Neuro-evolution and model optimization toolkit
Project description
NeuroEvolution
NeuroEvolution: Evolutionary Neural Architecture Search & Weight Optimization NeuroEvolution is a high-level Python library designed to explore the intersection of Deep Learning and Metaheuristic Optimization. It allows users to optimize neural network weights using Swarm Intelligence (SI) and Evolutionary Algorithms (EA) instead of traditional Gradient Descent, and performs Neural Architecture Search (NAS) to discover efficient model structures dynamically.
Built on top of PyTorch and Mealpy.
Key Features
Gradient-Free Optimization: Train neural networks without backpropagation using algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and more.
Dynamic Architecture Search (NAS): Automatically evolve network architectures (add/remove layers, adjust neurons/kernels) to find the best trade-off between performance and complexity.
Hybrid Training Strategy Combine the global search capabilities of Metaheuristics with the local precision of Gradient Descent (Adam) to overcome the "curse of dimensionality" in deep networks.
Hardware-Aware Evaluation: Optimize models not just for accuracy/loss, but also for Inference Latency using multi-objective fitness functions.
Plug-and-Play: Seamless integration with Scikit-Learn style API (fit/predict paradigm).
Wide Algorithm Support: Access to 10+ state-of-the-art metaheuristics via Mealpy.
Skip connection
NeuroEvolution supports hardware-aware and multi-criteria optimization, including:
Accuracy / Loss
Inference latency
Parameter count
FLOPs
Training time
Using Pareto dominance, the framework can identify optimal trade-offs instead of a single solution.
Partial Optimization Experiment: In an attempt to mitigate the curse of dimensionality, we tested applying metaheuristic algorithms solely to the last layer and randomly selected intermediate layers. This strategy proved suboptimal, producing results inferior to full-model training.
Installation
pip install NeuroEvolution
Use
** Matérial used for benchmark :NVIDIA GeForce RTX 3060 Laptop GPU, 5 years old **
Layers
Conv2dCfgDropoutCfgFlattenCfgLinearCfgMaxPool2dCfgGlobalAvgPoolCfg
Methods
search_weights: only weights of a modelsearch_linear_model: only for MLPhybrid_search: use various algorithms for weights searchsearch_model: search model + possibility of hybrid search if listhybridnot emptysearch_model_evolutionary: evolutionnary search model + possibility of hybrid search if listhybridnot empty
Supported Algorithms
NeuroOptimizer wraps powerful metaheuristics provided by Mealpy.
| Code | Algorithm Name | Best Use Case |
|---|---|---|
| Adam | Adaptive Moment Estimation | Baseline. Large networks, images, high-dim data. |
| GWO | Grey Wolf Optimizer | Balanced exploration/exploitation. General purpose. |
| PSO | Particle Swarm Optimization | Fast convergence on simple landscapes. |
| DE | Differential Evolution | Robust for noisy functions and regression. |
| GA | Genetic Algorithm | Classic evolutionary approach. Very robust. |
| WOA | Whale Optimization Algorithm | Escaping local minima via spiral search. |
| SMA | Slime Mould Algorithm | High precision, adaptive weights. |
| ABC | Artificial Bee Colony | Strong local search (fine-tuning). |
Example
import torch.nn as nn
from sklearn.datasets import make_moons
from NeuroEvolution import NeuroOptimizer
from NeuroEvolution.layer_classes import LinearCfg
# 1. Prepare Data
X, y = make_moons(n_samples=1000, noise=0.1, random_state=42)
# 2. Define Architecture (Manually)
# A simple MLP: Input(2) -> Linear(16) -> ReLU -> Linear(2)
my_layers = [
LinearCfg(in_features=2, out_features=16, activation=nn.ReLU),
LinearCfg(in_features=16, out_features=2, activation=None) # Output dim = classes
]
# 3. Initialize Optimizer
optimizer = NeuroOptimizer(
X, y,
Layers=my_layers,
task="classification"
)
# 4. Search for Weights using GWO (Grey Wolf Optimizer)
print("Optimizing weights with GWO...")
