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

bug down！ Here it can solve xor successfully!

Browse Source
This commit is contained in:
wls2002
2023-05-07 16:03:52 +08:00
parent d1f54022bd
commit a3b9bca866
12 changed files with 120 additions and 254 deletions
Download Patch File Download Diff File Expand all files Collapse all files

58
algorithms/neat/pipeline.py
View File

@@ -1,12 +1,12 @@
from typing import List, Union, Tuple, Callable
import time
import numpy as np
import jax
from .species import SpeciesController
from .genome.numpy import create_initialize_function, create_mutate_function, create_forward_function
from .genome.numpy import batch_crossover
from .genome.numpy import expand, expand_single, pop_analysis
from .genome import create_initialize_function, create_mutate_function, create_forward_function
from .genome import batch_crossover
from .genome import expand, expand_single, pop_analysis
from .genome.origin_neat import *
@@ -19,7 +19,8 @@ class Pipeline:
Neat algorithm pipeline.
"""
def __init__(self, config):
def __init__(self, config, seed=42):
self.randkey = jax.random.PRNGKey(seed)
self.config = config
self.N = config.basic.init_maximum_nodes
@@ -69,23 +70,23 @@ class Pipeline:
self.update_next_generation(crossover_pair)
analysis = pop_analysis(self.pop_nodes, self.pop_connections, self.input_idx, self.output_idx)
# analysis = pop_analysis(self.pop_nodes, self.pop_connections, self.input_idx, self.output_idx)
try:
for nodes, connections in zip(self.pop_nodes, self.pop_connections):
g = array2object(self.config, nodes, connections)
print(g)
net = FeedForwardNetwork.create(g)
real_out = [net.activate(x) for x in xor_inputs]
func = create_forward_function(nodes, connections, self.N, self.input_idx, self.output_idx, batch=True)
out = func(xor_inputs)
real_out = np.array(real_out)
out = np.array(out)
print(real_out, out)
assert np.allclose(real_out, out)
except AssertionError:
np.save("err_nodes.npy", self.pop_nodes)
np.save("err_connections.npy", self.pop_connections)
# try:
# for nodes, connections in zip(self.pop_nodes, self.pop_connections):
# g = array2object(self.config, nodes, connections)
# print(g)
# net = FeedForwardNetwork.create(g)
# real_out = [net.activate(x) for x in xor_inputs]
# func = create_forward_function(nodes, connections, self.N, self.input_idx, self.output_idx, batch=True)
# out = func(xor_inputs)
# real_out = np.array(real_out)
# out = np.array(out)
# print(real_out, out)
# assert np.allclose(real_out, out)
# except AssertionError:
# np.save("err_nodes.npy", self.pop_nodes)
# np.save("err_connections.npy", self.pop_connections)
# print(g)
@@ -93,7 +94,6 @@ class Pipeline:
self.expand()
def auto_run(self, fitness_func, analysis: Union[Callable, str] = "default"):
for _ in range(self.config.neat.population.generation_limit):
forward_func = self.ask(batch=True)
@@ -109,7 +109,6 @@ class Pipeline:
self.tell(fitnesses)
print("Generation limit reached!")
def update_next_generation(self, crossover_pair: List[Union[int, Tuple[int, int]]]) -> None:
"""
create the next generation
@@ -117,6 +116,7 @@ class Pipeline:
"""
assert self.pop_nodes.shape[0] == self.pop_size
k1, k2, self.randkey = jax.random.split(self.randkey, 3)
# crossover
# prepare elitism mask and crossover pair
@@ -127,19 +127,20 @@ class Pipeline:
crossover_pair[i] = (pair, pair)
crossover_pair = np.array(crossover_pair)
crossover_rand_keys = jax.random.split(k1, self.pop_size)
# batch crossover
wpn = self.pop_nodes[crossover_pair[:, 0]] # winner pop nodes
wpc = self.pop_connections[crossover_pair[:, 0]] # winner pop connections
lpn = self.pop_nodes[crossover_pair[:, 1]] # loser pop nodes
lpc = self.pop_connections[crossover_pair[:, 1]] # loser pop connections
# npn, npc = batch_crossover(crossover_rand_keys, wpn, wpc, lpn, lpc) # new pop nodes, new pop connections
npn, npc = batch_crossover(wpn, wpc, lpn, lpc)
# print(pop_analysis(npn, npc, self.input_idx, self.output_idx))
npn, npc = batch_crossover(crossover_rand_keys, wpn, wpc, lpn, lpc) # new pop nodes, new pop connections
# mutate
mutate_rand_keys = jax.random.split(k2, self.pop_size)
new_node_keys = np.array(self.fetch_new_node_keys())
m_npn, m_npc = self.mutate_func(npn, npc, new_node_keys) # mutate_new_pop_nodes
m_npn, m_npc = self.mutate_func(mutate_rand_keys, npn, npc, new_node_keys) # mutate_new_pop_nodes
# elitism don't mutate
# (pop_size, ) to (pop_size, 1, 1)
@@ -156,7 +157,6 @@ class Pipeline:
unused.append(key)
self.new_node_keys_pool = unused + self.new_node_keys_pool
def expand(self):
"""
Expand the population if needed.
@@ -176,7 +176,6 @@ class Pipeline:
for s in self.species_controller.species.values():
s.representative = expand_single(*s.representative, self.N)
def fetch_new_node_keys(self):
# if remain unused keys are not enough, create new keys
if len(self.new_node_keys_pool) < self.pop_size:
@@ -189,7 +188,6 @@ class Pipeline:
self.new_node_keys_pool = self.new_node_keys_pool[self.pop_size:]
return res
def default_analysis(self, fitnesses):
max_f, min_f, mean_f, std_f = max(fitnesses), min(fitnesses), np.mean(fitnesses), np.std(fitnesses)
species_sizes = [len(s.members) for s in self.species_controller.species.values()]