diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1408ee4f1..b7cf52042 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -70,6 +70,7 @@ To release a new version, please update the changelog as followed:
 ## [Unreleased]
 
 ### Added
+- Support nested layer customization (#PR 1015)
 
 ### Changed
 
@@ -83,7 +84,7 @@ To release a new version, please update the changelog as followed:
 
 ### Fixed
 - Fix `tf.models.Model._construct_graph` for list of outputs, e.g. STN case (PR #1010)
-
+- Enable better `in_channels` exception raise. (pR #1015)
 ### Removed
 
 ### Security
@@ -91,7 +92,7 @@ To release a new version, please update the changelog as followed:
 ### Contributors
 
 - @zsdonghao
-- @ChrisWu1997: #1010
+- @ChrisWu1997: #1010 #1015
 
 ## [2.1.0]
 
diff --git a/examples/database/task_script.py b/examples/database/task_script.py
index 2076847dd..3d77102b1 100644
--- a/examples/database/task_script.py
+++ b/examples/database/task_script.py
@@ -12,6 +12,7 @@
 # load dataset from database
 X_train, y_train, X_val, y_val, X_test, y_test = db.find_top_dataset('mnist')
 
+
 # define the network
 def mlp():
     ni = tl.layers.Input([None, 784], name='input')
@@ -24,15 +25,18 @@ def mlp():
     M = tl.models.Model(inputs=ni, outputs=net)
     return M
 
+
 network = mlp()
 
 # cost and accuracy
 cost = tl.cost.cross_entropy
 
+
 def acc(y, y_):
     correct_prediction = tf.equal(tf.argmax(y, 1), tf.convert_to_tensor(y_, tf.int64))
     return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
+
 # define the optimizer
 train_op = tf.optimizers.Adam(learning_rate=0.0001)
 
@@ -43,8 +47,17 @@ def acc(y, y_):
 # )
 
 tl.utils.fit(
-    network, train_op=tf.optimizers.Adam(learning_rate=0.0001), cost=tl.cost.cross_entropy, X_train=X_train,
-    y_train=y_train, acc=acc, batch_size=256, n_epoch=20, X_val=X_val, y_val=y_val, eval_train=False,
+    network,
+    train_op=tf.optimizers.Adam(learning_rate=0.0001),
+    cost=tl.cost.cross_entropy,
+    X_train=X_train,
+    y_train=y_train,
+    acc=acc,
+    batch_size=256,
+    n_epoch=20,
+    X_val=X_val,
+    y_val=y_val,
+    eval_train=False,
 )
 
 # evaluation and save result that match the result_key
@@ -55,5 +68,3 @@ def acc(y, y_):
 db.save_model(network, model_name='mlp', name=str(n_units1) + '-' + str(n_units2), test_accuracy=test_accuracy)
 # in other script, you can load the model as follow
 # net = db.find_model(sess=sess, model_name=str(n_units1)+'-'+str(n_units2)
-
-tf.python.keras.layers.BatchNormalization
\ No newline at end of file
diff --git a/tensorlayer/layers/core.py b/tensorlayer/layers/core.py
index 7049e216c..6fcbd4aa8 100644
--- a/tensorlayer/layers/core.py
+++ b/tensorlayer/layers/core.py
@@ -127,8 +127,11 @@ def __init__(self, name=None, act=None, *args, **kwargs):
 
         # Layer weight state
         self._all_weights = None
-        self._trainable_weights = None
-        self._nontrainable_weights = None
+        self._trainable_weights = []
+        self._nontrainable_weights = []
+
+        # nested layers
+        self._layers = None
 
         # Layer training state
         self.is_train = True
@@ -179,20 +182,18 @@ def all_weights(self):
         if self._all_weights is not None and len(self._all_weights) > 0:
             pass
         else:
-            self._all_weights = list()
-            if self._trainable_weights is not None:
-                self._all_weights.extend(self._trainable_weights)
-            if self._nontrainable_weights is not None:
-                self._all_weights.extend(self._nontrainable_weights)
+            self._all_weights = self.trainable_weights + self.nontrainable_weights
         return self._all_weights
 
