Gradient-Free Training: The Silent Revolution Changing AI Training 🇺🇸🇧🇷
🇺🇸 ARTICLE IN ENGLISH 🇺🇸
📢 Training AI without Backpropagation: The future is here and costs 50% less! 🚀
📌 The Context Few Know About: While everyone talks about gradients, a silent revolution is happening. In 2024, training an LLM with backpropagation can cost over $10 million - a value that varies drastically depending on the model (GPT-4 vs LLaMA), dataset (Common Crawl vs proprietary data), and infrastructure (cloud vs owned). According to MLCommons reports, gradient-free methods are reducing costs by 30-50% for specific tasks like hyperparameter optimization and neural architectures. Boston Dynamics, DeepMind, and IBM have already adopted these techniques in production!
💡 1. Evolutionary Algorithms: Darwin Meets AI
Check this real example with CMA-ES optimizing a complete neural network:
import torch
import torch.nn as nn
from deap import algorithms, base, creator, tools
import numpy as np
# Simple neural network for classification
class SimpleNet(nn.Module):
def __init__(self, input_size=784, hidden_sizes=[128, 64], num_classes=10):
super(SimpleNet, self).__init__()
self.layers = nn.ModuleList()
# Dense layers
prev_size = input_size
for hidden_size in hidden_sizes:
self.layers.append(nn.Linear(prev_size, hidden_size))
prev_size = hidden_size
self.output = nn.Linear(prev_size, num_classes)
self.relu = nn.ReLU()
def forward(self, x):
x = x.view(x.size(0), -1) # Flatten
for layer in self.layers:
x = self.relu(layer(x))
return self.output(x)
# Evolutionary evaluation function
def eval_network(weights_vector, model, data_loader, device):
"""Evaluates fitness of an individual (weight set)"""
vector_to_weights(weights_vector, model)
model.eval()
correct = 0
total = 0
with torch.no_grad():
for data, target in data_loader:
data, target = data.to(device), target.to(device)
outputs = model(data)
_, predicted = torch.max(outputs.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
accuracy = correct / total
return (accuracy,) # Returns tuple (fitness,)
def vector_to_weights(vector, model):
"""Converts 1D vector to model weights - Optimized Version"""
idx = 0
with torch.no_grad():
for param in model.parameters():
param_length = param.numel()
# Optimized approach: avoids intermediate tensor and uses copy_ for efficiency
param.data.copy_(
torch.from_numpy(vector[idx:idx + param_length]).reshape(param.shape)
)
idx += param_length
# Evolutionary algorithm setup
def setup_evolution(model, num_weights):
creator.create("FitnessMax", base.Fitness, weights=(1.0,)) # Maximize accuracy
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
# Individual = random weight vector
toolbox.register("attr_float", np.random.randn)
toolbox.register("individual", tools.initRepeat,
creator.Individual, toolbox.attr_float, n=num_weights)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# Evolutionary operators
toolbox.register("mate", tools.cxBlend, alpha=0.3)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
return toolbox
# Usage example
def train_with_evolution(model, train_loader, generations=50, population_size=100):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Count total parameters
num_weights = sum(p.numel() for p in model.parameters())
print(f"Total parameters: {num_weights:,}")
toolbox = setup_evolution(model, num_weights)
toolbox.register("evaluate", eval_network,
model=model, data_loader=train_loader, device=device)
# Initial population
population = toolbox.population(n=population_size)
# Statistics
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("max", np.max)
# Evolution!
print("Starting evolution...")
population, logbook = algorithms.eaSimple(
population, toolbox,
cxpb=0.7, # 70% crossover probability
mutpb=0.2, # 20% mutation probability
ngen=generations,
stats=stats,
verbose=True
)
# Best individual
best_individual = tools.selBest(population, k=1)[0]
vector_to_weights(best_individual, model)
return model, logbook
# Practical usage
model = SimpleNet()
# train_loader would be your PyTorch DataLoader
# trained_model, evolution_log = train_with_evolution(model, train_loader)
🌍 Visual Analogy: The Neural Network Farm
Imagine a farm where each neural network is an animal:
Traditional Method (Backprop): Veterinarian examines each animal individually, prescribes specific treatment for each symptom 🩺
Evolutionary Method: The healthiest animals naturally reproduce, passing superior genes. The weak are eliminated by natural selection 🧬
Mental image: A green field where herds of neural networks "graze" on data, and only the most efficient survive and multiply
💡 2. Bayesian Optimization: The Statistical Oracle
from skopt import gp_minimize
from skopt.space import Real, Integer
from skopt.utils import use_named_args
import matplotlib.pyplot as plt
# Hyperparameter space to optimize
dimensions = [
Real(low=1e-6, high=1e-1, name='learning_rate', prior='log-uniform'),
Integer(low=16, high=512, name='batch_size'),
Integer(low=1, high=5, name='num_layers'),
Integer(low=32, high=256, name='hidden_size'),
Real(low=0.0, high=0.5, name='dropout_rate'),
]
# Objective function (simulates training)
@use_named_args(dimensions)
def objective(**params):
"""
Simulates model training with given hyperparameters
Returns: validation loss (lower is better)
"""
# In a real case, you would train the model here
lr = params['learning_rate']
batch_size = params['batch_size']
num_layers = params['num_layers']
hidden_size = params['hidden_size']
dropout = params['dropout_rate']
# Performance simulation based on hyperparameters
# (in real case, replace with actual training)
performance = (
0.1 * (lr - 0.001)**2 + # Optimal LR near 0.001
0.01 * (batch_size - 64)**2 / 1000 + # Optimal batch size near 64
0.02 * num_layers + # Penalty for many layers
0.001 * (hidden_size - 128)**2 / 100 + # Optimal hidden size near 128
0.1 * dropout # Penalty for high dropout
)
return performance
# Bayesian Optimization
print("Starting Bayesian Optimization...")
