corrected version
diff --git a/docs/templates/layers/writing-your-own-keras-layers.md b/docs/templates/layers/writing-your-own-keras-layers.md
index f543b437ee6..b92406f8f0e 100644
--- a/docs/templates/layers/writing-your-own-keras-layers.md
+++ b/docs/templates/layers/writing-your-own-keras-layers.md
@@ -2,7 +2,7 @@
 
 For simple, stateless custom operations, you are probably better off using `layers.core.Lambda` layers. But for any custom operation that has trainable weights, you should implement your own layer.
 
-Here is the skeleton of a Keras layer, **as of Keras 1.1.3** (if you have an older version, please upgrade). There are only three methods you need to implement:
+Here is the skeleton of a Keras layer, **as of Keras 1.2.0** (if you have an older version, please upgrade). There are only three methods you need to implement:
 
 - `build(input_shape)`: this is where you will define your weights. 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.
@@ -32,4 +32,4 @@ class MyLayer(Layer):
         return (input_shape[0], self.output_dim)
 ```
 
-The existing Keras layers provide ample examples of how to implement almost anything. Never hesitate to read the source code!
+The existing Keras layers provide examples of how to implement almost anything. Never hesitate to read the source code!

the version tag should be 1.2.0, afaik 1.1.3 doesn't exist
Btw. https://keras.io/layers/writing-your-own-keras-layers/ shows an older version of this page.