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>
87 lines
2.3 KiB
Python
87 lines
2.3 KiB
Python
import random
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import matplotlib.pyplot as plt
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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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N = 8
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N_VIS = N * N
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N_HID = 32
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N_CASES = 400
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class BinaryAEModel(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(
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(N_VIS, N_HID),
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EntityParams(),
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TrainingParams(learning_rate=0.1, momentum=0.9, num_epochs=5000, do_rao_blackwell=True)
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)
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def forward(self, x: Mat) -> Mat:
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return self.unit1.forward(x)
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def reconstruct(self, x: Mat) -> Mat:
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return self.unit1.reconstruct(x)
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def make_bars_and_stripes(n_cases: int, n: int = N) -> Mat:
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data = np.zeros((n_cases, n * n), dtype=np.float64)
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for i in range(n_cases):
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img = np.zeros((n * n,), dtype=np.float64)
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if random.random() > 0.5:
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row = random.randint(0, n - 1)
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img[row * n:(row + 1) * n] = 1.0
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else:
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col = random.randint(0, n - 1)
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img[col::n] = 1.0
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data[i] = img
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return data
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def test_binary_autoencoder():
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model = BinaryAEModel("binary_autoencoder_pytest", "results")
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model.unit1.training_params.num_epochs = 500
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model.init(0.01)
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train_batch = make_bars_and_stripes(N_CASES)
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model.train(train_batch)
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mae = float(np.mean(np.abs(train_batch - np.array([model.reconstruct(model.forward(x)) for x in train_batch]))))
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assert mae < 0.4, f"Reconstruction MAE {mae:.4f} too high"
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def main():
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model = BinaryAEModel("binary_autoencoder", "results")
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model.init(0.01)
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train_batch = make_bars_and_stripes(N_CASES)
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model.train(train_batch)
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model.save()
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test_batch = make_bars_and_stripes(10)
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n_show = len(test_batch)
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fig, axes = plt.subplots(2, n_show, figsize=(n_show * 1.5, 3))
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axes[0, 0].set_ylabel("Original")
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axes[1, 0].set_ylabel("Recon")
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total_error = 0.0
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for i, inp in enumerate(test_batch):
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h = model.forward(inp)
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recon = model.reconstruct(h)
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total_error += float(np.mean(np.abs(inp - recon)))
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axes[0, i].imshow(np.asnumpy(np.reshape(inp, (N, N))), cmap='gray', vmin=0, vmax=1)
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axes[0, i].axis('off')
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axes[1, i].imshow(np.asnumpy(np.reshape(recon, (N, N))), cmap='gray', vmin=0, vmax=1)
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axes[1, i].axis('off')
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print(f"Mean reconstruction error: {total_error / n_show:.4f}")
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plt.tight_layout()
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plt.show()
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if __name__ == "__main__":
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main()
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