min + x_interval * i, y_coor) for i in range(neuron_cnt)]

1
src/tensorneat/genome/default.py
View File

@@ -331,3 +331,4 @@ class DefaultGenome(BaseGenome):
**kwargs, **kwargs,
) )
plt.savefig(save_path, dpi=save_dpi) plt.savefig(save_path, dpi=save_dpi)
plt.close()

6
test/mlp_substrate.py Normal file
View File

@@ -0,0 +1,6 @@
from tensorneat.algorithm.hyperneat.substrate.mlp import MLPSubstrate, analysis_substrate
layers = [3, 4, 2]
coor_range = (-1, 1, -1, 1)
nodes = analysis_substrate(layers, coor_range)
print(nodes)