     @property
     def trainable_weights(self):
-        return self._trainable_weights
+        nested = self._collect_sublayers_attr('trainable_weights')
+        return self._trainable_weights + nested
 
     @property
     def nontrainable_weights(self):
-        return self._nontrainable_weights
+        nested = self._collect_sublayers_attr('nontrainable_weights')
+        return self._nontrainable_weights + nested
 
     @property
     def weights(self):
@@ -200,6 +201,21 @@ def weights(self):
             "no property .weights exists, do you mean .all_weights, .trainable_weights, or .nontrainable_weights ?"
         )
 
+    def _collect_sublayers_attr(self, attr):
+        if attr not in ['trainable_weights', 'nontrainable_weights']:
+            raise ValueError(
+                "Only support to collect some certain attributes of nested layers,"
+                "e.g. 'trainable_weights', 'nontrainable_weights', but got {}".format(attr)
+            )
+        if self._layers is None:
+            return []
+        nested = []
+        for layer in self._layers:
+            value = getattr(layer, attr)
+            if value is not None:
+                nested.extend(value)
+        return nested
+
     def __call__(self, inputs, *args, **kwargs):
         """
         (1) Build the Layer if necessary.
@@ -326,6 +342,20 @@ def __setitem__(self, key, item):
     def __delitem__(self, key):
         raise TypeError("The Layer API does not allow to use the method: `__delitem__`")
 
+    def __setattr__(self, key, value):
+        if isinstance(value, Layer):
+            value._nodes_fixed = True
+            if self._layers is None:
+                self._layers = []
+            self._layers.append(value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, name):
+        value = getattr(self, name, None)
+        if isinstance(value, Layer):
+            self._layers.remove(value)
+        super().__delattr__(name)
+
     @protected_method
     def get_args(self):
         init_args = {"layer_type": "normal"}
diff --git a/tensorlayer/models/core.py b/tensorlayer/models/core.py
index 52d8dc083..2d62d2b56 100644
--- a/tensorlayer/models/core.py
+++ b/tensorlayer/models/core.py
@@ -591,6 +591,15 @@ def _fix_nodes_for_layers(self):
             layer._fix_nodes_for_layers()
         self._nodes_fixed = True
 
