update recurrent genome
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root
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import jax, jax.numpy as jnp
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import jax, jax.numpy as jnp
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from tensorneat.algorithm import NEAT
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from tensorneat.algorithm import NEAT
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from tensorneat.algorithm.neat import DefaultGenome
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from tensorneat.algorithm.neat import DefaultGenome, RecurrentGenome
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key = jax.random.key(0)
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key = jax.random.key(0)
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genome = DefaultGenome(num_inputs=5, num_outputs=3, max_nodes=100, max_conns=500, init_hidden_layers=())
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genome = DefaultGenome(num_inputs=5, num_outputs=3, max_nodes=100, max_conns=500, init_hidden_layers=(1, 2 ,3))
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state = genome.setup()
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state = genome.setup()
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nodes, conns = genome.initialize(state, key)
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nodes, conns = genome.initialize(state, key)
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print(genome.repr(state, nodes, conns))
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print(genome.repr(state, nodes, conns))
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inputs = jnp.array([1, 2, 3, 4, 5])
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transformed = genome.transform(state, nodes, conns)
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outputs = genome.forward(state, transformed, inputs)
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print(outputs)
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network = genome.network_dict(state, nodes, conns)
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print(network)
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genome.visualize(network)
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16
examples/tmp2.py
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16
examples/tmp2.py
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import jax, jax.numpy as jnp
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arr = jnp.ones((10, 10))
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a = jnp.array([
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[1, 2, 3],
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[4, 5, 6]
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])
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def attach_with_inf(arr, idx):
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target_dim = arr.ndim + idx.ndim - 1
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expand_idx = jnp.expand_dims(idx, axis=tuple(range(idx.ndim, target_dim)))
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return jnp.where(expand_idx == 1, jnp.nan, arr[idx])
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b = attach_with_inf(arr, a)
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print(b)
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415
network.svg
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415
network.svg
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<cc:Work>
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<dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
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<dc:date>2024-07-10T15:27:16.806503</dc:date>
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<dc:format>image/svg+xml</dc:format>
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<dc:creator>
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<cc:Agent>
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<dc:title>Matplotlib v3.9.0, https://matplotlib.org/</dc:title>
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After Width: | Height: | Size: 18 KiB |
@@ -34,9 +34,6 @@ class BaseGene(StatefulBaseClass):
|
|||||||
def forward(self, state, attrs, inputs):
