Now, we can build our first sequential keras model and train it:

1. Let's start by defining a sequential model:

model_sequential <- keras_model_sequential()

2. We need to add layers to the model we defined in the preceding code block:

model_sequential %>% 
layer_dense(units = 16,batch_size = ,input_shape = c(784)) %>% 
layer_activation('relu') %>% 
layer_dense(units = 1)

3. After adding the layers to our model, we need to compile it:

model_sequential %>% compile(
 loss = "mse",
 optimizer = optimizer_sgd(),
 metrics = list("mean_absolute_error")
)

4. Now, let's visualize the summary of the model we created:

model_sequential %>% summary()

The summary of the model is as follows:

5. Now, let's train the model and store the training stats in a variable in order to plot the model's metrics:

history <- model_sequential %>% fit(
 x_data, 
 y_data,
 epochs = 30,
 batch_size = 128, 
 validation_split = 0.2
)

# Plotting model metrics
plot(history)

The preceding code generates the following plot:

The preceding plot shows the loss and mean absolute error for the training and validation data.