result = gp_minimize(
func=objective,
dimensions=dimensions,
n_calls=100, # 100 evaluations
n_initial_points=10, # 10 random initial points
acq_func='EI', # Expected Improvement
random_state=42
)
print(f"Best hyperparameters found:")
print(f"Learning Rate: {result.x[0]:.6f}")
print(f"Batch Size: {result.x[1]}")
print(f"Num Layers: {result.x[2]}")
print(f"Hidden Size: {result.x[3]}")
print(f"Dropout Rate: {result.x[4]:.3f}")
print(f"Best Loss: {result.fun:.6f}")
# Convergence visualization
from skopt.plots import plot_convergence
plot_convergence(result)
plt.title("Bayesian Optimization Convergence")
plt.show()
# EXPLANATION: The convergence plot shows how the optimizer
# explores the hyperparameter space throughout evaluations (n_calls).
# The blue line represents the best value found up to each iteration,
# demonstrating how the algorithm converges to the optimal solution
# by balancing exploration (searching unexplored areas) and
# exploitation (refining promising areas).
🔎 Specialized Hardware: The New Frontier
Google TPUs: Optimized for massive matrix operations, accelerate parallel evaluations in Evolution Strategies. DeepMind reduced robotic policy training time by 70% using TPUv4!
Groq LPU: Designed for LLM inference, but efficient in gradient-free tasks due to low latency. Executes Simulated Annealing in real-time for portfolio optimization.
Cerebras CS-3: Chip with 900,000 cores accelerates non-differentiable physical simulations used in evolutionary training.
🚨 Reality for Developers: Most still use traditional GPUs (NVIDIA/AMD). Parallelization of gradient-free algorithms on GPUs is an active research area, with libraries like JAX and Ray being fundamental for scaling these methods. JAX enables automatic parallelization of evolutionary algorithms, while Ray facilitates distributed training on clusters.
💡 3. Neuroevolution: NEAT in Action
NEAT (NeuroEvolution of Augmenting Topologies) is a special technique that goes beyond weight optimization: it also evolves the neural network architecture, dynamically adding or removing neurons and connections. This differentiates it from simple genetic algorithms that only optimize fixed weights.
import neat
import pickle
import numpy as np
# NEAT (NeuroEvolution of Augmenting Topologies) configuration
def create_neat_config():
config_text = """
[NEAT]
fitness_criterion = max
fitness_threshold = 3.9
pop_size = 150
reset_on_extinction = False
[DefaultGenome]
# node activation options
activation_default = tanh
activation_mutate_rate = 0.0
activation_options = tanh
# node aggregation options
aggregation_default = sum
aggregation_mutate_rate = 0.0
aggregation_options = sum
# node bias options
bias_init_mean = 0.0
bias_init_stdev = 1.0
bias_max_value = 30.0
bias_min_value = -30.0
bias_mutate_power = 0.5
bias_mutate_rate = 0.7
bias_replace_rate = 0.1
# genome compatibility options
compatibility_disjoint_coefficient = 1.0
compatibility_weight_coefficient = 0.5
# connection add/remove rates
conn_add_prob = 0.5
conn_delete_prob = 0.5
# connection enable options
enabled_default = True
enabled_mutate_rate = 0.01
feed_forward = True
initial_connection = full
# node add/remove rates
node_add_prob = 0.2
node_delete_prob = 0.2
# network parameters
num_hidden = 0
num_inputs = 4
num_outputs = 1
# node response options
response_init_mean = 1.0
response_init_stdev = 0.0
response_max_value = 30.0
response_min_value = -30.0
response_mutate_power = 0.0
response_mutate_rate = 0.0
response_replace_rate = 0.0
# connection weight options
weight_init_mean = 0.0
weight_init_stdev = 1.0
weight_max_value = 30
weight_min_value = -30
weight_mutate_power = 0.5
weight_mutate_rate = 0.8
weight_replace_rate = 0.1
[DefaultSpeciesSet]
compatibility_threshold = 3.0
[DefaultStagnation]
species_fitness_func = max
max_stagnation = 20
species_elitism = 2
[DefaultReproduction]
elitism = 2
survival_threshold = 0.2
"""
# Save temporary configuration
with open('neat_config.txt', 'w') as f:
f.write(config_text)
return neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
neat.DefaultSpeciesSet, neat.DefaultStagnation,
'neat_config.txt')
# Fitness function (example: XOR)
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
net = neat.nn.FeedForwardNetwork.create(genome, config)
genome.fitness = 4.0 # Maximum possible fitness for XOR
# Test all XOR combinations
for xi, xo in zip([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]],
[0.0, 1.0, 1.0, 0.0]):
output = net.activate(xi + [1.0, 1.0]) # Add bias
genome.fitness -= (output[0] - xo) ** 2
# Run NEAT
def run_neat():
config = create_neat_config()
# Initial population
p = neat.Population(config)
# Reporters to track evolution
p.add_reporter(neat.StdOutReporter(True))
stats = neat.StatisticsReporter()
p.add_reporter(stats)
p.add_reporter(neat.Checkpointer(5))
# Evolution for up to 300 generations
winner = p.run(eval_genomes, 300)
print('\nBest genome:\n{!s}'.format(winner))
# Save the winner
with open('winner.pkl', 'wb') as f:
pickle.dump(winner, f)
return winner, stats
# winner, statistics = run_neat()
💡 4. Empirical Comparison: Real Industry Data
Performance Comparison Table (2024):
Source: Adapted from Google Brain and MIT studies (2024). IMPORTANT: Accuracy, cost, and other metric values are highly context-dependent (dataset, architecture, hardware used, training time) and can vary significantly between different implementations and problems.
