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https://github.com/jung-geun/PSO.git
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jung-geun
813433000a
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
139 lines
3.8 KiB
Python
139 lines
3.8 KiB
Python
"""Dry Bean dataset gradient baseline (PyTorch standard backprop optimizer, non-PSO)."""
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import copy
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader, TensorDataset
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder
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from ucimlrepo import fetch_ucirepo
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class BeanModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(16, 12),
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nn.ReLU(),
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nn.Linear(12, 8),
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nn.ReLU(),
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nn.Linear(8, 7),
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)
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def forward(self, x):
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return self.net(x)
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def get_data(seed: int = 42):
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dry_bean_dataset = fetch_ucirepo(id=602)
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X = dry_bean_dataset.data.features
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y = dry_bean_dataset.data.targets
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x = X.to_numpy().astype("float32")
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encoder = LabelEncoder()
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y_encoded = encoder.fit_transform(y.values.ravel()).astype("int64")
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x_train, x_test, y_train, y_test = train_test_split(
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x, y_encoded, test_size=0.2, random_state=seed, shuffle=True
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)
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return (
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torch.tensor(x_train, dtype=torch.float32),
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torch.tensor(x_test, dtype=torch.float32),
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torch.tensor(y_train, dtype=torch.int64),
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torch.tensor(y_test, dtype=torch.int64),
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)
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def get_device() -> torch.device:
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if (
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hasattr(torch.backends, "mps")
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and torch.backends.mps.is_built()
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and torch.backends.mps.is_available()
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):
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return torch.device("mps")
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elif torch.cuda.is_available():
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return torch.device("cuda")
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return torch.device("cpu")
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def main():
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torch.manual_seed(42)
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np.random.seed(42)
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device = get_device()
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print(f"Selected device: {device}")
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x_train, x_test, y_train, y_test = get_data(seed=42)
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train_dataset = TensorDataset(x_train, y_train)
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val_dataset = TensorDataset(x_test, y_test)
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train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)
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val_loader = DataLoader(val_dataset, batch_size=10, shuffle=False)
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model = BeanModel().to(device)
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optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
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criterion = nn.CrossEntropyLoss()
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best_val_loss = float("inf")
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best_state = None
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patience = 10
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min_delta = 0.001
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patience_counter = 0
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for epoch in range(150):
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model.train()
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for bx, by in train_loader:
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bx, by = bx.to(device), by.to(device)
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optimizer.zero_grad()
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out = model(bx)
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loss = criterion(out, by)
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loss.backward()
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optimizer.step()
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model.eval()
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val_loss = 0.0
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total = 0
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with torch.no_grad():
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for bx, by in val_loader:
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bx, by = bx.to(device), by.to(device)
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out = model(bx)
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loss = criterion(out, by)
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val_loss += loss.item() * bx.size(0)
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total += bx.size(0)
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val_loss /= total
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if val_loss < best_val_loss - min_delta:
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best_val_loss = val_loss
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best_state = copy.deepcopy(model.state_dict())
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patience_counter = 0
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else:
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patience_counter += 1
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if patience_counter >= patience:
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break
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if best_state is not None:
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model.load_state_dict(best_state)
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model.eval()
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test_loss = 0.0
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correct = 0
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total = 0
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with torch.no_grad():
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for bx, by in val_loader:
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bx, by = bx.to(device), by.to(device)
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out = model(bx)
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loss = criterion(out, by)
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test_loss += loss.item() * bx.size(0)
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preds = out.argmax(dim=1)
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correct += (preds == by).sum().item()
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total += bx.size(0)
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test_loss /= total
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test_acc = correct / total
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print(f"Final test loss: {test_loss:.4f}, accuracy: {test_acc:.4f}")
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if __name__ == "__main__":
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main()
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