For the model architecture and model summary, we will use the following code:

model <- keras_model_sequential()
model %>% layer_embedding(input_dim = 500, 
                          output_dim = 16, 
                          input_length = 100) %>%
         layer_flatten() %>% 
         layer_dense(units = 16, activation = 'relu') %>%
         layer_dense(units = 1, activation = "sigmoid")
summary(model)

OUTPUT
___________________________________________________________________
Layer (type)                  Output Shape              Param #    
===================================================================
embedding_12 (Embedding)      (None, 100, 16)           8000       
___________________________________________________________________
flatten_3 (Flatten)           (None, 1600)              0          
___________________________________________________________________
dense_6 (Dense)               (None, 16)                25616      
___________________________________________________________________
dense_7 (Dense)               (None, 1)                 17         
===================================================================
Total params: 33,633
Trainable params: 33,633
Non-trainable params: 0
___________________________________________________________________

From the preceding code, we can observe the following:

• Here, we've added layer_flatten() after layer_embedding().
• This is followed by a dense layer with 16 nodes and a relu activation function.
• The summary of the model shows that there are 33,633 parameters in total.

Now, we can compile the model.