model = optimizer.search_weights(
optimizer_name='GWO',
epochs=10,
population=20,
verbose=True
)
# 5. Evaluate
score = optimizer.evaluate(model, verbose=True)
print(f"Final Accuracy: {-score:.2%}") # Note: Evaluate returns negative acc for minimization
import numpy as np
from sklearn.datasets import make_moons
from NeuroEvolution import NeuroOptimizer
# 1. Define the Custom "Pareto" Function
# The optimizer minimizes the returned value.
# We want to MAXIMIZE accuracy and MINIMIZE latency.
def pareto_time_importance(accuracy, inference_time):
"""
Combines accuracy and inference time into a single score.
Penalizes models that are slower than a specific threshold.
"""
# Threshold: We aim for inference under 2ms (0.002s)
target_latency = 0.002
# Penalty Weight: How strictly do we punish slowness?
# Higher alpha = Speed is more important than small accuracy gains.
alpha = 10.0
# Calculate Penalty
# If time > 2ms, penalty increases. If time < 2ms, penalty is 0.
latency_penalty = max(0, (inference_time - target_latency) * alpha)
# Final Score Calculation:
# We minimize (-Accuracy + Penalty).
# Examples:
# Model A: Acc=0.99, Time=0.010s -> Score = -0.99 + (0.008 * 10) = -0.91 (Worse)
# Model B: Acc=0.98, Time=0.001s -> Score = -0.98 + 0 = -0.98 (Better)
return -accuracy + latency_penalty
# 2. Load Data
X, y = make_moons(n_samples=1000, noise=0.2, random_state=42)
# 3. Initialize Optimizer
optimizer = NeuroOptimizer(X, y, task="classification")
# 4. Run NAS with the Time Constraint
print("Starting NAS with Latency-Aware Optimization...")
best_model = optimizer.search_model(
epochs=15,
train_time=600,
optimizer_name_weights='GWO',
epochs_weights=5,
verbose=True,
# Pass your custom function here
time_importance=pareto_time_importance
)
print(f"\nBest 'Efficient' Model Architecture:\n{best_model}")
from sklearn.datasets import make_circles
from NeuroEvolution import NeuroOptimizer, LinearCfg
import torch.nn as nn
X, y = make_circles(n_samples=500, noise=0.05, factor=0.5)
layers = [LinearCfg(2, 32, nn.ReLU), LinearCfg(32, 2, None)]
optimizer = NeuroOptimizer(X, y, Layers=layers)
# Hybrid Strategy:
# 1. GWO finds a good starting region (Exploration)
# 2. Adam fine-tunes the weights (Exploitation)
model = optimizer.hybrid_search(
optimizers=['GWO', 'Adam'],
epochs=[10, 20], # 10 epochs GWO, then 20 epochs Adam
populations=25, # Only applies to GWO
learning_rate=0.01, # Only applies to Adam
verbose=True
)
# Skip Connection
layers = []
layers.append(Conv2dCfg(in_channels=0, out_channels=16, kernel_size=3, padding=1, activation=nn.ReLU))
layers.append(BatchNorm2dCfg(num_features=16))
for _ in range(depth):
sub_block = [
Conv2dCfg(in_channels=0, out_channels=16, kernel_size=3, padding=1, activation=nn.ReLU),
BatchNorm2dCfg(num_features=16),
Conv2dCfg(in_channels=0, out_channels=16, kernel_size=3, padding=1, activation=None)
]
layers.append(ResBlockCfg(sub_layers=sub_block))
layers.append(BatchNorm2dCfg(num_features=16))
layers.append(GlobalAvgPoolCfg())
layers.append(LinearCfg(in_features=0, out_features=2, activation=None))
