Add documentation to set self.built = True in MyLayer.build() (#4315)

* Added documentation to set self.built = True in MyLayer.build()

* Update writing-your-own-keras-layers.md

docs/templates/layers/writing-your-own-keras-layers.md

@@ -4,7 +4,7 @@ For simple, stateless custom operations, you are probably better off using `laye
 
 Here is the skeleton of a Keras layer. There are only three methods you need to implement:
 
-- `build(input_shape)`: this is where you will define your weights. Trainable weights should be added to the list `self.trainable_weights`. Other attributes of note are: `self.non_trainable_weights` (list) and `self.updates` (list of update tuples (tensor, new_tensor)). For an example of how to use `non_trainable_weights` and `updates`, see the code for the `BatchNormalization` layer.
+- `build(input_shape)`: this is where you will define your weights. Trainable weights should be added to the list `self.trainable_weights`. Other attributes of note are: `self.non_trainable_weights` (list) and `self.updates` (list of update tuples (tensor, new_tensor)). For an example of how to use `non_trainable_weights` and `updates`, see the code for the `BatchNormalization` layer.  This method must set `self.built = True`, which can be done by calling `super([Layer], self).build()`.
 - `call(x)`: this is where the layer's logic lives. Unless you want your layer to support masking, you only have to care about the first argument passed to `call`: the input tensor.
 - `get_output_shape_for(input_shape)`: in case your layer modifies the shape of its input, you should specify here the shape transformation logic. This allows Keras to do automatic shape inference.
 
@@ -23,6 +23,7 @@ class MyLayer(Layer):
         initial_weight_value = np.random.random((input_dim, output_dim))
         self.W = K.variable(initial_weight_value)
         self.trainable_weights = [self.W]
+        super(MyLayer, self).build()  # be sure you call this somewhere!
 
     def call(self, x, mask=None):
         return K.dot(x, self.W)