💡 5. Real Use Cases Changing the World
🤖 Boston Dynamics - Atlas Robot:
🧠 DeepMind - AlphaStar:
💰 Goldman Sachs - Trading Algorithms:
💡 6. Complete Tutorial: CNN without Backprop
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from deap import algorithms, base, creator, tools
import numpy as np
from torch.utils.data import DataLoader
class GradientFreeCNN(nn.Module):
"""Simple CNN for evolutionary training"""
def __init__(self, num_classes=10):
super(GradientFreeCNN, self).__init__()
self.features = nn.Sequential(
# First convolutional layer
nn.Conv2d(3, 16, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
# Second convolutional layer
nn.Conv2d(16, 32, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(32 * 8 * 8, 128),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(128, num_classes)
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
def prepare_cifar10_data(batch_size=64):
"""Prepares CIFAR-10 dataset"""
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Training dataset (small sample for demonstration)
train_dataset = torchvision.datasets.CIFAR10(
root='./data', train=True, download=True, transform=transform
)
# Use only a small sample for demonstration
train_subset = torch.utils.data.Subset(train_dataset, range(1000))
train_loader = DataLoader(
train_subset, batch_size=batch_size, shuffle=True
)
return train_loader
def weights_to_vector(model):
"""Converts model weights to 1D vector"""
vector = []
for param in model.parameters():
vector.extend(param.data.flatten().tolist())
return np.array(vector)
def vector_to_weights(vector, model):
"""Loads 1D vector as model weights - Optimized Version"""
idx = 0
with torch.no_grad():
for param in model.parameters():
param_length = param.numel()
# Optimized version: avoids intermediate tensor creation
param.data.copy_(
torch.from_numpy(vector[idx:idx + param_length]).reshape(param.shape)
)
idx += param_length
def evaluate_cnn_fitness(individual, model, data_loader, device):
"""
Evaluates fitness of an individual (CNN)
FITNESS FUNCTION LOGIC:
- We maximize accuracy (main objective)
- We penalize loss with lower weight (0.1 * avg_loss)
- This hybrid approach is robust because it avoids models that
"cheat" with high accuracy but unstable loss
- The result is a network that not only gets more right,
but also has calibrated confidence in its predictions
"""
# Load individual's weights into model
vector_to_weights(individual, model)
model.eval()
correct = 0
total = 0
total_loss = 0.0
criterion = nn.CrossEntropyLoss()
with torch.no_grad():
for data, target in data_loader:
data, target = data.to(device), target.to(device)
outputs = model(data)
loss = criterion(outputs, target)
total_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
accuracy = correct / total
avg_loss = total_loss / len(data_loader)
# Hybrid fitness: maximize accuracy, minimize loss
# The 0.1 weight for loss ensures accuracy is priority
fitness = accuracy - 0.1 * avg_loss
return (fitness,)
def evolutionary_cnn_training():
"""Trains CNN using evolutionary algorithm"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
# Prepare data
train_loader = prepare_cifar10_data(batch_size=32)
# Create model
model = GradientFreeCNN(num_classes=10).to(device)
# Count parameters
num_params = sum(p.numel() for p in model.parameters())
print(f"Total number of parameters: {num_params:,}")
# Evolutionary algorithm configuration
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
# Initialization with normal distribution
toolbox.register("attr_float", np.random.normal, 0, 0.1)
toolbox.register("individual", tools.initRepeat,
creator.Individual, toolbox.attr_float, n=num_params)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# Genetic operators
toolbox.register("evaluate", evaluate_cnn_fitness,
model=model, data_loader=train_loader, device=device)
toolbox.register("mate", tools.cxBlend, alpha=0.5)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.05, indpb=0.1)
toolbox.register("select", tools.selTournament, tournsize=3)
# Statistics
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max)
# Evolution parameters
POPULATION_SIZE = 20 # Small for demonstration
GENERATIONS = 10 # Few for demonstration
CXPB = 0.7 # Crossover probability
MUTPB = 0.3 # Mutation probability
print(f"Starting evolution with {POPULATION_SIZE} individuals...")
print(f"Generations: {GENERATIONS}")
# Initial population
population = toolbox.population(n=POPULATION_SIZE)
# Execute evolutionary algorithm
population, logbook = algorithms.eaSimple(
population, toolbox,
cxpb=CXPB, mutpb=MUTPB, ngen=GENERATIONS,
stats=stats, verbose=True
)
# Best individual
best_individual = tools.selBest(population, k=1)[0]
print(f"\nBest fitness achieved: {best_individual.fitness.values[0]:.4f}")
# Load best model
vector_to_weights(best_individual, model)
return model, logbook
# Usage example (uncomment to execute)
# trained_model, evolution_log = evolutionary_cnn_training()
print("Evolutionary CNN code ready for execution!")
🌍 Industry Leaders' Perspectives:
Yann LeCun (Meta AI, 2024): "Backpropagation is a crutch. We need methods that don't rely on derivatives, especially for dynamic systems like simulated worlds. Evolutionary algorithms and energy-based models are the future."
Geoffrey Hinton (University of Toronto): "Gradient-free methods are elegant, but still don't compete in scalability. Backpropagation, despite its limitations, remains unbeatable for models with billions of parameters."
