diff --git a/src/tests/test_linear.py b/src/tests/test_linear.py
new file mode 100644
index 0000000..22fd60f
--- /dev/null
+++ b/src/tests/test_linear.py
@@ -0,0 +1,46 @@
+import os.path
+
+from rbm.layer import Layer
+from rbm.status import Status
+from rbm.train import train
+from rbm.matrix import Mat, np
+from rbm.entity import EntityParams, TrainingParams
+
+WORK_DIR = "../../results"
+USE_OPTIMIZER = True
+
+def linear():
+	# Create params
+	entity_params = EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False)
+	training_params = TrainingParams(learning_rate=0.0001, do_batch_sample=True, num_epochs=10000, momentum=0.9)
+	entity_params.do_rao_blackwell = False
+	entity_params.num_gibbs_samples = 1
+
+	# Create layer
+	layer = Layer("Layer_0", (3, 1, 0, 32), entity_params, training_params)
+
+	# Init weights
+	layer.init(0.01)
+
+	# Load weights (if exists)
+	layer.load(os.path.join(WORK_DIR, "linear_layer0_state.npz"))
+
+	# Prepare training data
+	training_batch = Mat([[0.5,0.5,1], [0.1,0.9,1.0], [0.2,0.5,0.7], [0.9,0.1,1], [0.5,0.2,0.7]], dtype=np.float64)
+
+	# Train layer
+	train(layer.entity, training_batch, Status())
+
+	# Save weights
+	layer.save(os.path.join(WORK_DIR, "linear_layer0_state.npz"))
+
+	# Test with test data
+	test_batch = training_batch
+	for pattern in test_batch:
+		h = layer.entity.forward(pattern)
+		v = layer.entity.reconstruct(h)
+		print(f"P{pattern} : {v}")
+
+if __name__ == "__main__":
+	linear()
+	print("Test: [passed]")
diff --git a/src/tests/test_xor.py b/src/tests/test_xor.py
index 8a8adc2..2ed35bb 100644
--- a/src/tests/test_xor.py
+++ b/src/tests/test_xor.py
@@ -2,9 +2,9 @@ import os.path
 
 from rbm.layer import Layer
 from rbm.status import Status
-from rbm.train import train, TrainingParams
+from rbm.train import train
 from rbm.matrix import Mat, np
-from rbm.entity import EntityParams
+from rbm.entity import EntityParams, TrainingParams
 
 WORK_DIR = "../../results"
 USE_OPTIMIZER = True
@@ -29,7 +29,7 @@ def xor():
 	training_batch = Mat([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
 
 	# Train layer
-	train(layer.entity, training_batch, training_params, Status())
+	train(layer.entity, training_batch, Status())
 
 	# Save weights
 	layer.save(os.path.join(WORK_DIR, "xor_layer0_state.npz"))