67 lines
1.6 KiB
Python
67 lines
1.6 KiB
Python
from rbm.matrix import Mat, np
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from rbm.entity import Entity, EntityParams, TrainingParams
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from rbm.model import Model
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from image.sub_image import normalize
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WORK_DIR = "../../results"
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USE_OPTIMIZER = True
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N_VIS = 3000
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N_CASES = 1000
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class TestModel(Model):
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def __init__(self, name: str, work_dir: str = '.', do_gaussian_hidden=False):
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super().__init__(name, work_dir)
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if do_gaussian_hidden:
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# Hidden gaussian
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self.unit1 = Entity((N_VIS, 64), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=True),
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TrainingParams(learning_rate=0.01, momentum=0.9, num_epochs=1000))
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else:
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# Hidden binary
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self.unit1 = Entity((N_VIS, 1000), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False),
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TrainingParams(learning_rate=0.005, momentum=0.9, num_epochs=1000, mini_batch_size=1000))
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def forward(self, x: Mat):
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x = self.unit1.forward(x)
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return x
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def reconstruct(self, x: Mat):
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x = self.unit1.reconstruct(x)
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return x
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def linear():
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work_dir = "results"
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prj_name = "linear"
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prj_root = "/home/jens/work/repos/Rbm"
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# Create layer
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model = TestModel(prj_name, "results")
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# Init weights
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model.init(0.1)
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# Load weights (if exists)
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model.load()
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# Prepare training data
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training_batch = np.random.randn(N_CASES, N_VIS, dtype=np.float64)
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# Normalize training data
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training_batch_norm = normalize(training_batch)
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# Train layer
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model.train(training_batch_norm)
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# Save weights
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model.save()
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# Test with test data
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test_batch = training_batch
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for pattern in test_batch:
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h = model.forward(pattern)
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v = model.reconstruct(h)
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# print(f"P{pattern} : {v}")
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if __name__ == "__main__":
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linear()
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print("Test: [passed]")
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