added LabelLearner

src/tests/test_learn_encoded_labels.py

@@ -0,0 +1,64 @@
+import matplotlib.pyplot as plt
+
+
+from rbm.model import Model
+from rbm.label import Label
+from rbm.entity import Entity, EntityParams
+from rbm.matrix import Mat, np
+from rbm.train import TrainingParams
+
+class LabelLearner(Model):
+	def __init__(self, name: str, work_dir: str = '.'):
+		super().__init__(name, work_dir)
+		self.unit1 = Entity((16, 16), EntityParams())
+
+	def forward(self, x: Mat):
+		x = self.unit1.forward(x)
+		return x
+
+	def backward(self, x: Mat):
+		x = self.unit1.reconstruct(x)
+		return x
+
+if __name__ == "__main__":
+	voc_size = 100
+	work_dir = "../../results"
+	prj_root = "/home/jens/work/repos/Rbm"
+
+	# Create Labeler
+	enc = Label(voc_size, 16, work_dir)
+
+	# Load
+	enc.fitter.load()
+
+	# Encode test labels
+	test_labels = np.array([1, 23, 99, 37, 55, 7, 31, 10, 19, 70])
+	encoded, _ = enc.encode(test_labels)
+
+	# Create model
+	model = LabelLearner("Label-Learner", work_dir)
+
+	# Init state
+	model.init(0.01)
+
+	# load state
+	model.load()
+
+	# Train
+	model.train(encoded, TrainingParams(learning_rate=0.01, momentum=0.9, do_rao_blackwell=True, num_epochs=10000))
+
+	# save state
+	model.save()
+
+	# Plot
+	fig, axes = plt.subplots(1, len(encoded), figsize=(12, 3))
+	for index, inp in enumerate(encoded):
+		img = np.reshape(inp, (4, 4))
+		axes[index].imshow(img)
+		axes[index].axis('off')
+		axes[index].set_title(f'{test_labels[index]}')
+
+	plt.show()
+
+	print("Test: [passed]")
+