|
def forward(self, state, attrs, inputs):
|
||||||
raise NotImplementedError
|
raise NotImplementedError
|
||||||
|
|
||||||
def update_by_batch(self, state, attrs, batch_inputs):
|
|
||||||
raise NotImplementedError
|
|
||||||
|
|
||||||
@property
|
@property
|
||||||
def length(self):
|
def length(self):
|
||||||
return len(self.fixed_attrs) + len(self.custom_attrs)
|
return len(self.fixed_attrs) + len(self.custom_attrs)
|
||||||
|
|||||||
@@ -64,7 +64,7 @@ class BaseGenome(StatefulBaseClass):
|
|||||||
all_init_conns_in_idx.append(in_idx)
|
all_init_conns_in_idx.append(in_idx)
|
||||||
all_init_conns_out_idx.append(out_idx)
|
all_init_conns_out_idx.append(out_idx)
|
||||||
all_init_nodes.extend(in_layer)
|
all_init_nodes.extend(in_layer)
|
||||||
all_init_nodes.extend(layer_indices[-1])
|
all_init_nodes.extend(layer_indices[-1]) # output layer
|
||||||
|
|
||||||
if max_nodes < len(all_init_nodes):
|
if max_nodes < len(all_init_nodes):
|
||||||
raise ValueError(
|
raise ValueError(
|
||||||
|
|||||||
@@ -78,31 +78,34 @@ class DefaultGenome(BaseGenome):
|
|||||||
|
|
||||||
def cond_fun(carry):
|
def cond_fun(carry):
|
||||||
values, idx = carry
|
values, idx = carry
|
||||||
return (idx < self.max_nodes) & (cal_seqs[idx] != I_INF)
|
return (idx < self.max_nodes) & (
|
||||||
|
cal_seqs[idx] != I_INF
|
||||||
|
) # not out of bounds and next node exists
|
||||||
|
|
||||||
def body_func(carry):
|
def body_func(carry):
|
||||||
values, idx = carry
|
values, idx = carry
|
||||||
i = cal_seqs[idx]
|
i = cal_seqs[idx]
|
||||||
|
|
||||||
def input_node():
|
def input_node():
|
||||||
z = self.node_gene.input_transform(state, nodes_attrs[i], values[i])
|
return values
|
||||||
new_values = values.at[i].set(z)
|
|
||||||
return new_values
|
|
||||||
|
|
||||||
def otherwise():
|
def otherwise():
|
||||||
|
# calculate connections
|
||||||
conn_indices = u_conns[:, i]
|
conn_indices = u_conns[:, i]
|
||||||
hit_attrs = attach_with_inf(conns_attrs, conn_indices)
|
hit_attrs = attach_with_inf(conns_attrs, conn_indices) # fetch conn attrs
|
||||||
ins = vmap(self.conn_gene.forward, in_axes=(None, 0, 0))(
|
ins = vmap(self.conn_gene.forward, in_axes=(None, 0, 0))(
|
||||||
state, hit_attrs, values
|
state, hit_attrs, values
|
||||||
)
|
)
|
||||||
|
|
||||||
|
# calculate nodes
|
||||||
z = self.node_gene.forward(
|
z = self.node_gene.forward(
|
||||||
state,
|
state,
|
||||||
nodes_attrs[i],
|
nodes_attrs[i],
|
||||||
ins,
|
ins,
|
||||||
is_output_node=jnp.isin(i, self.output_idx),
|
is_output_node=jnp.isin(nodes[0], self.output_idx), # nodes[0] -> the key of nodes
|
||||||
)
|
)
|
||||||
|
|
||||||
|
# set new value
|
||||||
new_values = values.at[i].set(z)
|
new_values = values.at[i].set(z)
|
||||||
return new_values
|
return new_values
|
||||||
|
|
||||||
|
|||||||
@@ -1,12 +1,13 @@
|
|||||||
from typing import Callable
|
import jax
|
||||||
|
from jax import vmap, numpy as jnp
|
||||||
import jax, jax.numpy as jnp
|
|
||||||
from .utils import unflatten_conns
|
from .utils import unflatten_conns
|
||||||
|
|
||||||
from . import BaseGenome
|
from .base import BaseGenome
|
||||||
|
from .operations import DefaultMutation, DefaultCrossover, DefaultDistance
|
||||||
|
from .utils import unflatten_conns, extract_node_attrs, extract_conn_attrs
|
||||||
from ..gene import DefaultNodeGene, DefaultConnGene
|
from ..gene import DefaultNodeGene, DefaultConnGene
|
||||||
from .operations import DefaultMutation, DefaultCrossover
|
|
||||||
|
|
||||||
|
from tensorneat.common import attach_with_inf
|
||||||
|
|
||||||
class RecurrentGenome(BaseGenome):
|
class RecurrentGenome(BaseGenome):
|
||||||
"""Default genome class, with the same behavior as the NEAT-Python"""
|
"""Default genome class, with the same behavior as the NEAT-Python"""
|
||||||
@@ -17,14 +18,17 @@ class RecurrentGenome(BaseGenome):
|
|||||||
self,
|
self,
|
||||||
num_inputs: int,
|
num_inputs: int,
|
||||||
num_outputs: int,
|
num_outputs: int,
|
||||||
max_nodes = 50,
|
max_nodes=50,
|
||||||
max_conns = 100,
|
max_conns=100,
|
||||||
node_gene=DefaultNodeGene(),
|
node_gene=DefaultNodeGene(),
|
||||||
conn_gene=DefaultConnGene(),
|
conn_gene=DefaultConnGene(),
|
||||||
mutation=DefaultMutation(),
|
mutation=DefaultMutation(),
|
||||||
crossover=DefaultCrossover(),
|
crossover=DefaultCrossover(),
