R/model.R

keras_model

Keras Model

Description

A model is a directed acyclic graph of layers.

Usage

 
keras_model(inputs, outputs = NULL, ...) 

Arguments

Arguments Description
inputs Input layer
outputs Output layer
Any additional arguments

Examples

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') 
) 

See Also

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