diff --git a/vignettes/examples/eager_dcgan.R b/vignettes/examples/eager_dcgan.R
new file mode 100644
index 000000000..b919982a6
--- /dev/null
+++ b/vignettes/examples/eager_dcgan.R
@@ -0,0 +1,252 @@
+#' This is the companion code to the post
+#' "Generating digits with Keras and TensorFlow eager execution"
+#' on the TensorFlow for R blog.
+#'
+#' https://blogs.rstudio.com/tensorflow/posts/2018-08-26-eager-dcgan/
+
+
+# Setup -------------------------------------------------------------------
+
+# Important: Make sure you are using the latest versions of reticulate, keras, tensorflow and tfdatasets from github.
+# devtools::install_github(
+#   c(
+#     "rstudio/keras",
+#     "rstudio/tensorflow",
+#     "rstudio/tfdatasets",
+#     "rstudio/reticulate"
+#   )
+# )
+
+library(keras)
+use_implementation("tensorflow")
+library(tensorflow)
+tfe_enable_eager_execution(device_policy = "silent")
+
+library(tfdatasets)
+tf$set_random_seed(7777)
+
+mnist <- dataset_mnist()
+c(train_images, train_labels) %<-% mnist$train
+
+train_images <- train_images %>%
+  k_expand_dims() %>%
+  k_cast(dtype = "float32")
+
+train_images <- (train_images - 127.5) / 127.5
+
+buffer_size <- 60000
+batch_size <- 256L
+batches_per_epoch <- (buffer_size / batch_size) %>% round()
+
+train_dataset <- tensor_slices_dataset(train_images) %>%
+  dataset_shuffle(buffer_size) %>%
+  dataset_batch(batch_size)
+
+generator <-
+  function(name = NULL) {
+    keras_model_custom(name = name, function(self) {
+      self$fc1 <- layer_dense(units = 7 * 7 * 64, use_bias = FALSE)
+      self$batchnorm1 <- layer_batch_normalization()
+      self$leaky_relu1 <- layer_activation_leaky_relu()
+      
+      self$conv1 <-
+        layer_conv_2d_transpose(
+          filters = 64,
+          kernel_size = c(5, 5),
+          strides = c(1, 1),
+          padding = "same",
+          use_bias = FALSE
+        )
+      self$batchnorm2 <- layer_batch_normalization()
+      self$leaky_relu2 <- layer_activation_leaky_relu()
+      
+      self$conv2 <-
+        layer_conv_2d_transpose(
+          filters = 32,
+          kernel_size = c(5, 5),
+          strides = c(2, 2),
+          padding = "same",
+          use_bias = FALSE
+        )
+      self$batchnorm3 <- layer_batch_normalization()
+      self$leaky_relu3 <- layer_activation_leaky_relu()
+      
+      self$conv3 <-
+        layer_conv_2d_transpose(
+          filters = 1,
+          kernel_size = c(5, 5),
+          strides = c(2, 2),
+          padding = "same",
+          use_bias = FALSE,
+          activation = "tanh"
+        )
+      
+      function(inputs,
+               mask = NULL,
+               training = TRUE) {
+        self$fc1(inputs) %>%
+          self$batchnorm1(training = training) %>%
+          self$leaky_relu1() %>%
+          k_reshape(shape = c(-1, 7, 7, 64)) %>%
+          
+          self$conv1() %>%
+          self$batchnorm2(training = training) %>%
+          self$leaky_relu2() %>%
+          
+          self$conv2() %>%
+          self$batchnorm3(training = training) %>%
+          self$leaky_relu3() %>%
+          
+          self$conv3()
+      }
+    })
+  }
+
+discriminator <-
+  function(name = NULL) {
+    keras_model_custom(name = name, function(self) {
+      self$conv1 <- layer_conv_2d(
+        filters = 64,
+        kernel_size = c(5, 5),
+        strides = c(2, 2),
+        padding = "same"
+      )
+      self$leaky_relu1 <- layer_activation_leaky_relu()
+      self$dropout <- layer_dropout(rate = 0.3)
+      
+      self$conv2 <-
+        layer_conv_2d(
+          filters = 128,
+          kernel_size = c(5, 5),
+          strides = c(2, 2),
+          padding = "same"
+        )
+      self$leaky_relu2 <- layer_activation_leaky_relu()
+      self$flatten <- layer_flatten()
+      # no sigmoid because using tf$losses$sigmoid_cross_entropy
+      self$fc1 <- layer_dense(units = 1)
+      
+      function(inputs,
+               mask = NULL,
+               training = TRUE) {
+        inputs %>% self$conv1() %>%
+          self$leaky_relu1() %>%
+          self$dropout(training = training) %>%
+          self$conv2() %>%
+          self$leaky_relu2() %>%
+          self$flatten() %>%
+          self$fc1()
+        
+      }
+    })
+  }
+
+generator <- generator()
+discriminator <- discriminator()
+
+# https://www.tensorflow.org/api_docs/python/tf/contrib/eager/defun
+generator$call = tf$contrib$eager$defun(generator$call)
+discriminator$call = tf$contrib$eager$defun(discriminator$call)
+
+discriminator_loss <- function(real_output, generated_output) {
+  real_loss <-
+    tf$losses$sigmoid_cross_entropy(multi_class_labels = k_ones_like(real_output),
+                                    logits = real_output)