# Evolutionnary search model
best_model = opt.search_model_evolutionary(
epochs=epochs,
population_size=10,
epochs_weights=40,
optimizer_name_weights='Adam',
learning_rate_weights=0.01,
accuracy_target=target_acc,
verbose=True
)
Benchmark : Classification
Iris, Wine & Breast Cancer
Layers = [LinearCfg(self.n_features, 16, nn.ReLU), LinearCfg(16, self.output_dim, None) ]
model = neuro_opt.search_weights(optimizer_name=algo_choice, epochs=30, population=20)
====================================================================================================
Benchmarks
====================================================================================================
Dataset Algorithm Accuracy (%) Train Time (s) Latency (ms) Params (k) FLOPs (M)
Iris Adam 93.33 0.04 0.0652 0.13 0.0001
Wine Adam 100.00 0.05 0.0701 0.28 0.0003
Breast Cancer Adam 98.25 0.04 0.0752 0.53 0.0005
Iris GWO 100.00 0.74 0.0552 0.13 0.0001
Wine GWO 94.44 0.71 0.0500 0.28 0.0003
Breast Cancer GWO 97.37 0.80 0.0501 0.53 0.0005
Iris PSO 90.00 0.79 0.0500 0.13 0.0001
Wine PSO 83.33 0.56 0.0451 0.28 0.0003
Breast Cancer PSO 92.11 0.69 0.0551 0.53 0.0005
Iris DE 96.67 0.82 0.0652 0.13 0.0001
Wine DE 91.67 0.79 0.0712 0.28 0.0003
Breast Cancer DE 92.98 1.41 0.0703 0.53 0.0005
Iris WOA 93.33 0.59 0.0752 0.13 0.0001
Wine WOA 94.44 0.59 0.0804 0.28 0.0003
Breast Cancer WOA 96.49 0.64 0.0451 0.53 0.0005
Iris GA 100.00 0.66 0.0516 0.13 0.0001
Wine GA 100.00 0.84 0.0700 0.28 0.0003
Breast Cancer GA 96.49 0.83 0.0651 0.53 0.0005
Iris ABC 86.67 1.29 0.0451 0.13 0.0001
Wine ABC 91.67 1.36 0.0900 0.28 0.0003
Breast Cancer ABC 94.74 1.88 0.0853 0.53 0.0005
Iris SMO 96.67 1.52 0.0600 0.13 0.0001
Wine SMO 91.67 1.24 0.0500 0.28 0.0003
Breast Cancer SMO 96.49 1.41 0.0400 0.53 0.0005
Iris SMA 93.33 2.56 0.0653 0.13 0.0001
Wine SMA 91.67 9.16 0.0951 0.28 0.0003
Breast Cancer SMA 83.33 7.81 0.0361 0.53 0.0005
Iris HHO 90.00 0.95 0.0551 0.13 0.0001
Wine HHO 80.56 0.96 0.0801 0.28 0.0003
Breast Cancer HHO 94.74 1.33 0.0801 0.53 0.0005
====================================================================================================
model = neuro_opt.search_linear_model(optimizer_name_weights=algo_choice, epochs=30)
====================================================================================================
Benchmarks
====================================================================================================
Dataset Algorithm Accuracy (%) Train Time (s) Latency (ms) Params (k) FLOPs (M)
Iris Adam 100.00 0.60 0.1403 5.81 0.0057
Wine Adam 100.00 0.38 0.0601 1.75 0.0017
Breast Cancer Adam 98.25 0.31 0.0500 1.52 0.0015
Iris GWO 100.00 9.33 0.0752 1.32 0.0012
Wine GWO 100.00 8.76 0.0702 0.79 0.0007
Breast Cancer GWO 97.37 3.64 0.0350 0.53 0.0005
Iris PSO 96.67 10.75 0.0652 0.51 0.0005
Wine PSO 94.44 9.51 0.0811 1.23 0.0012
Breast Cancer PSO 97.37 15.65 0.0651 5.17 0.0051
Iris DE 96.67 5.61 0.0451 0.19 0.0002
Wine DE 94.44 3.70 0.0351 0.09 0.0001
Breast Cancer DE 97.37 6.90 0.0752 2.12 0.0021
Iris WOA 96.67 3.37 0.0500 0.16 0.0001
Wine WOA 88.89 3.08 0.0400 0.17 0.0002