+    def __setattr__(self, key, value):
+        if isinstance(value, Layer):
+            if value._built is False:
+                raise AttributeError(
+                    "The registered layer `{}` should be built in advance. "
+                    "Do you forget to pass the keyword argument 'in_channels'? ".format(value.name)
+                )
+        super().__setattr__(key, value)
+
     def __repr__(self):
         # tmpstr = self.__class__.__name__ + '(\n'
         tmpstr = self.name + '(\n'
diff --git a/tests/layers/test_layers_core_nested.py b/tests/layers/test_layers_core_nested.py
new file mode 100644
index 000000000..e44c12f3a
--- /dev/null
+++ b/tests/layers/test_layers_core_nested.py
@@ -0,0 +1,123 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-\
+import os
+import unittest
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import tensorflow as tf
+import tensorlayer as tl
+import numpy as np
+
+from tests.utils import CustomTestCase
+
+
+class Layer_nested(CustomTestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        print("##### begin testing nested layer #####")
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+        # tf.reset_default_graph()
+
+    def test_nested_layer_with_inchannels(cls):
+
+        class MyLayer(tl.layers.Layer):
+
+            def __init__(self, name=None):
+                super(MyLayer, self).__init__(name=name)
+                self.input_layer = tl.layers.Dense(in_channels=50, n_units=20)
+                self.build(None)
+                self._built = True
+
+            def build(self, inputs_shape=None):
+                self.W = self._get_weights('weights', shape=(20, 10))
+
+            def forward(self, inputs):
+                inputs = self.input_layer(inputs)
+                output = tf.matmul(inputs, self.W)
+                return output
+
+        class model(tl.models.Model):
+
+            def __init__(self, name=None):
+                super(model, self).__init__(name=name)
+                self.layer = MyLayer()
+
+            def forward(self, inputs):
+                return self.layer(inputs)
+
+        input = tf.random.normal(shape=(100, 50))
+        model_dynamic = model()
+        model_dynamic.train()
+        cls.assertEqual(model_dynamic(input).shape, (100, 10))
+        cls.assertEqual(len(model_dynamic.all_weights), 3)
+        cls.assertEqual(len(model_dynamic.trainable_weights), 3)
+        model_dynamic.layer.input_layer.b.assign_add(tf.ones((20, )))
+        cls.assertEqual(np.sum(model_dynamic.all_weights[-1].numpy() - tf.ones(20, ).numpy()), 0)
+
+        ni = tl.layers.Input(shape=(100, 50))
+        nn = MyLayer(name='mylayer1')(ni)
+        model_static = tl.models.Model(inputs=ni, outputs=nn)
+        model_static.eval()
+        cls.assertEqual(model_static(input).shape, (100, 10))
+        cls.assertEqual(len(model_static.all_weights), 3)
+        cls.assertEqual(len(model_static.trainable_weights), 3)
+        model_static.get_layer('mylayer1').input_layer.b.assign_add(tf.ones((20, )))
+        cls.assertEqual(np.sum(model_static.all_weights[-1].numpy() - tf.ones(20, ).numpy()), 0)
+
+    def test_nested_layer_without_inchannels(cls):
+
+        class MyLayer(tl.layers.Layer):
+
+            def __init__(self, name=None):
+                super(MyLayer, self).__init__(name=name)
+                self.input_layer = tl.layers.Dense(n_units=20)  # no need for in_channels here
+                self.build(None)
+                self._built = True
+
+            def build(self, inputs_shape=None):
+                self.W = self._get_weights('weights', shape=(20, 10))
+
+            def forward(self, inputs):
+                inputs = self.input_layer(inputs)
+                output = tf.matmul(inputs, self.W)
+                return output
+
+        class model(tl.models.Model):
+
+            def __init__(self, name=None):
+                super(model, self).__init__(name=name)
+                self.layer = MyLayer()
+
+            def forward(self, inputs):
+                return self.layer(inputs)
+
+        input = tf.random.normal(shape=(100, 50))
+        model_dynamic = model()
+        model_dynamic.train()
+        cls.assertEqual(model_dynamic(input).shape, (100, 10))
+        cls.assertEqual(len(model_dynamic.all_weights), 3)
+        cls.assertEqual(len(model_dynamic.trainable_weights), 3)
+        model_dynamic.layer.input_layer.b.assign_add(tf.ones((20, )))
+        cls.assertEqual(np.sum(model_dynamic.all_weights[-1].numpy() - tf.ones(20, ).numpy()), 0)
+
+        ni = tl.layers.Input(shape=(100, 50))
+        nn = MyLayer(name='mylayer2')(ni)
+        model_static = tl.models.Model(inputs=ni, outputs=nn)
+        model_static.eval()
+        cls.assertEqual(model_static(input).shape, (100, 10))
+        cls.assertEqual(len(model_static.all_weights), 3)
+        cls.assertEqual(len(model_static.trainable_weights), 3)
+        model_static.get_layer('mylayer2').input_layer.b.assign_add(tf.ones((20, )))
+        cls.assertEqual(np.sum(model_static.all_weights[-1].numpy() - tf.ones(20, ).numpy()), 0)
+
+
+if __name__ == '__main__':
+
+    tl.logging.set_verbosity(tl.logging.DEBUG)
+
+    unittest.main()
diff --git a/tests/models/test_model_core.py b/tests/models/test_model_core.py
index 28e57b0f2..3db470f9d 100644
--- a/tests/models/test_model_core.py
+++ b/tests/models/test_model_core.py
@@ -401,6 +401,25 @@ def test_model_weights_copy(self):
         new_len = len(model_weights)
         self.assertEqual(new_len - 1, ori_len)
 
+    def test_inchannels_exception(self):
+        print('-' * 20, 'test_inchannels_exception', '-' * 20)
+
+        class my_model(Model):
+
+            def __init__(self):
+                super(my_model, self).__init__()
+                self.dense = Dense(64)
+                self.vgg = tl.models.vgg16()
+
+            def forward(self, x):
+                return x
+
+        try:
+            M = my_model()
+        except Exception as e:
+            self.assertIsInstance(e, AttributeError)
+            print(e)
+
 
 if __name__ == '__main__':