41 lines
902 B
Python
41 lines
902 B
Python
from rbm.params import EntityParams
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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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def xor():
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# Create params
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params = EntityParams()
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params.do_rao_blackwell = True
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params.num_gibbs_samples = 3
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# Create layer
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layer = Layer("Layer_0", (3, 1, 0, 16), 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()
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# Prepare training data
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training_batch = Mat([[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()
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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.gibbs_v_to_h(pattern)
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v = layer.entity.gibbs_h_to_v(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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