Breast Cancer WOA 92.98 10.04 0.1051 2.72 0.0026
Iris GA 96.67 3.97 0.0551 0.15 0.0001
Wine GA 91.67 7.45 0.0801 0.67 0.0006
Breast Cancer GA 95.61 7.85 0.0351 0.53 0.0005
Iris ABC 96.67 8.71 0.0551 0.13 0.0001
Wine ABC 97.22 9.77 0.0501 0.70 0.0007
Breast Cancer ABC 96.49 10.79 0.0502 1.42 0.0014
Iris SMO 96.67 17.23 0.0601 1.30 0.0012
Wine SMO 94.44 11.62 0.0502 0.19 0.0002
Breast Cancer SMO 96.49 12.82 0.0400 0.96 0.0009
Iris SMA 100.00 169.17 0.0601 0.71 0.0007
Wine SMA 97.22 190.23 0.0500 0.26 0.0002
Breast Cancer SMA 96.49 165.34 0.0451 0.30 0.0003
Iris HHO 93.33 7.78 0.0451 0.13 0.0001
Wine HHO 88.89 8.97 0.0451 0.28 0.0003
Breast Cancer HHO 96.49 7.70 0.0700 0.53 0.0005
====================================================================================================
model = neuro_opt.search_model(
hybrid=['GWO','Adam'], hybrid_epochs=[10,10],
epochs=10,
train_time=60,
epochs_weights=10,
population_weights=20,
)
====================================================================================================
Benchmarks
====================================================================================================
Dataset Algorithm Accuracy (%) Train Time (s) Latency (ms) Params (k) FLOPs (M)
Iris GWO+Adam 100.00 0.16 0.0400 0.13 0.0001
Wine GWO+Adam 100.00 0.36 0.0453 0.43 0.0004
Breast Cancer GWO+Adam 97.37 1.81 0.0400 1.06 0.0010
====================================================================================================
make_moons (n=2000, noise=0.3) | 20 Runs Average
X, y = make_moons(n_samples=2000, noise=0.3)
neuro_opt = NeuroOptimizer(X, y, task="classification")
for opt in NeuroOptimizer.get_available_optimizers():
model = neuro_opt.search_linear_model(
optimizer_name_weights=opt,
epochs=5,
train_time=60,
epochs_weights=10,
population_weights=20,
)
====================================================================================================
ALGORITHM | AVG ACCURACY | STD DEV | AVG INF TIME (ms) | BEST ACC
----------------------------------------------------------------------------------------------------
GWO | 87.28% | ±1.49% | 0.2502 ms | 89.95%
WOA | 86.48% | ±1.35% | 0.5262 ms | 90.15%
PSO | 86.29% | ±1.41% | 0.2500 ms | 88.75%
DE | 86.06% | ±1.49% | 0.3985 ms | 89.95%
SMO | 85.78% | ±1.37% | 0.3003 ms | 89.80%
ABC | 85.78% | ±1.03% | 0.2485 ms | 87.55%
HHO | 85.04% | ±1.57% | 0.4988 ms | 88.80%
GA | 85.01% | ±1.97% | 0.3985 ms | 89.40%
SMA | 84.79% | ±2.12% | 0.6004 ms | 89.25%
Adam | 83.88% | ±1.87% | 0.3001 ms | 88.05%
====================================================================================================
model = neuro_opt.search_linear_model(
optimizer_name_weights=opt,
epochs=10,
train_time=60,
epochs_weights=10,
population_weights=20,
)
====================================================================================================
ALGORITHM | AVG ACCURACY | STD DEV | AVG INF TIME (ms) | BEST ACC
----------------------------------------------------------------------------------------------------
GWO | 88.32% | ±1.28% | 0.8899 ms | 91.05%
