improved tests

src/tests/test_learn_encoded_labels.py

@@ -59,12 +59,22 @@ if __name__ == "__main__":
 	model.save()
 
 	# Plot
-	fig, axes = plt.subplots(1, len(encoded), figsize=(12, 3))
+	cmap = 'Grays'
+	fig, axes = plt.subplots(3, len(encoded), figsize=(12, 3))
 	for index, inp in enumerate(encoded):
-		img = np.reshape(inp, (label_w, label_h))
-		axes[index].imshow(img)
-		axes[index].axis('off')
-		axes[index].set_title(f'{test_labels[index]}')
+		hidden = model.forward(inp)
+		recon = model.backward(hidden)
+		img_hidden = np.reshape(hidden, (4, 4))
+		img_recon = np.reshape(recon, (label_w, label_h))
+		axes[0][index].imshow(img_hidden, cmap=cmap)
+		axes[0][index].axis('off')
+		axes[0][index].set_title(f'{test_labels[index]}')
+		axes[1][index].imshow(img_recon, cmap=cmap)
+		axes[1][index].axis('off')
+		axes[1][index].set_title(f'{test_labels[index]}')
+		axes[2][index].imshow(np.reshape(inp, (label_w, label_h)), cmap=cmap)
+		axes[2][index].axis('off')
+		axes[2][index].set_title(f'{test_labels[index]}')
 
 	plt.show()
 

src/tests/test_norbs.py

@@ -8,7 +8,7 @@ from rbm.matrix import Mat, np, read_armadillo
 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=False), TrainingParams(learning_rate=0.000001, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
+		self.unit1 = Entity((96*96, 16), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False), TrainingParams(learning_rate=0.000001, momentum=0.9, num_epochs=1000))
 
 	def forward(self, x: Mat):
 		x = self.unit1.forward(x)
@@ -34,9 +34,10 @@ if __name__ == "__main__":
 
 	# Load train data
 	train_batch = read_armadillo(os.path.join(prj_root, f"{prj_name}.training.dat"))
+	test_batch = train_batch
 
 	# Load test data
-	test_batch = read_armadillo(os.path.join(prj_root, f"{prj_name}.test.dat"))
+	#test_batch = read_armadillo(os.path.join(prj_root, f"{prj_name}.test.dat"))
 
 	# Train
 	model.train(train_batch)