💡 7. Essential Frameworks and Libraries
For Evolutionary Algorithms:
# DEAP - Distributed Evolutionary Algorithms in Python
pip install deap
# NEAT - NeuroEvolution of Augmenting Topologies
pip install neat-python
# PyGAD - Genetic Algorithm in Python
pip install pygad
For Bayesian Optimization:
# Scikit-Optimize
pip install scikit-optimize
# Optuna - Hyperparameter optimization framework
pip install optuna
# GPyOpt - Gaussian Process Optimization
pip install GPyOpt
For Advanced Algorithms:
# Nevergrad - Gradient-free optimization
pip install nevergrad
# Hyperopt - Distributed hyperparameter optimization
pip install hyperopt
# Ray Tune - Scalable hyperparameter tuning
pip install ray[tune]
🔎 Surprising Data from Current Research:
📊 MIT (2024): Used Evolution Strategies for GPT-3 fine-tuning in summarization with 80% accuracy of traditional method, but 60% cheaper in terms of computation time and energy consumption - reducing from 72h to 29h of GPU and consumption from 420kWh to 168kWh!
🔬 IBM Quantum: Combination of genetic algorithms with quantum computing for portfolio optimization achieved 34% better performance in risk-adjusted returns
🤖 OpenAI: Experiments with Population-Based Training resulted in RL agents 45% more robust to adversaries and perturbation attacks
💡 8. Bonus Code: Hybrid Optimization
import torch
import torch.nn as nn
import torch.optim as optim
from skopt import gp_minimize
from skopt.space import Real
import numpy as np
class HybridOptimizer:
"""Combines Bayesian Optimization + Gradient Descent"""
def __init__(self, model, train_loader, val_loader):
self.model = model
self.train_loader = train_loader
self.val_loader = val_loader
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def train_with_hyperparams(self, lr, momentum, weight_decay):
"""Trains model with given hyperparameters"""
optimizer = optim.SGD(
self.model.parameters(),
lr=lr,
momentum=momentum,
weight_decay=weight_decay
)
criterion = nn.CrossEntropyLoss()
# Fast training (few epochs for Bayesian optimization)
self.model.train()
for epoch in range(3): # Few epochs for speed
for batch_idx, (data, target) in enumerate(self.train_loader):
if batch_idx > 50: # Limit batches for speed
break
data, target = data.to(self.device), target.to(self.device)
optimizer.zero_grad()
output = self.model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
# Evaluation
return self.evaluate()
def evaluate(self):
"""Evaluates model on validation set"""
self.model.eval()
total_loss = 0
correct = 0
total = 0
with torch.no_grad():
for data, target in self.val_loader:
data, target = data.to(self.device), target.to(self.device)
output = self.model(data)
total_loss += nn.CrossEntropyLoss()(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
total += target.size(0)
accuracy = correct / total
avg_loss = total_loss / len(self.val_loader)
return avg_loss, accuracy
def optimize_hyperparams(self, n_calls=50):
"""Bayesian optimization of hyperparameters"""
# Search space
space = [
Real(1e-4, 1e-1, name='lr', prior='log-uniform'),
Real(0.0, 0.99, name='momentum'),
Real(1e-6, 1e-2, name='weight_decay', prior='log-uniform')
]
def objective(params):
lr, momentum, weight_decay = params
# Reset model (important for fair comparison)
for layer in self.model.modules():
if hasattr(layer, 'reset_parameters'):
layer.reset_parameters()
# Train with hyperparameters
loss, accuracy = self.train_with_hyperparams(lr, momentum, weight_decay)
# Return loss (minimize)
return loss
print("Starting Bayesian hyperparameter optimization...")
result = gp_minimize(
objective, space, n_calls=n_calls,
random_state=42, n_initial_points=10
)
best_lr, best_momentum, best_wd = result.x
print(f"Best hyperparameters:")
print(f" Learning Rate: {best_lr:.6f}")
print(f" Momentum: {best_momentum:.4f}")
print(f" Weight Decay: {best_wd:.6f}")
print(f" Best Loss: {result.fun:.4f}")
return result
# Usage example
# model = YourModel()
# hybrid_opt = HybridOptimizer(model, train_loader, val_loader)
# best_params = hybrid_opt.optimize_hyperparams()
🚀 Conclusion and Next Steps:
The Future is Hybrid: The trend isn't to replace backpropagation, but complement it. Companies like Google, Meta, and Microsoft already use:
Current Challenges:
Call to Action: 🎯 Have you tried Bayesian optimization on your hyperparameters? 🧬 How about training your next network with evolutionary algorithms? 🚀 The future of AI doesn't just depend on gradients - it depends on evolution!
Repositories to Experiment:
#GradientFree #AI #MachineLearning #EvolutionaryAlgorithms #BayesianOptimization #DeepLearning #Innovation #TechLeadership #DataScience
🇧🇷 ARTIGO EM PORTUGUÊS 🇧🇷
Gradient-Free Training: A Revolução Silenciosa que está Mudando o Treinamento de IA
📢 Treinando IA sem Backpropagation: O futuro já chegou e custa 50% menos! 🚀
📌 O Contexto que Poucos Conhecem: Enquanto todos falam de gradientes, uma revolução silenciosa está acontecendo. Em 2024, treinar um LLM com backpropagation pode custar mais de $10 milhões - valor que varia drasticamente dependendo do modelo (GPT-4 vs LLaMA), dataset (Common Crawl vs dados proprietários) e infraestrutura (cloud vs própria). Segundo relatórios do MLCommons, métodos gradient-free estão reduzindo custos em 30-50% para tarefas específicas como otimização de hiperparâmetros e arquiteturas neurais. A Boston Dynamics, DeepMind e IBM já adotaram essas técnicas em produção!
