70 lines
1.5 KiB
Python
70 lines
1.5 KiB
Python
import numpy as np
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from params import RbmParams
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from state import RbmState
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from status import Status
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from cd_train import cd_jens
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from entity import Entity
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class Layer:
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def __init__(self, name: str, shape: tuple[int, int, int, int], params: RbmParams):
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self.name = name
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self.shape = shape
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self.entity = Entity((shape[0]*shape[1]+shape[2], shape[3]), params)
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self.state_filename = f"{self.name}_state.npz"
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def init(self, std: float):
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self.entity.state.init(mu=0, std=std)
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def save(self, filename: str = None):
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if filename is None:
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filename = self.state_filename
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self.entity.state.to_file(filename)
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def load(self, filename: str = None):
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if filename is None:
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filename = self.state_filename
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state = RbmState.from_file(filename)
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if state is not None:
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self.entity.state = state
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def xor():
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# Create params
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params = RbmParams()
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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 = 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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layer.entity.train(training_batch, cd_jens, 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 = np.array([[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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