PSO | 87.51% | ±1.19% | 0.7262 ms | 89.80%
SMO | 87.00% | ±1.45% | 0.6778 ms | 90.30%
ABC | 86.96% | ±0.90% | 0.4767 ms | 88.85%
GA | 86.84% | ±1.21% | 0.4511 ms | 89.35%
DE | 86.78% | ±1.42% | 0.6773 ms | 89.45%
WOA | 86.27% | ±1.64% | 1.7371 ms | 89.70%
HHO | 85.86% | ±1.31% | 0.6460 ms | 88.90%
SMA | 85.37% | ±1.85% | 0.6266 ms | 88.65%
Adam | 84.99% | ±1.00% | 0.6017 ms | 87.05%
====================================================================================================
model = neuro_opt.search_model(
hybrid=['GWO','Adam'], hybrid_epochs=[10,10],
epochs=10,
train_time=60,
epochs_weights=10,
population_weights=20,
)
====================================================================================================
ALGORITHM | AVG ACCURACY | STD DEV | AVG INF TIME (ms) | BEST ACC
----------------------------------------------------------------------------------------------------
GWO + Adam | 90.40% | ±0.85% | 0.4511 ms | 91.45%
====================================================================================================
neuro_opt = NeuroOptimizer(X, y, task="classification")
for opt in ['Adam','GWD']:
model=neuro_opt.search_linear_model(optimizer_name_weights=opt, epochs=20, train_time=60,
epochs_weights=20, population_weights=20,
time_importance=time_importance)
Benchmark : Regression
cos(x)+gauss(0,0.1)
x = torch.linspace(-1, 1, 100).unsqueeze(1)
y = torch.cos( x) + torch.randn(100,1)*0.1
xtest= torch.linspace(0, 1, 100).unsqueeze(1)
Layers = [
LinearCfg(1, 32, nn.Tanh),
LinearCfg(32, 32, nn.Tanh),
LinearCfg(32, 1, None)
]
neuro_opt = NeuroOptimizer(x, y, task="regression", Layers=Layers, activation=nn.Tanh)
model = neuro_opt.search_linear_model(optimizer_name_weights='Adam', epochs=50, train_time=10*60,
epochs_weights=200, population_weights=20,
verbose=True)
with torch.no_grad():
pred = model(xtest)
fetch_california_housing, load_diabetes, make_friedman1
=========================================================================================================
DATASET | ALGO | R² SCORE (Max 1.0) | MSE | INF TIME (ms)
---------------------------------------------------------------------------------------------------------
California Housing (2k) | Adam | 0.7754 | 0.2052 | 1.0002
California Housing (2k) | GWO | 0.6756 | 0.2963 | 0.0000
California Housing (2k) | PSO | -0.0324 | 0.9431 | 0.0000
California Housing (2k) | DE | 0.5069 | 0.4504 | 0.0000
California Housing (2k) | WOA | 0.3165 | 0.6244 | 0.0000
California Housing (2k) | GA | 0.5918 | 0.3729 | 0.0000
California Housing (2k) | ABC | 0.3652 | 0.5799 | 0.0000
California Housing (2k) | SMO | 0.6208 | 0.3464 | 0.0000
California Housing (2k) | SMA | 0.5682 | 0.3944 | 0.0000
California Housing (2k) | HHO | 0.3607 | 0.5840 | 0.0000
Diabetes | Adam | 0.1371 | 4571.8247 | 0.0000
Diabetes | GWO | 0.3713 | 3331.1218 | 0.0000
Diabetes | PSO | 0.0974 | 4782.2788 | 1.0004
Diabetes | DE | 0.2826 | 3800.8921 | 0.0000
Diabetes | WOA | -0.1124 | 5893.6045 | 0.0000
Diabetes | GA | 0.3249 | 3576.7546 | 0.0000
Diabetes | ABC | 0.2084 | 4193.9717 | 1.0002
