Plot keras model

Plot keras model

plot_model(model, ...)

Arguments

model	A keras model defined using keras::keras_model_sequential or keras::keras_model
...	not used

Examples

## ---- sequential ----

require(keras)
#> Loading required package: keras
#> Warning: package 'keras' was built under R version 3.5.2

# Sequential model with several different layers

model <- keras_model_sequential() %>%
  layer_dense(10, input_shape = c(64, 64)) %>%
  layer_conv_1d(filters = 16, kernel_size = 8) %>%
  layer_max_pooling_1d() %>%
  layer_flatten() %>%
  layer_dense(25) %>%
  layer_dense(25, activation = "relu") %>%
  layer_dropout(0.25) %>%
  layer_dense(2, activation = "sigmoid")


model
#> Model
#> ________________________________________________________________________________
#> Layer (type)                        Output Shape                    Param #     
#> ================================================================================
#> dense_1 (Dense)                     (None, 64, 10)                  650         
#> ________________________________________________________________________________
#> conv1d_1 (Conv1D)                   (None, 57, 16)                  1296        
#> ________________________________________________________________________________
#> max_pooling1d_1 (MaxPooling1D)      (None, 28, 16)                  0           
#> ________________________________________________________________________________
#> flatten_1 (Flatten)                 (None, 448)                     0           
#> ________________________________________________________________________________
#> dense_2 (Dense)                     (None, 25)                      11225       
#> ________________________________________________________________________________
#> dense_3 (Dense)                     (None, 25)                      650         
#> ________________________________________________________________________________
#> dropout_1 (Dropout)                 (None, 25)                      0           
#> ________________________________________________________________________________
#> dense_4 (Dense)                     (None, 2)                       52          
#> ================================================================================
#> Total params: 13,873
#> Trainable params: 13,873
#> Non-trainable params: 0
#> ________________________________________________________________________________
#> 
#> 

model %>% plot_model()

## ---- network ----

# Model with several inputs and several outputs
# Example from https://keras.rstudio.com/articles/functional_api.html

model <- local({
  main_input <- layer_input(shape = c(100), dtype = 'int32', name = 'main_input')

  lstm_out <- main_input %>%
    layer_embedding(input_dim = 10000, output_dim = 512, input_length = 100) %>%
    layer_lstm(units = 32)

  auxiliary_output <- lstm_out %>%
    layer_dense(units = 1, activation = 'sigmoid', name = 'aux_output')

  auxiliary_input <- layer_input(shape = c(5), name = 'aux_input')

  main_output <- layer_concatenate(c(lstm_out, auxiliary_input)) %>%
    layer_dense(units = 64, activation = 'relu') %>%
    layer_dense(units = 64, activation = 'relu') %>%
    layer_dense(units = 64, activation = 'relu') %>%
    layer_dense(units = 1, activation = 'sigmoid', name = 'main_output')

  keras_model(
    inputs = c(main_input, auxiliary_input),
    outputs = c(main_output, auxiliary_output)
  )
})

model
#> Model
#> ________________________________________________________________________________
#> Layer (type)              Output Shape      Param #  Connected to               
#> ================================================================================
#> main_input (InputLayer)   (None, 100)       0                                   
#> ________________________________________________________________________________
#> embedding_1 (Embedding)   (None, 100, 512)  5120000  main_input[0][0]           
#> ________________________________________________________________________________
#> lstm_1 (LSTM)             (None, 32)        69760    embedding_1[0][0]          
#> ________________________________________________________________________________
#> aux_input (InputLayer)    (None, 5)         0                                   
#> ________________________________________________________________________________
#> concatenate_1 (Concatenat (None, 37)        0        lstm_1[0][0]               
#>                                                      aux_input[0][0]            
#> ________________________________________________________________________________
#> dense_5 (Dense)           (None, 64)        2432     concatenate_1[0][0]        
#> ________________________________________________________________________________
#> dense_6 (Dense)           (None, 64)        4160     dense_5[0][0]              
#> ________________________________________________________________________________
#> dense_7 (Dense)           (None, 64)        4160     dense_6[0][0]              
#> ________________________________________________________________________________
#> main_output (Dense)       (None, 1)         65       dense_7[0][0]              
#> ________________________________________________________________________________
#> aux_output (Dense)        (None, 1)         33       lstm_1[0][0]               
#> ================================================================================
#> Total params: 5,200,610
#> Trainable params: 5,200,610
#> Non-trainable params: 0
#> ________________________________________________________________________________
#> 
#> 

model %>% plot_model()