💡 1. Algoritmos Evolutivos: Darwin Encontra a IA
Veja este exemplo real com CMA-ES otimizando uma rede neural completa:
Recommended by LinkedIn
import torch
import torch.nn as nn
from deap import algorithms, base, creator, tools
import numpy as np
# Rede neural simples para classificação
class SimpleNet(nn.Module):
def __init__(self, input_size=784, hidden_sizes=[128, 64], num_classes=10):
super(SimpleNet, self).__init__()
self.layers = nn.ModuleList()
# Camadas densas
prev_size = input_size
for hidden_size in hidden_sizes:
self.layers.append(nn.Linear(prev_size, hidden_size))
prev_size = hidden_size
self.output = nn.Linear(prev_size, num_classes)
self.relu = nn.ReLU()
def forward(self, x):
x = x.view(x.size(0), -1) # Flatten
for layer in self.layers:
x = self.relu(layer(x))
return self.output(x)
# Função de avaliação evolutiva
def eval_network(weights_vector, model, data_loader, device):
"""Avalia fitness de um indivíduo (conjunto de pesos)"""
vector_to_weights(weights_vector, model)
model.eval()
correct = 0
total = 0
with torch.no_grad():
for data, target in data_loader:
data, target = data.to(device), target.to(device)
outputs = model(data)
_, predicted = torch.max(outputs.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
accuracy = correct / total
return (accuracy,) # Retorna tupla (fitness,)
def vector_to_weights(vector, model):
"""Converte vetor 1D em pesos do modelo - Versão Otimizada"""
idx = 0
with torch.no_grad():
for param in model.parameters():
param_length = param.numel()
# Abordagem otimizada: evita tensor intermediário e usa copy_ para eficiência
param.data.copy_(
torch.from_numpy(vector[idx:idx + param_length]).reshape(param.shape)
)
idx += param_length
# Configuração do algoritmo evolutivo
def setup_evolution(model, num_weights):
creator.create("FitnessMax", base.Fitness, weights=(1.0,)) # Maximizar accuracy
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
# Indivíduo = vetor de pesos aleatórios
toolbox.register("attr_float", np.random.randn)
toolbox.register("individual", tools.initRepeat,
creator.Individual, toolbox.attr_float, n=num_weights)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# Operadores evolutivos
toolbox.register("mate", tools.cxBlend, alpha=0.3)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.1, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
return toolbox
# Exemplo de uso
def train_with_evolution(model, train_loader, generations=50, population_size=100):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Conta total de parâmetros
num_weights = sum(p.numel() for p in model.parameters())
print(f"Total de parâmetros: {num_weights:,}")
toolbox = setup_evolution(model, num_weights)
toolbox.register("evaluate", eval_network,
model=model, data_loader=train_loader, device=device)
# População inicial
population = toolbox.population(n=population_size)
# Estatísticas
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("max", np.max)
# Evolução!
print("Iniciando evolução...")
population, logbook = algorithms.eaSimple(
population, toolbox,
cxpb=0.7, # 70% probabilidade de crossover
mutpb=0.2, # 20% probabilidade de mutação
ngen=generations,
stats=stats,
verbose=True
)
# Melhor indivíduo
best_individual = tools.selBest(population, k=1)[0]
vector_to_weights(best_individual, model)
return model, logbook
# Uso prático
model = SimpleNet()
# train_loader seria seu DataLoader do PyTorch
# trained_model, evolution_log = train_with_evolution(model, train_loader)
🌍 Analogia Visual: A Fazenda de Redes Neurais
Imagine uma fazenda onde cada rede neural é um animal:
Método Tradicional (Backprop): Veterinário examina cada animal individualmente, prescreve tratamento específico para cada sintoma 🩺
Método Evolutivo: Os animais mais saudáveis se reproduzem naturalmente, passando genes superiores. Os fracos são eliminados pela seleção natural 🧬
Imagem mental: Um campo verde onde manadas de redes neurais "pastam" dados, e apenas as mais eficientes sobrevivem e se multiplicam
💡 2. Otimização Bayesiana: O Oráculo Estatístico
from skopt import gp_minimize
from skopt.space import Real, Integer
from skopt.utils import use_named_args
import matplotlib.pyplot as plt
# Espaço de hiperparâmetros para otimizar
dimensions = [
Real(low=1e-6, high=1e-1, name='learning_rate', prior='log-uniform'),
Integer(low=16, high=512, name='batch_size'),
Integer(low=1, high=5, name='num_layers'),
Integer(low=32, high=256, name='hidden_size'),
Real(low=0.0, high=0.5, name='dropout_rate'),
]
# Função objetivo (simula treinamento)
@use_named_args(dimensions)
def objective(**params):
"""
Simula o treinamento de um modelo com hiperparâmetros dados
Retorna: validation loss (menor é melhor)
"""
# Em um caso real, você treinaria o modelo aqui
lr = params['learning_rate']
batch_size = params['batch_size']
num_layers = params['num_layers']
hidden_size = params['hidden_size']
dropout = params['dropout_rate']
# Simulação de performance baseada nos hiperparâmetros
# (em caso real, substitua por treinamento real)
performance = (
0.1 * (lr - 0.001)**2 + # LR ótimo próximo de 0.001
0.01 * (batch_size - 64)**2 / 1000 + # Batch size ótimo próximo de 64
0.02 * num_layers + # Penalidade por muitas camadas
0.001 * (hidden_size - 128)**2 / 100 + # Hidden size ótimo próximo de 128
0.1 * dropout # Penalidade por dropout alto
)
return performance
# Otimização Bayesiana
print("Iniciando Otimização Bayesiana...")
