jensandClaude Sonnet 4.6
f7ed8563d7
- Add batch_delay() to shift visible input per unit index - Unify forward_step() to work with combined vc matrix - Fix split() to always slice on axis=1 - Add index param to Entity for readable naming - Rename test_xor.py to xor.py, replace Mat with np.array Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
import os.path
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from rbm.layer import Layer
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from rbm.status import Status
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from rbm.train import train
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from rbm.matrix import Mat, np
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from rbm.entity import EntityParams, TrainingParams
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WORK_DIR = "../../results"
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USE_OPTIMIZER = True
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def xor():
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# Create params
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entity_params = EntityParams()
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training_params = TrainingParams()
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entity_params.do_rao_blackwell = True
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entity_params.num_gibbs_samples = 3
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# Create layer
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layer = Layer("Layer_0", (3, 1, 0, 16), entity_params, training_params)
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# Init weights
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layer.init(0.01)
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# Load weights (if exists)
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layer.load(os.path.join(WORK_DIR, "xor_layer0_state.npz"))
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# Prepare training data
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training_batch = np.array([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
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# Train layer
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train(layer.entity, training_batch, Status())
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# Save weights
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layer.save(os.path.join(WORK_DIR, "xor_layer0_state.npz"))
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# Test with test data
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test_batch = Mat([[0,0,0], [0,1,0], [1,0,0], [1,1,0]], dtype=np.float64)
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for pattern in test_batch:
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h = layer.entity.forward(pattern)
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v = layer.entity.reconstruct(h)
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print(f"P{pattern} : {v}")
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if __name__ == "__main__":
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xor()
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print("Test: [passed]")
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