plot_model: visualization of the model architecture
In dongminjung/ttgsea: Tokenizing Text of Gene Set Enrichment Analysis

Description Usage Arguments Value Author(s) See Also Examples

View source: R/ttgsea.R

You are allowed to create a visualization of your model architecture. This architecture displays the information about the name, input shape, and output shape of layers in a flowchart.

1	plot_model(x)

`x`	deep learning model

plot for the model architecture

Dongmin Jung

purrr::map, purrr::map_chr, purrr::pluck, purrr::imap_dfr, DiagrammeR::grViz

library(reticulate)
if (keras::is_keras_available() & reticulate::py_available()) {
  inputs1 <- layer_input(shape = c(1000))
  inputs2 <- layer_input(shape = c(1000))
  
  predictions1 <- inputs1 %>%
    layer_dense(units = 128, activation = 'relu') %>%
    layer_dense(units = 64, activation = 'relu') %>%
    layer_dense(units = 32, activation = 'softmax')
  
  predictions2 <- inputs2 %>%
    layer_dense(units = 128, activation = 'relu') %>%
    layer_dense(units = 64, activation = 'relu') %>%
    layer_dense(units = 32, activation = 'softmax')
  
  combined <- layer_concatenate(c(predictions1, predictions2)) %>%
    layer_dense(units = 16, activation = 'softmax')
  
  
  model <- keras_model(inputs = c(inputs1, inputs2),
                       outputs = combined)
  plot_model(model)
}