result = gp_minimize(
func=objective,
dimensions=dimensions,
n_calls=100, # 100 avaliações
n_initial_points=10, # 10 pontos aleatórios iniciais
acq_func='EI', # Expected Improvement
random_state=42
)
print(f"Melhores hiperparâmetros encontrados:")
print(f"Learning Rate: {result.x[0]:.6f}")
print(f"Batch Size: {result.x[1]}")
print(f"Num Layers: {result.x[2]}")
print(f"Hidden Size: {result.x[3]}")
print(f"Dropout Rate: {result.x[4]:.3f}")
print(f"Melhor Loss: {result.fun:.6f}")
# Visualização da convergência
from skopt.plots import plot_convergence
plot_convergence(result)
plt.title("Convergência da Otimização Bayesiana")
plt.show()
# EXPLICAÇÃO: O gráfico de convergência mostra como o otimizador
# explora o espaço de hiperparâmetros ao longo das avaliações (n_calls).
# A linha azul representa o melhor valor encontrado até cada iteração,
# demonstrando como o algoritmo converge para a solução ótima
# balanceando exploration (busca em áreas inexploradas) e
# exploitation (refinamento de áreas promissoras).
🔎 Hardware Especializado: A Nova Fronteira
TPUs da Google: Otimizadas para operações matriciais massivas, aceleram avaliações paralelas em Evolution Strategies. A DeepMind reduziu 70% do tempo de treinamento de políticas robóticas usando TPUv4!
Groq LPU: Projetado para inferência de LLMs, mas eficiente em tarefas gradient-free devido à baixa latência. Executa Simulated Annealing em tempo real para otimização de portfólios.
Cerebras CS-3: Chip com 900.000 cores acelera simulações físicas não diferenciáveis usadas em treinamento evolutivo.
🚨 Realidade para Desenvolvedores: A maioria ainda utiliza GPUs tradicionais (NVIDIA/AMD). A paralelização de algoritmos gradient-free em GPUs é uma área de pesquisa ativa, com bibliotecas como JAX e Ray sendo fundamentais para escalar esses métodos. JAX permite paralelização automática de algoritmos evolutivos, enquanto Ray facilita distributed training em clusters.
💡 3. Neuroevolução: NEAT em Ação
NEAT (NeuroEvolution of Augmenting Topologies) é uma técnica especial que vai além da otimização de pesos: ele também evolui a arquitetura da rede neural, adicionando ou removendo neurônios e conexões dinamicamente. Isso o diferencia de algoritmos genéticos simples que apenas otimizam pesos fixos.
import neat
import pickle
import numpy as np
# Configuração do NEAT (NeuroEvolution of Augmenting Topologies)
def create_neat_config():
config_text = """
[NEAT]
fitness_criterion = max
fitness_threshold = 3.9
pop_size = 150
reset_on_extinction = False
[DefaultGenome]
# node activation options
activation_default = tanh
activation_mutate_rate = 0.0
activation_options = tanh
# node aggregation options
aggregation_default = sum
aggregation_mutate_rate = 0.0
aggregation_options = sum
# node bias options
bias_init_mean = 0.0
bias_init_stdev = 1.0
bias_max_value = 30.0
bias_min_value = -30.0
bias_mutate_power = 0.5
bias_mutate_rate = 0.7
bias_replace_rate = 0.1
# genome compatibility options
compatibility_disjoint_coefficient = 1.0
compatibility_weight_coefficient = 0.5
# connection add/remove rates
conn_add_prob = 0.5
conn_delete_prob = 0.5
# connection enable options
enabled_default = True
enabled_mutate_rate = 0.01
feed_forward = True
initial_connection = full
# node add/remove rates
node_add_prob = 0.2
node_delete_prob = 0.2
# network parameters
num_hidden = 0
num_inputs = 4
num_outputs = 1
# node response options
response_init_mean = 1.0
response_init_stdev = 0.0
response_max_value = 30.0
response_min_value = -30.0
response_mutate_power = 0.0
response_mutate_rate = 0.0
response_replace_rate = 0.0
# connection weight options
weight_init_mean = 0.0
weight_init_stdev = 1.0
weight_max_value = 30
weight_min_value = -30
weight_mutate_power = 0.5
weight_mutate_rate = 0.8
weight_replace_rate = 0.1
[DefaultSpeciesSet]
compatibility_threshold = 3.0
[DefaultStagnation]
species_fitness_func = max
max_stagnation = 20
species_elitism = 2
[DefaultReproduction]
elitism = 2
survival_threshold = 0.2
"""
# Salva configuração temporária
with open('neat_config.txt', 'w') as f:
f.write(config_text)
return neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
neat.DefaultSpeciesSet, neat.DefaultStagnation,
'neat_config.txt')
# Função de fitness (exemplo: XOR)
def eval_genomes(genomes, config):
for genome_id, genome in genomes:
net = neat.nn.FeedForwardNetwork.create(genome, config)
genome.fitness = 4.0 # Fitness máximo possível para XOR
# Testa todas as combinações XOR
for xi, xo in zip([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]],
[0.0, 1.0, 1.0, 0.0]):
output = net.activate(xi + [1.0, 1.0]) # Adiciona bias
genome.fitness -= (output[0] - xo) ** 2
# Executar NEAT
def run_neat():
config = create_neat_config()
# População inicial
p = neat.Population(config)
# Reporters para acompanhar evolução
p.add_reporter(neat.StdOutReporter(True))
stats = neat.StatisticsReporter()
p.add_reporter(stats)
p.add_reporter(neat.Checkpointer(5))
# Evolução por até 300 gerações
winner = p.run(eval_genomes, 300)
print('\nMelhor genoma:\n{!s}'.format(winner))
# Salva o vencedor
with open('winner.pkl', 'wb') as f:
pickle.dump(winner, f)
return winner, stats
# winner, statistics = run_neat()
💡 4. Comparação Empírica: Dados Reais da Indústria
Tabela Comparativa de Performance (2024):
Fonte: Adaptado de estudos da Google Brain e MIT (2024). IMPORTANTE: Os valores de acurácia, custo e outras métricas são altamente dependentes do contexto (dataset, arquitetura, hardware utilizado, tempo de treinamento) e podem variar significativamente entre diferentes implementações e problemas.
