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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
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
import pytest
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import torch
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import torch.nn as nn
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@pytest.fixture
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def xor_data():
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"""
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Returns deterministic XOR input features (4, 2) and labels (4, 1) as float32 torch tensors on CPU.
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"""
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x = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]], dtype=torch.float32)
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y = torch.tensor([[0.0], [1.0], [1.0], [0.0]], dtype=torch.float32)
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return x, y
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@pytest.fixture
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def model_factory():
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"""
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Factory fixture producing deterministic, PyTorch nn.Module models.
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Supports units tuning, zero initialization, and input/output dimension changes.
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"""
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def _create_model(
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units: int = 4,
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zero_init: bool = False,
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input_dim: int = 2,
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output_dim: int = 1,
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) -> nn.Module:
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torch.manual_seed(42)
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layers = [
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nn.Linear(input_dim, units),
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nn.ReLU(),
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nn.Linear(units, output_dim),
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]
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model = nn.Sequential(*layers)
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if zero_init:
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for m in model.modules():
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if isinstance(m, nn.Linear):
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nn.init.zeros_(m.weight)
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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return model
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return _create_model
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