|
||||||
|
distance=DefaultDistance(),
|
||||||
|
output_transform=None,
|
||||||
|
input_transform=None,
|
||||||
|
init_hidden_layers=(),
|
||||||
activate_time=10,
|
activate_time=10,
|
||||||
output_transform: Callable = None,
|
|
||||||
):
|
):
|
||||||
super().__init__(
|
super().__init__(
|
||||||
num_inputs,
|
num_inputs,
|
||||||
@@ -35,29 +39,25 @@ class RecurrentGenome(BaseGenome):
|
|||||||
conn_gene,
|
conn_gene,
|
||||||
mutation,
|
mutation,
|
||||||
crossover,
|
crossover,
|
||||||
|
distance,
|
||||||
|
output_transform,
|
||||||
|
input_transform,
|
||||||
|
init_hidden_layers,
|
||||||
)
|
)
|
||||||
self.activate_time = activate_time
|
self.activate_time = activate_time
|
||||||
|
|
||||||
if output_transform is not None:
|
|
||||||
try:
|
|
||||||
_ = output_transform(jnp.zeros(num_outputs))
|
|
||||||
except Exception as e:
|
|
||||||
raise ValueError(f"Output transform function failed: {e}")
|
|
||||||
self.output_transform = output_transform
|
|
||||||
|
|
||||||
def transform(self, state, nodes, conns):
|
def transform(self, state, nodes, conns):
|
||||||
u_conns = unflatten_conns(nodes, conns)
|
u_conns = unflatten_conns(nodes, conns)
|
||||||
return nodes, conns, u_conns
|
return nodes, conns, u_conns
|
||||||
|
|
||||||
def restore(self, state, transformed):
|
def forward(self, state, transformed, inputs):
|
||||||
nodes, conns, u_conns = transformed
|
nodes, conns, u_conns = transformed
|
||||||
return nodes, conns
|
|
||||||
|
|
||||||
def forward(self, state, inputs, transformed):
|
|
||||||
nodes, conns = transformed
|
|
||||||
|
|
||||||
vals = jnp.full((self.max_nodes,), jnp.nan)
|
vals = jnp.full((self.max_nodes,), jnp.nan)
|
||||||
nodes_attrs = nodes[:, 1:] # remove index
|
|
||||||
|
nodes_attrs = vmap(extract_node_attrs)(nodes)
|
||||||
|
conns_attrs = vmap(extract_conn_attrs)(conns)
|
||||||
|
expand_conns_attrs = attach_with_inf(conns_attrs, u_conns)
|
||||||
|
|
||||||
def body_func(_, values):
|
def body_func(_, values):
|
||||||
|
|
||||||
@@ -65,14 +65,14 @@ class RecurrentGenome(BaseGenome):
|
|||||||
values = values.at[self.input_idx].set(inputs)
|
values = values.at[self.input_idx].set(inputs)
|
||||||
|
|
||||||
# calculate connections
|
# calculate connections
|
||||||
node_ins = jax.vmap(
|
node_ins = vmap(
|
||||||
jax.vmap(self.conn_gene.forward, in_axes=(None, 1, None)),
|
vmap(self.conn_gene.forward, in_axes=(None, 0, None)),
|
||||||
in_axes=(None, 1, 0),
|
in_axes=(None, 0, 0),
|
||||||
)(state, conns, values)
|
)(state, expand_conns_attrs, values)
|
||||||
|
|
||||||
# calculate nodes
|
# calculate nodes
|
||||||
is_output_nodes = jnp.isin(jnp.arange(self.max_nodes), self.output_idx)
|
is_output_nodes = jnp.isin(nodes[:, 0], self.output_idx)
|
||||||
values = jax.vmap(self.node_gene.forward, in_axes=(None, 0, 0, 0))(
|
values = vmap(self.node_gene.forward, in_axes=(None, 0, 0, 0))(
|
||||||
state, nodes_attrs, node_ins.T, is_output_nodes
|
state, nodes_attrs, node_ins.T, is_output_nodes
|
||||||
)
|
)
|
||||||
|
|
||||||
@@ -87,3 +87,6 @@ class RecurrentGenome(BaseGenome):
|
|||||||
|
|
||||||
def sympy_func(self, state, network, precision=3):
|
def sympy_func(self, state, network, precision=3):
|
||||||
raise ValueError("Sympy function is not supported for Recurrent Network!")
|
raise ValueError("Sympy function is not supported for Recurrent Network!")
|
||||||
|
|
||||||
|
def visualize(self, network):
|
||||||
|
raise ValueError("Visualize function is not supported for Recurrent Network!")
|
||||||
|
|||||||
@@ -6,12 +6,11 @@ from jax import numpy as jnp, Array, jit, vmap
|
|||||||
|
|
||||||
I_INF = np.iinfo(jnp.int32).max # infinite int
|
I_INF = np.iinfo(jnp.int32).max # infinite int
|
||||||
|
|
||||||
# TODO: strange implementation
|
|
||||||
def attach_with_inf(arr, idx):
|
def attach_with_inf(arr, idx):
|
||||||
expand_size = arr.ndim - idx.ndim
|
target_dim = arr.ndim + idx.ndim - 1
|
||||||
expand_idx = jnp.expand_dims(
|
expand_idx = jnp.expand_dims(idx, axis=tuple(range(idx.ndim, target_dim)))
|
||||||
idx, axis=tuple(range(idx.ndim, expand_size + idx.ndim))
|
|
||||||
)
|
|
||||||
return jnp.where(expand_idx == I_INF, jnp.nan, arr[idx])
|
return jnp.where(expand_idx == I_INF, jnp.nan, arr[idx])
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
Reference in New Issue
Block a user