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{
"schema": "rain_ep_structured_bias_screen_v1",
"author": {
"repository": "https://github.com/rain-neuromorphics/energy-based-learning",
"revision": "6b253fd8a5d267535f58ab79992256ef10031ceb"
},
"protocol": {
"dataset": "FashionMNIST",
"network": "author ConvHopfieldEnergy28 32-64-10",
"algorithm": "positive equilibrium propagation",
"adapter": "layer",
"mode": "constant",
"bias_ratio": 4.0,
"predictor_rate": 0.2,
"neutral_cadence": 1,
"layer_calibration_steps": 1,
"calibration_batches": 0,
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"calibration_seconds": 7.152557373046875e-07,
"epochs": 3,
"train_limit": 10000,
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"batch_size": 128,
"training_iterations": 12,
"inference_iterations": 30,
"seed": 1988,
"device": "cuda",
"determinism": "author_default",
"autodiff_used_for_learning": false
},
"metrics": [
{
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"train_cost": NaN,
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"finite": false,
"corrector": {
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"clean_state_difference_rms": NaN,
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"bias_to_clean_state_difference_rms": NaN,
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],
"epochs_completed": 1,
"final": {
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"residual_to_clean_state_difference_rms": NaN,
"neutral_observations": 128
},
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}
}
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