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https://github.com/jung-geun/PSO.git
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feat: modernize PSO and add convergence research
Migrate the package and examples to the tensor-native PyTorch implementation, add benchmark evidence, and add the guarded post-training convergence protocol with TensorBoard progress monitoring and hash-verified recovery. Constraint: Preserve one-shot official-test sealing and auditable research artifacts Rejected: Commit local .omc runs and downloaded datasets | multi-gigabyte runtime state is machine-local Confidence: high Scope-risk: broad Not-tested: Production CUDA run on pieroot-server
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# %%
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import json
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import os
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import sys
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import argparse
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import torch
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import torch.nn as nn
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import numpy as np
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import tensorflow as tf
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from keras.datasets import fashion_mnist
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from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
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from keras.models import Sequential
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from pso import optimizer
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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from pso import Optimizer
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from cli import add_pso_args, build_optimizer_kwargs
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def get_data():
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(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
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def get_data(seed: int = 42):
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from sklearn.decomposition import PCA
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from torchvision.datasets import FashionMNIST
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x_train, x_test = x_train / 255.0, x_test / 255.0
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x_train = x_train.reshape((60000, 28, 28, 1))
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x_test = x_test.reshape((10000, 28, 28, 1))
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train_dataset = FashionMNIST(root="./data", train=True, download=True)
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test_dataset = FashionMNIST(root="./data", train=False, download=True)
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y_train, y_test = tf.one_hot(y_train, 10), tf.one_hot(y_test, 10)
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x_train_raw = (train_dataset.data[:3000].float() / 255.0).reshape(3000, -1).numpy()
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y_train = train_dataset.targets[:3000].long()
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x_train, x_test = tf.convert_to_tensor(x_train), tf.convert_to_tensor(x_test)
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y_train, y_test = tf.convert_to_tensor(y_train), tf.convert_to_tensor(y_test)
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x_test_raw = (test_dataset.data[:1000].float() / 255.0).reshape(1000, -1).numpy()
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y_test = test_dataset.targets[:1000].long()
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print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
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print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
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pca = PCA(n_components=32, whiten=True, random_state=seed)
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x_train_pca = pca.fit_transform(x_train_raw)
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x_test_pca = pca.transform(x_test_raw)
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x_train = torch.tensor(x_train_pca, dtype=torch.float32)
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x_test = torch.tensor(x_test_pca, dtype=torch.float32)
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print(f"x_train : {x_train.shape} | y_train : {y_train.shape}")
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print(f"x_test : {x_test.shape} | y_test : {y_test.shape}")
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return x_train, y_train, x_test, y_test
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def make_model():
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model = Sequential()
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model.add(
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Conv2D(32, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
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def make_model(seed: int = 42):
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torch.manual_seed(seed)
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return nn.Linear(32, 10)
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def main():
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parser = argparse.ArgumentParser(description="PSO Fashion-MNIST Benchmark Script")
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add_pso_args(
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parser,
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defaults={
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"method": "original",
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"initialization": "model_noise",
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"evaluation": "fixed_subset",
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"convergence": "particle_reset",
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"refinement": "adam",
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"n_particles": 30,
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"c0": None,
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"c1": None,
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"w_min": None,
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"w_max": None,
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"negative_swarm": 0.0,
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"mutation_swarm": 0.05,
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"particle_min": -3.0,
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"particle_max": 3.0,
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"velocity_limit_ratio": 0.1,
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"boundary_strategy": "reflect",
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"seed": 42,
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"epochs": 80,
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"batch_size": 1000,
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"fitness_size": 2000,
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"renewal": "loss",
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"output_dir": "output/fashion_mnist",
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"checkpoint_interval": 25,
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"refinement_epochs": 10,
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"refinement_lr": 0.001,
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},
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)
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Flatten())
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model.add(Dropout(0.25))
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model.add(Dense(256, activation="relu"))
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model.add(Dense(128, activation="relu"))
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model.add(Dense(10, activation="softmax"))
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args = parser.parse_args()
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return model
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model = make_model(seed=args.seed)
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x_train, y_train, x_test, y_test = get_data(seed=args.seed)
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fitness_size = args.fitness_size if args.evaluation == "fixed_subset" else None
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refinement_epochs = args.refinement_epochs if args.refinement == "adam" else 0
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kwargs = build_optimizer_kwargs(
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args,
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model=model,
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loss=nn.CrossEntropyLoss(),
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task="multiclass",
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inertia_profile={"c0": 0.7, "c1": 0.5, "w_min": 0.1, "w_max": 0.8},
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)
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pso_fashion = Optimizer(**kwargs)
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print(f"Optimizer device: {pso_fashion.device}")
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best_score = pso_fashion.fit(
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x_train,
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y_train,
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epochs=args.epochs,
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batch_size=args.batch_size,
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fitness_size=fitness_size,
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renewal=args.renewal,
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validation_data=(x_test, y_test),
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output_dir=args.output_dir,
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checkpoint_interval=25,
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save_info=True,
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refinement_epochs=refinement_epochs,
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refinement_lr=args.refinement_lr,
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)
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print(f"Done! Best score: {best_score}")
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# %%
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model = make_model()
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x_train, y_train, x_test, y_test = get_data()
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pso_mnist = optimizer(
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model,
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loss="categorical_crossentropy",
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n_particles=200,
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c0=0.7,
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c1=0.5,
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w_min=0.1,
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w_max=0.8,
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negative_swarm=0.0,
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mutation_swarm=0.05,
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convergence_reset=True,
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convergence_reset_patience=10,
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convergence_reset_monitor="loss",
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)
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best_score = pso_mnist.fit(
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x_train,
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y_train,
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epochs=1000,
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save_info=True,
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log=2,
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log_name="fashion_mnist",
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renewal="loss",
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check_point=25,
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batch_size=5000,
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)
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print("Done!")
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sys.exit(0)
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if __name__ == "__main__":
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main()
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