diff --git a/tests/test_ElmClassifier.py b/tests/test_ElmClassifier.py
index 50d6e1d..5f2f98f 100644
--- a/tests/test_ElmClassifier.py
+++ b/tests/test_ElmClassifier.py
@@ -12,7 +12,7 @@ def test_ElmClassifier_class():
     X = np.random.rand(100, 6)
     y = np.random.randint(0, 2, size=100)
 
-    model = ElmClassifier(hidden_size=10, act_name="elu", seed=42)
+    model = ElmClassifier(layer_sizes=(10, ), act_name="elu", seed=42)
     model.fit(X, y)
     pred = model.predict(X)
     assert ElmClassifier.SUPPORTED_CLS_METRICS == model.SUPPORTED_CLS_METRICS
diff --git a/tests/test_ElmRegressor.py b/tests/test_ElmRegressor.py
index 952cf0d..7ad9cce 100644
--- a/tests/test_ElmRegressor.py
+++ b/tests/test_ElmRegressor.py
@@ -13,7 +13,7 @@ def test_ElmRegressor_class():
     noise = np.random.normal(loc=0.0, scale=0.1, size=(100, 5))
     y = 2 * X + 1 + noise
 
-    model = ElmRegressor(hidden_size=10, act_name="elu", seed=42)
+    model = ElmRegressor(layer_sizes=(10, ), act_name="elu", seed=42)
     model.fit(X, y)
 
     pred = model.predict(X)
diff --git a/tests/test_MhaElmClassifier.py b/tests/test_MhaElmClassifier.py
index eaa42c1..e79bc0a 100644
--- a/tests/test_MhaElmClassifier.py
+++ b/tests/test_MhaElmClassifier.py
@@ -13,8 +13,8 @@ def test_MhaElmClassifier_class():
     y = np.random.randint(0, 2, size=100)
 
     opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
-    model = MhaElmClassifier(hidden_size=10, act_name="elu", obj_name="AS", optimizer="BaseGA",
-                             optimizer_paras=opt_paras, verbose=False, seed=42)
+    model = MhaElmClassifier(layer_sizes=(10, ), act_name="elu", obj_name="AS", optim="BaseGA",
+                             optim_paras=opt_paras, verbose=False, seed=42)
     model.fit(X, y)
     pred = model.predict(X)
     assert MhaElmClassifier.SUPPORTED_CLS_OBJECTIVES == model.SUPPORTED_CLS_OBJECTIVES
diff --git a/tests/test_MhaElmRegressor.py b/tests/test_MhaElmRegressor.py
index e536286..14d3d0d 100644
--- a/tests/test_MhaElmRegressor.py
+++ b/tests/test_MhaElmRegressor.py
@@ -14,8 +14,8 @@ def test_MhaElmRegressor_class():
     y = 2 * X + 1 + noise
 
     opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
-    model = MhaElmRegressor(hidden_size=10, act_name="elu", obj_name="RMSE", optimizer="BaseGA",
-                            optimizer_paras=opt_paras, verbose=False, seed=42)
+    model = MhaElmRegressor(layer_sizes=(10, ), act_name="elu", obj_name="RMSE", optim="BaseGA",
+                            optim_paras=opt_paras, verbose=False, seed=42)
     model.fit(X, y)
 
     pred = model.predict(X)