# Initialize the sequential model
model2 <- keras_model_sequential()

# Add layers to model
model2 %>%
    layer_dense(units = 8, activation = 'relu', input_shape = c(4)) %>%
    layer_dense(units = 5, activation = 'relu') %>%
    layer_dense(units = 3, activation = 'softmax')

# Compile the model
model2 %>% compile(
     loss = 'categorical_crossentropy',
     optimizer = 'adam',
     metrics = 'accuracy'
 )

# Fit the model to the data
history2 = model2 %>% fit(
     iris.training, iris.trainLabels,
     epochs = 200, batch_size = 5,
     validation_split = 0.2
 )