- added training params as list for model.train()

- added gaussian sample
- introduced layout concept
- updated README

src/tests/test_deep_norbs.py

@@ -0,0 +1,65 @@
+import os
+import matplotlib.pyplot as plt
+
+from rbm.model import Model
+from rbm.entity import Entity, EntityParams
+from rbm.matrix import Mat, np, read_armadillo
+from rbm.train import TrainingParams
+
+class TestModel(Model):
+	def __init__(self, name: str, work_dir: str = '.'):
+		super().__init__(name, work_dir)
+		self.unit1 = Entity((96*96, 16), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=True))
+		self.unit2 = Entity((16, 16), EntityParams())
+
+	def forward(self, x: Mat):
+		x = self.unit1.forward(x)
+		x = self.unit2.forward(x)
+		return x
+
+	def backward(self, x: Mat):
+		x = self.unit2.reconstruct(x)
+		x = self.unit1.reconstruct(x)
+		return x
+
+if __name__ == "__main__":
+	work_dir = "results"
+	prj_name = "deep_norb_small_16h_v2"
+	prj_root = "/home/jens/work/repos/Rbm"
+
+	# Create model
+	model = TestModel(prj_name, "results")
+
+	# Init state
+	model.init(0.01)
+
+	# load state
+	model.load()
+
+	# Load train data
+	train_batch = read_armadillo(os.path.join(prj_root, f"norb_small_16h_v2.training.dat"))
+
+	# Load test data
+	test_batch = read_armadillo(os.path.join(prj_root, f"norb_small_16h_v2.test.dat"))
+
+	# Train
+	model.train(train_batch,[
+		TrainingParams(learning_rate=0.00001, momentum=0.9, do_rao_blackwell=True, num_epochs=1000, num_gibbs_samples=3),
+		TrainingParams(learning_rate=0.01, momentum=0.9, do_rao_blackwell=True, num_epochs=1000, num_gibbs_samples=1)
+		])
+
+	# save state
+	model.save()
+
+	fig, axes = plt.subplots(1, len(test_batch), figsize=(12, 3))
+	for index, inp in enumerate(test_batch):
+		out_normalized = model.backward(model.forward(inp))
+		img = 2*(out_normalized + 0.5)
+		img = np.reshape(img, (96, 96))
+		axes[index].imshow(img)
+		axes[index].axis('off')
+
+	plt.show()
+
+
+