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>
57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
from model.model import Model
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from rbm.entity import Entity, EntityParams, TrainingParams
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from rbm.matrix import Mat, np
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class DeepModel(Model):
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def __init__(self, name: str, work_dir: str = '.'):
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super().__init__(name, work_dir)
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self.unit1 = Entity((1024, 333), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
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self.unit2 = Entity((333, 64), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
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self.unit3 = Entity((64, 128), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
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self.unit4 = Entity((128, 128), EntityParams(), TrainingParams(learning_rate=0.1, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
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def forward(self, x: Mat):
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x = self.unit1.forward(x)
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x = self.unit2.forward(x)
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x = self.unit3.forward(x)
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x = self.unit4.forward(x)
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return x
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def backward(self, x: Mat):
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x = self.unit4.reconstruct(x)
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x = self.unit3.reconstruct(x)
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x = self.unit2.reconstruct(x)
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x = self.unit1.reconstruct(x)
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return x
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def test_deep_model():
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model = DeepModel("TestModel_pytest", "results")
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model.unit1.training_params.num_epochs = 100
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model.unit2.training_params.num_epochs = 100
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model.unit3.training_params.num_epochs = 100
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model.unit4.training_params.num_epochs = 100
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model.init(0.1)
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batch = (np.random.rand(32, 1024) > 0.5).astype(np.float64)
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model.train(batch)
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errors = [float(np.mean((model.backward(model.forward(x)) - x) ** 2)) for x in batch]
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assert sum(errors) / len(errors) < 0.5, "Deep model reconstruction MSE too high"
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def main():
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model = DeepModel("TestModel", "results")
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model.init(0.1)
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model.load()
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batch = (np.random.rand(64, 1024) > 0.5).astype(np.float64)
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model.train(batch)
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model.save()
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for index, inp in enumerate(batch):
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out = model.backward(model.forward(inp))[0]
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print(f"- Pattern {index} -------------------------")
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print(f"Input : {inp}")
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print(f"Output : {(out > 0.9).astype(np.float64)}")
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print(f"Error : {np.mean((out - inp)**2):0.3f}")
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if __name__ == "__main__":
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main()
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