A model is a directed acyclic graph of layers.

keras_model(inputs, outputs = NULL)

Arguments

inputs

Input layer

outputs

Output layer

See also

Other model functions: compile, evaluate.keras.engine.training.Model, evaluate_generator, fit_generator, fit, get_config, get_layer, keras_model_sequential, pop_layer, predict.keras.engine.training.Model, predict_generator, predict_on_batch, predict_proba, summary.keras.engine.training.Model, train_on_batch

Examples

# NOT RUN {
library(keras)

# input layer
inputs <- layer_input(shape = c(784))

# outputs compose input + dense layers
predictions <- inputs %>%
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dense(units = 10, activation = 'softmax')

# create and compile model
model <- keras_model(inputs = inputs, outputs = predictions)
model %>% compile(
  optimizer = 'rmsprop',
  loss = 'categorical_crossentropy',
  metrics = c('accuracy')
)
# }