💡 5. Casos de Uso Reais que Estão Mudando o Mundo
🤖 Boston Dynamics - Atlas Robot:
🧠 DeepMind - AlphaStar:
💰 Goldman Sachs - Trading Algorithms:
💡 6. Tutorial Completo: CNN sem Backprop
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from deap import algorithms, base, creator, tools
import numpy as np
from torch.utils.data import DataLoader
class GradientFreeCNN(nn.Module):
"""CNN simples para treinamento evolutivo"""
def __init__(self, num_classes=10):
super(GradientFreeCNN, self).__init__()
self.features = nn.Sequential(
# Primeira camada convolucional
nn.Conv2d(3, 16, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
# Segunda camada convolucional
nn.Conv2d(16, 32, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(32 * 8 * 8, 128),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(128, num_classes)
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
def prepare_cifar10_data(batch_size=64):
"""Prepara dataset CIFAR-10"""
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Dataset de treino (sample pequeno para demonstração)
train_dataset = torchvision.datasets.CIFAR10(
root='./data', train=True, download=True, transform=transform
)
# Usa apenas uma pequena amostra para demonstração
train_subset = torch.utils.data.Subset(train_dataset, range(1000))
train_loader = DataLoader(
train_subset, batch_size=batch_size, shuffle=True
)
return train_loader
def weights_to_vector(model):
"""Converte pesos do modelo em vetor 1D"""
vector = []
for param in model.parameters():
vector.extend(param.data.flatten().tolist())
return np.array(vector)
def vector_to_weights(vector, model):
"""Carrega vetor 1D como pesos do modelo - Versão Otimizada"""
idx = 0
with torch.no_grad():
for param in model.parameters():
param_length = param.numel()
# Versão otimizada: evita criação de tensor intermediário
param.data.copy_(
torch.from_numpy(vector[idx:idx + param_length]).reshape(param.shape)
)
idx += param_length
def evaluate_cnn_fitness(individual, model, data_loader, device):
"""
Avalia fitness de um indivíduo (CNN)
LÓGICA DA FUNÇÃO FITNESS:
- Maximizamos a acurácia (principal objetivo)
- Penalizamos a loss com peso menor (0.1 * avg_loss)
- Esta abordagem híbrida é robusta porque evita modelos que
"trapaceiam" com alta acurácia mas loss instável
- O resultado é uma rede que não apenas acerta mais,
mas também tem confiança calibrada em suas predições
"""
# Carrega pesos do indivíduo no modelo
vector_to_weights(individual, model)
model.eval()
correct = 0
total = 0
total_loss = 0.0
criterion = nn.CrossEntropyLoss()
with torch.no_grad():
for data, target in data_loader:
data, target = data.to(device), target.to(device)
outputs = model(data)
loss = criterion(outputs, target)
total_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
accuracy = correct / total
avg_loss = total_loss / len(data_loader)
# Fitness híbrida: maximizar accuracy, minimizar loss
# O peso 0.1 para loss garante que accuracy seja prioridade
fitness = accuracy - 0.1 * avg_loss
return (fitness,)
def evolutionary_cnn_training():
"""Treina CNN usando algoritmo evolutivo"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Usando device: {device}")
# Prepara dados
train_loader = prepare_cifar10_data(batch_size=32)
# Cria modelo
model = GradientFreeCNN(num_classes=10).to(device)
# Conta parâmetros
num_params = sum(p.numel() for p in model.parameters())
print(f"Número total de parâmetros: {num_params:,}")
# Configuração do algoritmo evolutivo
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
# Inicialização com distribuição normal
toolbox.register("attr_float", np.random.normal, 0, 0.1)
toolbox.register("individual", tools.initRepeat,
creator.Individual, toolbox.attr_float, n=num_params)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# Operadores genéticos
toolbox.register("evaluate", evaluate_cnn_fitness,
model=model, data_loader=train_loader, device=device)
toolbox.register("mate", tools.cxBlend, alpha=0.5)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.05, indpb=0.1)
toolbox.register("select", tools.selTournament, tournsize=3)
# Estatísticas
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max)
# Parâmetros da evolução
POPULATION_SIZE = 20 # Pequeno para demonstração
GENERATIONS = 10 # Poucos para demonstração
CXPB = 0.7 # Probabilidade de crossover
MUTPB = 0.3 # Probabilidade de mutação
print(f"Iniciando evolução com {POPULATION_SIZE} indivíduos...")
print(f"Gerações: {GENERATIONS}")
# População inicial
population = toolbox.population(n=POPULATION_SIZE)
# Executa algoritmo evolutivo
population, logbook = algorithms.eaSimple(
population, toolbox,
cxpb=CXPB, mutpb=MUTPB, ngen=GENERATIONS,
stats=stats, verbose=True
)
# Melhor indivíduo
best_individual = tools.selBest(population, k=1)[0]
print(f"\nMelhor fitness alcançado: {best_individual.fitness.values[0]:.4f}")
# Carrega melhor modelo
vector_to_weights(best_individual, model)
return model, logbook
# Exemplo de uso (descomente para executar)
# trained_model, evolution_log = evolutionary_cnn_training()
print("Código CNN evolutivo pronto para execução!")
🌍 Perspectivas de Líderes da Indústria:
Yann LeCun (Meta AI, 2024): "Backpropagation é uma muleta. Precisamos de métodos que não dependam de derivadas, especialmente para sistemas dinâmicos como mundos simulados. Algoritmos evolutivos e energy-based models são o futuro."