Diabetes | SMO | 0.2716 | 3859.1201 | 0.0000
Diabetes | SMA | 0.3340 | 3528.4121 | 0.0000
Diabetes | HHO | 0.1961 | 4259.2891 | 1.0011
Friedman Non-Linear | Adam | 0.6277 | 8.6988 | 0.0000
Friedman Non-Linear | GWO | 0.6134 | 9.0339 | 0.0000
Friedman Non-Linear | PSO | 0.0281 | 22.7096 | 0.0000
Friedman Non-Linear | DE | 0.4986 | 11.7168 | 0.0000
Friedman Non-Linear | WOA | 0.1627 | 19.5654 | 0.0000
Friedman Non-Linear | GA | 0.3862 | 14.3419 | 0.0000
Friedman Non-Linear | ABC | 0.1274 | 20.3887 | 0.0000
Friedman Non-Linear | SMO | 0.3631 | 14.8830 | 0.0000
Friedman Non-Linear | SMA | 0.5366 | 10.8274 | 0.0000
Friedman Non-Linear | HHO | -0.1552 | 26.9935 | 0.0000
=========================================================================================================
Classification Image
MNIST
Layers = [
Conv2dCfg(1, 1, 3),
FlattenCfg(),
LinearCfg(36, 10, None)
]
for opt in NeuroOptimizer.get_available_optimizers():
neuro_opt = NeuroOptimizer(X, y, task="classification",Layers=Layers)
model=neuro_opt.search_weights(optimizer_name=opt, epochs=50, population=40)
model=neuro_opt.search_model(epochs=20,optimizer_name_weights=opt, epochs_weights=50, population_weights=40)
Model search_weights search_model
Adam 0.9443 0.9649
GWO 0.3077 0.4140
PSO 0.1163 0.1268
DE 0.3127 0.4023
WOA 0.1013 0.1903
GA 0.3823 0.4452
ABC 0.1747 0.2460
SMO 0.2332 0.2454
SMA 0.2532 0.2627
HHO 0.2721 0.1358
====================================================================================================
BENCHMARK
====================================================================================================
Dataset Algo Mode Accuracy Train Time (s) Inf Time (ms)
Digits (8x8) ABC NAS (Evolved) 0.1111 7.1849 1.0071
Digits (8x8) ABC Weights (Fixed) 0.1056 6.4637 0.9966
Digits (8x8) Adam NAS (Evolved) 0.9583 0.1828 0.0000
Digits (8x8) Adam Weights (Fixed) 0.9583 0.1626 1.0056
Digits (8x8) DE NAS (Evolved) 0.1028 3.4276 1.0009
Digits (8x8) DE Weights (Fixed) 0.1278 3.3676 1.0014
Digits (8x8) GA NAS (Evolved) 0.1361 3.8615 1.5094
Digits (8x8) GA Weights (Fixed) 0.0806 3.6658 0.0000
Digits (8x8) GWO NAS (Evolved) 0.1667 4.2136 1.0002
Digits (8x8) GWO Weights (Fixed) 0.3500 4.6753 2.0032
Digits (8x8) HHO NAS (Evolved) 0.1222 6.7114 1.0071
Digits (8x8) HHO Weights (Fixed) 0.1250 6.2650 0.9999
Digits (8x8) PSO NAS (Evolved) 0.1333 6.6804 0.0000
Digits (8x8) PSO Weights (Fixed) 0.0972 5.9419 0.0000
Digits (8x8) SMA NAS (Evolved) 0.1861 188.2196 1.0016
Digits (8x8) SMA Weights (Fixed) 0.2056 179.5234 0.0000
Digits (8x8) SMO NAS (Evolved) 0.0806 8.1273 2.0084
Digits (8x8) SMO Weights (Fixed) 0.1389 7.4610 1.0035
Digits (8x8) WOA NAS (Evolved) 0.0944 3.6672 0.0000
Digits (8x8) WOA Weights (Fixed) 0.2000 4.3089 2.9955
FashionMNIST (28x28) ABC NAS (Evolved) 0.0725 58.2891 8.0380
FashionMNIST (28x28) ABC Weights (Fixed) 0.1150 57.6505 6.0019
FashionMNIST (28x28) Adam NAS (Evolved) 0.7875 1.8577 7.5350
FashionMNIST (28x28) Adam Weights (Fixed) 0.7675 1.9463 5.0027
FashionMNIST (28x28) DE NAS (Evolved) 0.1375 28.8300 3.5248
