jensandClaude Sonnet 4.6
3edc124a5c
- Moved src/tests/ → tests/ - Added test_* functions with assertions to script-style test files - Added main() to each so IDEs offer it as a separate run target from pytest - Fixed cupy_test.py: remove spurious x_gpu += x_cpu, fix duplicate xlabel Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
from rbm.matrix import Mat, np
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from rbm.entity import Entity, EntityParams, TrainingParams
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from model.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 LinearModel(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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class _SmallLinearModel(Model):
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def __init__(self):
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super().__init__("linear_pytest", "results")
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self.unit1 = Entity((64, 32), EntityParams(do_gaussian_visible=True),
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TrainingParams(learning_rate=0.005, momentum=0.9, num_epochs=100))
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def forward(self, x: Mat):
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return self.unit1.forward(x)
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def reconstruct(self, x: Mat):
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return self.unit1.reconstruct(x)
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def test_linear():
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model = _SmallLinearModel()
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model.init(0.1)
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batch = normalize(np.random.randn(50, 64, dtype=np.float64))
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model.train(batch)
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for pattern in batch:
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recon = model.reconstruct(model.forward(pattern))
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assert recon.size == pattern.size
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def main():
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model = LinearModel("linear", "results")
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model.init(0.1)
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model.load()
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training_batch = np.random.randn(N_CASES, N_VIS, dtype=np.float64)
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training_batch_norm = normalize(training_batch)
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model.train(training_batch_norm)
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model.save()
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for pattern in training_batch:
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model.reconstruct(model.forward(pattern))
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if __name__ == "__main__":
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main()
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