Geoffrey Hinton (University of Toronto): "Métodos gradient-free são elegantes, mas ainda não competem em escalabilidade. O backpropagation, apesar de seus limites, permanece insuperável para modelos com bilhões de parâmetros."
💡 7. Frameworks e Bibliotecas Essenciais
Para Algoritmos Evolutivos:
# DEAP - Distributed Evolutionary Algorithms in Python
pip install deap
# NEAT - NeuroEvolution of Augmenting Topologies
pip install neat-python
# PyGAD - Genetic Algorithm in Python
pip install pygad
Para Otimização Bayesiana:
# Scikit-Optimize
pip install scikit-optimize
# Optuna - Hyperparameter optimization framework
pip install optuna
# GPyOpt - Gaussian Process Optimization
pip install GPyOpt
Para Algoritmos Avançados:
# Nevergrad - Gradient-free optimization
pip install nevergrad
# Hyperopt - Distributed hyperparameter optimization
pip install hyperopt
# Ray Tune - Scalable hyperparameter tuning
pip install ray[tune]
🔎 Dados Surpreendentes da Pesquisa Atual:
📊 MIT (2024): Usou Evolution Strategies para fine-tuning do GPT-3 em sumarização com 80% da acurácia do método tradicional, mas 60% mais barato em termos de tempo de computação e consumo energético - reduzindo de 72h para 29h de GPU e consumo de 420kWh para 168kWh!
🔬 IBM Quantum: Combinação de algoritmos genéticos com computação quântica para otimizar portfólios financeiros alcançou 34% melhor performance em risk-adjusted returns
🤖 OpenAI: Experimentos com Population-Based Training resultaram em agentes de RL 45% mais robustos a adversários e ataques de perturbação
💡 8. Código Bônus: Otimização Híbrida
import torch
import torch.nn as nn
import torch.optim as optim
from skopt import gp_minimize
from skopt.space import Real
import numpy as np
class HybridOptimizer:
"""Combina Bayesian Optimization + Gradient Descent"""
def __init__(self, model, train_loader, val_loader):
self.model = model
self.train_loader = train_loader
self.val_loader = val_loader
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def train_with_hyperparams(self, lr, momentum, weight_decay):
"""Treina modelo com hiperparâmetros dados"""
optimizer = optim.SGD(
self.model.parameters(),
lr=lr,
momentum=momentum,
weight_decay=weight_decay
)
criterion = nn.CrossEntropyLoss()
# Treinamento rápido (poucas épocas para otimização bayesiana)
self.model.train()
for epoch in range(3): # Poucos epochs para speed
for batch_idx, (data, target) in enumerate(self.train_loader):
if batch_idx > 50: # Limita batches para speed
break
data, target = data.to(self.device), target.to(self.device)
optimizer.zero_grad()
output = self.model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
# Avaliação
return self.evaluate()
def evaluate(self):
"""Avalia modelo no conjunto de validação"""
self.model.eval()
total_loss = 0
correct = 0
total = 0
with torch.no_grad():
for data, target in self.val_loader:
data, target = data.to(self.device), target.to(self.device)
output = self.model(data)
total_loss += nn.CrossEntropyLoss()(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
total += target.size(0)
accuracy = correct / total
avg_loss = total_loss / len(self.val_loader)
return avg_loss, accuracy
def optimize_hyperparams(self, n_calls=50):
"""Otimização bayesiana dos hiperparâmetros"""
# Espaço de busca
space = [
Real(1e-4, 1e-1, name='lr', prior='log-uniform'),
Real(0.0, 0.99, name='momentum'),
Real(1e-6, 1e-2, name='weight_decay', prior='log-uniform')
]
def objective(params):
lr, momentum, weight_decay = params
# Reset modelo (importante para comparação justa)
for layer in self.model.modules():
if hasattr(layer, 'reset_parameters'):
layer.reset_parameters()
# Treina com hiperparâmetros
loss, accuracy = self.train_with_hyperparams(lr, momentum, weight_decay)
# Retorna loss (minimizar)
return loss
print("Iniciando otimização bayesiana de hiperparâmetros...")
result = gp_minimize(
objective, space, n_calls=n_calls,
random_state=42, n_initial_points=10
)
best_lr, best_momentum, best_wd = result.x
print(f"Melhores hiperparâmetros:")
print(f" Learning Rate: {best_lr:.6f}")
print(f" Momentum: {best_momentum:.4f}")
print(f" Weight Decay: {best_wd:.6f}")
print(f" Melhor Loss: {result.fun:.4f}")
return result
# Exemplo de uso
# model = YourModel()
# hybrid_opt = HybridOptimizer(model, train_loader, val_loader)
# best_params = hybrid_opt.optimize_hyperparams()
🚀 Conclusão e Próximos Passos:
O Futuro é Híbrido: A tendência não é substituir o backpropagation, mas complementá-lo. Empresas como Google, Meta e Microsoft já usam:
Desafios Atuais:
Call to Action: 🎯 Já experimentou otimização bayesiana nos seus hiperparâmetros? 🧬 Que tal treinar sua próxima rede com algoritmos evolutivos? 🚀 O futuro da IA não depende apenas de gradientes - depende de evolução!
Repositórios para Experimentar:
Thanks for sharing, Marcelo
Seeing the cost reduction statistics makes me realize how impactful this could be for democratizing AI globally
The blend of evolutionary strategies with Bayesian optimization and hybrid methods could be the key to robust AI training.
Posts like this spark curiosity and encourage deeper exploration into methods that can truly shape the future of AI.
This highlights the importance of exploring alternative methods instead of relying on a single dominant paradigm.