FashionMNIST (28x28) DE Weights (Fixed) 0.0925 29.7496 5.5254
FashionMNIST (28x28) GA NAS (Evolved) 0.1975 37.2505 6.5241
FashionMNIST (28x28) GA Weights (Fixed) 0.0950 35.5957 5.0051
FashionMNIST (28x28) GWO NAS (Evolved) 0.1225 34.8034 5.5251
FashionMNIST (28x28) GWO Weights (Fixed) 0.3350 34.4230 4.5180
FashionMNIST (28x28) PSO NAS (Evolved) 0.0825 47.7354 4.0159
FashionMNIST (28x28) PSO Weights (Fixed) 0.0475 54.5541 4.9982
FashionMNIST (28x28) SMO NAS (Evolved) 0.1025 83.0668 5.0015
FashionMNIST (28x28) SMO Weights (Fixed) 0.1450 81.2067 6.0298
FashionMNIST (28x28) WOA NAS (Evolved) 0.1850 28.9955 5.0030
FashionMNIST (28x28) WOA Weights (Fixed) 0.0775 26.7720 6.5250
====================================================================================================
LAYERS = [
Conv2dCfg(1, 8, 3, padding=1),
Conv2dCfg(8, 8, 3, padding=1),
Conv2dCfg(8, 8, 3, padding=1),
FlattenCfg(),
LinearCfg(X.shape[2]*X.shape[3]*X.shape[1]*8, 10, None)
]
====================================================================================================
BENCHMARK
====================================================================================================
Dataset Algo Mode Accuracy Train Time (s) Inf Time (ms)
Digits (8x8) ABC NAS (Evolved) 0.1139 1.8717 3.9964
Digits (8x8) ABC Weights (Fixed) 0.1250 1.6242 2.0325
Digits (8x8) Adam NAS (Evolved) 0.9833 0.9938 23.0863
Digits (8x8) Adam Weights (Fixed) 0.9722 0.9804 4.0231
Digits (8x8) DE NAS (Evolved) 0.1556 2.0961 2.0087
Digits (8x8) DE Weights (Fixed) 0.1111 0.9968 1.0011
Digits (8x8) GA NAS (Evolved) 0.1111 1.3131 1.0011
Digits (8x8) GA Weights (Fixed) 0.0250 2.4344 4.0030
Digits (8x8) GWO NAS (Evolved) 0.0639 0.8758 1.9996
Digits (8x8) GWO Weights (Fixed) 0.1806 1.2347 1.5016
Digits (8x8) HHO NAS (Evolved) 0.1333 1.5475 3.0150
Digits (8x8) HHO Weights (Fixed) 0.0917 2.4058 2.1415
Digits (8x8) PSO NAS (Evolved) 0.1417 2.6542 2.0010
Digits (8x8) PSO Weights (Fixed) 0.1333 1.7392 2.0003
Digits (8x8) SMA NAS (Evolved) 0.0778 154.5960 1.5140
Digits (8x8) SMA Weights (Fixed) 0.0917 18.4968 0.9997
Digits (8x8) SMO NAS (Evolved) 0.0861 1.9286 1.5068
Digits (8x8) SMO Weights (Fixed) 0.1278 2.8755 0.9689
Digits (8x8) WOA NAS (Evolved) 0.0556 1.0074 3.0093
Digits (8x8) WOA Weights (Fixed) 0.0306 1.1385 2.5125
FashionMNIST (28x28) ABC NAS (Evolved) 0.1200 2.0240 16.5219
FashionMNIST (28x28) ABC Weights (Fixed) 0.0925 2.5769 22.5620
FashionMNIST (28x28) Adam NAS (Evolved) 0.8400 7.9745 23.0608
FashionMNIST (28x28) Adam Weights (Fixed) 0.8450 8.7450 34.7574
FashionMNIST (28x28) DE NAS (Evolved) 0.0750 1.3014 27.2593
FashionMNIST (28x28) DE Weights (Fixed) 0.0950 10.5563 24.0796
FashionMNIST (28x28) GA NAS (Evolved) 0.1000 1.2839 21.5788
FashionMNIST (28x28) GA Weights (Fixed) 0.1350 1.9806 21.0924
FashionMNIST (28x28) GWO NAS (Evolved) 0.1175 1.1394 17.0500
FashionMNIST (28x28) GWO Weights (Fixed) 0.1350 1.4528 41.0292
FashionMNIST (28x28) HHO NAS (Evolved) 0.0825 1.6817 17.5858
FashionMNIST (28x28) HHO Weights (Fixed) 0.0975 1.7816 21.0438
FashionMNIST (28x28) PSO NAS (Evolved) 0.1200 1.8998 16.5281
