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>
83 lines
2.0 KiB
Python
83 lines
2.0 KiB
Python
import matplotlib.pyplot as plt
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from model.model import Model
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from label.label import Label
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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 LabelLearner(Model):
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def __init__(self, name: str, dim, work_dir: str = '.'):
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super().__init__(name, work_dir)
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self.unit1 = Entity(dim, EntityParams(do_gaussian_hidden=False), TrainingParams(learning_rate=0.01, 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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return x
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def backward(self, x: Mat):
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x = self.unit1.reconstruct(x)
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return x
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if __name__ == "__main__":
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voc_size = 100
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do_train = False
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work_dir = "../../results"
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prj_root = "/home/jens/work/repos/Rbm"
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# Create Labeler
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label_w = 5
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label_h = 5
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enc = Label(voc_size, label_w*label_h, Label.EncodingType.OneHot, work_dir)
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# Load
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enc.fitter.load()
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# Train
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if do_train:
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train_labels = list(range(voc_size))
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enc.fit(train_labels)
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enc.fitter.save()
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# Encode test labels
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test_labels = np.array([1, 23, 99, 37, 55, 7, 31, 10, 19, 70])
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encoded, _ = enc.encode(test_labels)
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# Create model
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model = LabelLearner("Label-Learner", (label_w*label_h, 16), work_dir)
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# Init state
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model.init(0.01)
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# load state
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model.load()
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# Train
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model.train(encoded)
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# save state
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model.save()
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# Plot
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cmap = 'Grays'
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fig, axes = plt.subplots(3, len(encoded), figsize=(12, 3))
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for index, inp in enumerate(encoded):
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hidden = model.forward(inp)
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recon = model.backward(hidden)
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img_hidden = np.reshape(hidden, (4, 4))
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img_recon = np.reshape(recon, (label_w, label_h))
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axes[0][index].imshow(img_hidden.get(), cmap=cmap)
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axes[0][index].axis('off')
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axes[0][index].set_title(f'{test_labels[index]}')
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axes[1][index].imshow(img_recon.get(), cmap=cmap)
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axes[1][index].axis('off')
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axes[1][index].set_title(f'{test_labels[index]}')
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axes[2][index].imshow(np.reshape(inp.get(), (label_w, label_h)), cmap=cmap)
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axes[2][index].axis('off')
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axes[2][index].set_title(f'{test_labels[index]}')
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plt.show()
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print("Test: [passed]")
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