diff --git a/R/model_edges.R b/R/model_edges.R
index b44420e..b88b0de 100644
--- a/R/model_edges.R
+++ b/R/model_edges.R
@@ -27,10 +27,14 @@ inbound_nodes <- function(model){
   inbound <- map(
     model_layers,
     function(x){
-      if (length(x$inbound_nodes))
-        x$inbound_nodes[[1]] %>%
-        map_chr(c(1, 1))
-      else NA
+      if (length(x$inbound_nodes) == 1) {
+        x$inbound_nodes[[1]] %>% map_chr(c(1, 1))
+      } else if(length(x$inbound_nodes) > 1) {
+        # needed for shared layers
+        x$inbound_nodes %>% map_chr(c(1, 1))
+      } else {
+        NA
+      }
     }
   )
   names(inbound) <- map(model_layers, "name")
diff --git a/inst/examples/example_shared_layer.R b/inst/examples/example_shared_layer.R
new file mode 100644
index 0000000..ff88ea8
--- /dev/null
+++ b/inst/examples/example_shared_layer.R
@@ -0,0 +1,32 @@
+## ---- shared ----
+
+require(keras)
+
+# Example of a network with a shared layers
+# from: https://keras.rstudio.com/articles/functional_api.html
+
+tweet_a <- layer_input(shape = c(280, 256))
+tweet_b <- layer_input(shape = c(280, 256))
+
+# This layer can take as input a matrix and will return a vector of size 64
+shared_lstm <- layer_lstm(units = 64)
+
+# When we reuse the same layer instance multiple times, the weights of the layer are also
+# being reused (it is effectively *the same* layer)
+encoded_a <- tweet_a %>% shared_lstm
+encoded_b <- tweet_b %>% shared_lstm
+
+# We can then concatenate the two vectors and add a logistic regression on top
+predictions <- layer_concatenate(c(encoded_a, encoded_b), axis=-1) %>%
+  layer_dense(units = 1, activation = 'sigmoid')
+
+# We define a trainable model linking the tweet inputs to the predictions
+model <- keras_model(inputs = c(tweet_a, tweet_b), outputs = predictions)
+
+model %>% compile(
+  optimizer = 'rmsprop',
+  loss = 'binary_crossentropy',
+  metrics = c('accuracy')
+)
+
+plot_model(model)
diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
index af18b84..0427e00 100644
--- a/vignettes/introduction.Rmd
+++ b/vignettes/introduction.Rmd
@@ -39,6 +39,12 @@ knitr::read_chunk(
 )
 ```
 
+```{r cache=FALSE, include=FALSE}
+knitr::read_chunk(
+  here::here("inst/examples/example_shared_layer.R")
+)
+```
+
 ### Sequential model
 
 Simple sequential model
@@ -51,6 +57,10 @@ Simple sequential model
 ```{r network, fig.height=6}
 ```
 
+Shared layers
+
+```{r shared, fig.height=6}
+```
 
 ## Famous architectures