FashionMNIST (28x28) PSO Weights (Fixed) 0.0700 1.7432 20.5402
FashionMNIST (28x28) SMA NAS (Evolved) 0.1150 22.3150 28.6043
FashionMNIST (28x28) SMA Weights (Fixed) 0.0700 3.2801 21.5473
FashionMNIST (28x28) SMO NAS (Evolved) 0.1575 39.5106 18.0459
FashionMNIST (28x28) SMO Weights (Fixed) 0.1025 2.7280 20.4964
FashionMNIST (28x28) WOA NAS (Evolved) 0.0700 1.8454 30.6456
FashionMNIST (28x28) WOA Weights (Fixed) 0.0925 1.5019 36.1836
====================================================================================================
LAYERS = [
Conv2dCfg(1, 32, 3, padding=1),
Conv2dCfg(32, 32, 3, padding=1),
Conv2dCfg(32, 32, 3, padding=1),
FlattenCfg(),
LinearCfg(X.shape[2]*X.shape[3]*X.shape[1]*32, 10, None)
]
=====================
RÉSULTATS STATISTIQUES FINAUX
========================================================================================================================
Dataset Algo Runs Accuracy (Mean ± Std) Acc Max Train Time Mean (s) Inf Time Mean (ms)
Digits (8x8) Adam 20 98.64% ± 0.53% 0.994444 15.851734 22.440183
FashionMNIST (28x28) Adam 20 83.00% ± 0.81% 0.847500 189.154225 213.330531
========================================================================================================================
LAYERS = [
Conv2dCfg(in_channels=1, out_channels=32, kernel_size=3, padding=1, activation=nn.ReLU),
MaxPool2dCfg(kernel_size=2, stride=2),
Conv2dCfg(in_channels=32, out_channels=64, kernel_size=3, padding=1, activation=nn.ReLU),
MaxPool2dCfg(kernel_size=2, stride=2),
FlattenCfg(),
LinearCfg(in_features=(X.shape[2] // 4) * (X.shape[3] // 4) * 64, out_features=128, activation=nn.ReLU),
DropoutCfg(p=0.4),
LinearCfg(in_features=128, out_features=10, activation=None)
]
========================================================================================================================
RÉSULTATS STATISTIQUES FINAUX
========================================================================================================================
Dataset Algo Runs Accuracy (Mean ± Std) Acc Max Train Time Mean (s) Inf Time Mean (ms)
Digits (8x8) Adam 50 98.62% ± 0.63% 1.0000 3.693523 5.815082
FashionMNIST (28x28) Adam 50 84.46% ± 1.56% 0.8775 47.100273 74.207230
========================================================================================================================
best_model = opt.search_model_evolutionary(
epochs=10,
population_size=10,
epochs_weights=40,
optimizer_name_weights='Adam',
learning_rate_weights=0.01,
verbose=True
)
--- RESULTATS Digits (Image Classification) ---
train time NAS : 10.14s
Accuracy : 91.39%
Architecture :
DynamicNet(
(net): Sequential(
(0): Flatten(start_dim=1, end_dim=-1)
(1): Linear(in_features=64, out_features=10, bias=True)
)
)
--- RESULTATS California Housing (Regression) ---
train time NAS : 3.07s
MSE Final : 1.6875
Architecture :
DynamicNet(
(net): Sequential(
(0): Linear(in_features=8, out_features=5, bias=True)
(1): ReLU()
(2): Linear(in_features=5, out_features=1, bias=True)
)
)
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