+  generated_loss <-
+    tf$losses$sigmoid_cross_entropy(multi_class_labels = k_zeros_like(generated_output),
+                                    logits = generated_output)
+  real_loss + generated_loss
+}
+
+generator_loss <- function(generated_output) {
+  tf$losses$sigmoid_cross_entropy(tf$ones_like(generated_output), generated_output)
+}
+
+discriminator_optimizer <- tf$train$AdamOptimizer(1e-4)
+generator_optimizer <- tf$train$AdamOptimizer(1e-4)
+
+num_epochs <- 150
+noise_dim <- 100L
+num_examples_to_generate <- 25L
+
+random_vector_for_generation <-
+  k_random_normal(c(num_examples_to_generate,
+                    noise_dim))
+
+generate_and_save_images <- function(model, epoch, test_input) {
+  predictions <- model(test_input, training = FALSE)
+  png(paste0("images_epoch_", epoch, ".png"))
+  par(mfcol = c(5, 5))
+  par(mar = c(0.5, 0.5, 0.5, 0.5),
+      xaxs = 'i',
+      yaxs = 'i')
+  for (i in 1:25) {
+    img <- predictions[i, , , 1]
+    img <- t(apply(img, 2, rev))
+    image(
+      1:28,
+      1:28,
+      img * 127.5 + 127.5,
+      col = gray((0:255) / 255),
+      xaxt = 'n',
+      yaxt = 'n'
+    )
+  }
+  dev.off()
+}
+
+train <- function(dataset, epochs, noise_dim) {
+  for (epoch in seq_len(num_epochs)) {
+    start <- Sys.time()
+    total_loss_gen <- 0
+    total_loss_disc <- 0
+    iter <- make_iterator_one_shot(train_dataset)
+    
+    until_out_of_range({
+      batch <- iterator_get_next(iter)
+      noise <- k_random_normal(c(batch_size, noise_dim))
+      with(tf$GradientTape() %as% gen_tape, {
+        with(tf$GradientTape() %as% disc_tape, {
+          generated_images <- generator(noise)
+          disc_real_output <- discriminator(batch, training = TRUE)
+          disc_generated_output <-
+            discriminator(generated_images, training = TRUE)
+          gen_loss <- generator_loss(disc_generated_output)
+          disc_loss <-
+            discriminator_loss(disc_real_output, disc_generated_output)
+        })
+      })
+      
+      gradients_of_generator <-
+        gen_tape$gradient(gen_loss, generator$variables)
+      gradients_of_discriminator <-
+        disc_tape$gradient(disc_loss, discriminator$variables)
+      
+      generator_optimizer$apply_gradients(purrr::transpose(list(
+        gradients_of_generator, generator$variables
+      )))
+      discriminator_optimizer$apply_gradients(purrr::transpose(
+        list(gradients_of_discriminator, discriminator$variables)
+      ))
+      
+      total_loss_gen <- total_loss_gen + gen_loss
+      total_loss_disc <- total_loss_disc + disc_loss
+      
+    })
+    
+    cat("Time for epoch ", epoch, ": ", Sys.time() - start, "\n")
+    cat("Generator loss: ",
+        total_loss_gen$numpy() / batches_per_epoch,
+        "\n")
+    cat("Discriminator loss: ",
+        total_loss_disc$numpy() / batches_per_epoch,
+        "\n\n")
+    if (epoch %% 10 == 0)
+      generate_and_save_images(generator,
+                               epoch,
+                               random_vector_for_generation)
+    
+  }
+}
+
+train(train_dataset, num_epochs, noise_dim)
diff --git a/vignettes/examples/eager_dcgan.Rmd b/vignettes/examples/eager_dcgan.Rmd
new file mode 100644
index 000000000..70c8d418e
--- /dev/null
+++ b/vignettes/examples/eager_dcgan.Rmd
@@ -0,0 +1,17 @@
+---
+title: eager_dcgan
+type: docs
+repo: https://github.com/rstudio/keras
+menu:
+  main:
+    parent: keras-examples
+---
+
+<div class="source-ref">
+<span class="caption">Source: </span>`r sprintf("https://github.com/rstudio/keras/blob/master/vignettes/examples/%s.R", rmarkdown::metadata$title)`
+</div>
+
+```{r, echo = FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+knitr::spin_child(paste0(rmarkdown::metadata$title, ".R"))
+```
diff --git a/vignettes/examples/index.Rmd b/vignettes/examples/index.Rmd
index 280a6a569..bf0f02fc3 100644
--- a/vignettes/examples/index.Rmd
+++ b/vignettes/examples/index.Rmd
@@ -11,6 +11,7 @@ title: "Keras Examples"
 | [cifar10_densenet](cifar10_densenet.html) | Trains a DenseNet-40-12 on the CIFAR10 small images dataset. |
 | [conv_lstm](conv_lstm.html) | Demonstrates the use of a convolutional LSTM network. |
 | [deep_dream](deep_dream.html) | Deep Dreams in Keras. |
+| [eager_dcgan](eager_dcgan.html) | Generating digits with generative adversarial networks and eager execution. |
 | [imdb_bidirectional_lstm](imdb_bidirectional_lstm.html) | Trains a Bidirectional LSTM on the IMDB sentiment classification task. |
 | [imdb_cnn](imdb_cnn.html) | Demonstrates the use of Convolution1D for text classification. |
 | [imdb_cnn_lstm](imdb_cnn_lstm.html) | Trains a convolutional stack followed by a recurrent stack network on the IMDB sentiment classification task. |