merge_models: Merge two models

In GenomeNet/deepG: Deep Learning for Genome Sequence Data

Merge two models

Description

Combine two models at certain layers and add dense layer(s) afterwards.

Usage

merge_models(
  models,
  layer_names,
  layer_dense,
  solver = "adam",
  learning_rate = 1e-04,
  freeze_base_model = c(FALSE, FALSE),
  model_seed = NULL
)

Arguments

models	List of two models.
layer_names	Vector of length 2 with names of layers to merge.
layer_dense	Vector specifying number of neurons per dense layer after last LSTM or CNN layer (if no LSTM used).
solver	Optimization method, options are ⁠"adam", "adagrad", "rmsprop"⁠ or "sgd".
learning_rate	Learning rate for optimizer.
freeze_base_model	Boolean vector of length 2. Whether to freeze weights of individual models.
model_seed	Set seed for model parameters in tensorflow if not NULL.

Value

A keras model merging two input models.

Examples

model_1 <- create_model_lstm_cnn(layer_lstm = c(64, 64), maxlen = 50, layer_dense = c(32, 4),
                                 verbose = FALSE)
model_2 <- create_model_lstm_cnn(layer_lstm = c(32), maxlen = 40, 
                                 layer_dense = c(8, 2), verbose = FALSE)
# get names of second to last layers
num_layers_1 <- length(model_1$get_config()$layers)
layer_name_1 <- model_1$get_config()$layers[[num_layers_1 - 1]]$name
num_layers_2 <- length(model_2$get_config()$layers)
layer_name_2 <- model_2$get_config()$layers[[num_layers_2 - 1]]$name
# merge models
model <- merge_models(models = list(model_1, model_2),
                      layer_names = c(layer_name_1, layer_name_2),
                      layer_dense = c(6, 2), 
                      freeze_base_model = c(FALSE, FALSE)) 

GenomeNet/deepG documentation built on Dec. 24, 2024, 12:11 p.m.