diff --git a/monai/optimizers/utils.py b/monai/optimizers/utils.py
index 9c4bfcf6ee..c52ab07a04 100644
--- a/monai/optimizers/utils.py
+++ b/monai/optimizers/utils.py
@@ -34,6 +34,10 @@ def generate_param_groups(
         layer_matches: a list of callable functions to select or filter out network layer groups,
             for "select" type, the input will be the `network`, for "filter" type,
             the input will be every item of `network.named_parameters()`.
+            for "select", the parameters will be
+            `select_func(network).parameters()`.
+            for "filter", the parameters will be
+            `map(lambda x: x[1], filter(filter_func, network.named_parameters()))`
         match_types: a list of tags to identify the matching type corresponding to the `layer_matches` functions,
             can be "select" or "filter".
         lr_values: a list of LR values corresponding to the `layer_matches` functions.
@@ -48,7 +52,7 @@ def generate_param_groups(
         print(net.named_parameters())  # print out all the named parameters to filter out expected items
         params = generate_param_groups(
             network=net,
-            layer_matches=[lambda x: x.model[-1], lambda x: "conv.weight" in x],
+            layer_matches=[lambda x: x.model[0], lambda x: "2.0.conv" in x[0]],
             match_types=["select", "filter"],
             lr_values=[1e-2, 1e-3],
         )
@@ -71,7 +75,8 @@ def _select():
 
     def _get_filter(f):
         def _filter():
-            return filter(f, network.named_parameters())
+            # should eventually generate a list of network parameters
+            return map(lambda x: x[1], filter(f, network.named_parameters()))
 
         return _filter
 
diff --git a/tests/test_generate_param_groups.py b/tests/test_generate_param_groups.py
index 8ccb8b7977..ea1fad44f9 100644
--- a/tests/test_generate_param_groups.py
+++ b/tests/test_generate_param_groups.py
@@ -25,6 +25,7 @@
         "lr_values": [1],
     },
     (1, 100),
+    [5, 21],
 ]
 
 TEST_CASE_2 = [
@@ -34,6 +35,7 @@
         "lr_values": [1, 2, 3],
     },
     (1, 2, 3, 100),
+    [5, 16, 5, 0],
 ]
 
 TEST_CASE_3 = [
@@ -43,15 +45,17 @@
         "lr_values": [1],
     },
     (1, 100),
+    [2, 24],
 ]
 
 TEST_CASE_4 = [
     {
-        "layer_matches": [lambda x: x.model[-1], lambda x: "conv.weight" in x],
+        "layer_matches": [lambda x: x.model[0], lambda x: "2.0.conv" in x[0]],
         "match_types": ["select", "filter"],
         "lr_values": [1, 2],
     },
     (1, 2, 100),
+    [5, 4, 17],
 ]
 
 TEST_CASE_5 = [
@@ -62,12 +66,24 @@
         "include_others": False,
     },
     (1),
+    [5],
+]
+
+TEST_CASE_6 = [
+    {
+        "layer_matches": [lambda x: "weight" in x[0]],
+        "match_types": ["filter"],
+        "lr_values": [1],
+        "include_others": True,
+    },
+    (1),
+    [16, 10],
 ]
 
 
 class TestGenerateParamGroups(unittest.TestCase):
-    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5])
-    def test_lr_values(self, input_param, expected_values):
+    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])
+    def test_lr_values(self, input_param, expected_values, expected_groups):
         device = "cuda" if torch.cuda.is_available() else "cpu"
         net = Unet(
             dimensions=3,
@@ -85,7 +101,7 @@ def test_lr_values(self, input_param, expected_values):
             torch.testing.assert_allclose(param_group["lr"], value)
 
         n = [len(p["params"]) for p in params]
-        assert sum(n) == 26 or all(n), "should have either full model or non-empty subsets."
+        self.assertListEqual(n, expected_groups)
 
     def test_wrong(self):
         """overlapped"""