% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_model_utils.R
\name{remove_add_layers}
\alias{remove_add_layers}
\title{Remove layers from model and add dense layers}
\usage{
remove_add_layers(
  model = NULL,
  layer_name = NULL,
  dense_layers = NULL,
  shared_dense_layers = NULL,
  last_activation = list("softmax"),
  output_names = NULL,
  losses = NULL,
  verbose = TRUE,
  dropout = NULL,
  dropout_shared = NULL,
  freeze_base_model = FALSE,
  compile = FALSE,
  learning_rate = 0.001,
  solver = "adam",
  flatten = FALSE,
  global_pooling = NULL,
  model_seed = NULL,
  mixed_precision = FALSE,
  mirrored_strategy = NULL
)
}
\arguments{
\item{model}{A keras model.}

\item{layer_name}{Name of last layer to use from old model.}

\item{dense_layers}{List of vectors specifying number of units for each dense layer. If this is a list of length > 1, model
has multiple output layers.}

\item{shared_dense_layers}{Vector with number of units for dense layer. These layers will be connected on top of layer in
argument \code{layer_name}. Can be used to have shared dense layers, before model has multiple output layers. Don't use if model has just one output layer
(use only \code{dense_layers}).}

\item{last_activation}{List of activations for last entry for each list entry from \code{dense_layers}. Either \code{"softmax"}, \code{"sigmoid"} or \code{"linear"}.}

\item{output_names}{List of names for each output layer.}

\item{losses}{List of loss function for each output.}

\item{verbose}{Boolean.}

\item{dropout}{List of vectors with dropout rates for each new dense layer.}

\item{dropout_shared}{Vectors of dropout rates for dense layer from \code{shared_dense_layers}.}

\item{freeze_base_model}{Whether to freeze all weights before new dense layers.}

\item{compile}{Boolean, whether to compile the new model.}

\item{learning_rate}{Learning rate if \code{compile = TRUE}, default learning rate of the old model.}

\item{solver}{Optimization method, options are \verb{"adam", "adagrad", "rmsprop"} or \code{"sgd"}.}

\item{flatten}{Whether to add flatten layer before new dense layers.}

\item{global_pooling}{"max_ch_first" for global max pooling with channel first
(\href{https://keras.io/api/layers/pooling_layers/global_average_pooling1d/}{keras docs}),
"max_ch_last" for global max pooling with channel last, "average_ch_first" for global average pooling with channel first,
"average_ch_last" for global average pooling with channel last or \code{NULL} for no global pooling.
"both_ch_first" or "both_ch_last" to combine average and max pooling. "all" for all 4 options at once.}

\item{model_seed}{Set seed for model parameters in tensorflow if not \code{NULL}.}

\item{mixed_precision}{Whether to use mixed precision (https://www.tensorflow.org/guide/mixed_precision).}

\item{mirrored_strategy}{Whether to use distributed mirrored strategy. If NULL, will use distributed mirrored strategy only if >1 GPU available.}
}
\value{
A keras model; added and/or removed layers from some base model.
}
\description{
Function takes a model as input and removes all layers after a certain layer, specified in \code{layer_name} argument.
Optional to add dense layers on top of pruned model. Model can have multiple output layers with separate loss/activation functions.
You can freeze all the weights of the pruned model by setting \code{freeze_base_model = TRUE}.
}
\examples{
\dontshow{if (reticulate::py_module_available("tensorflow")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
model_1 <- create_model_lstm_cnn(layer_lstm = c(64, 64),
                                 maxlen = 50,
                                 layer_dense = c(32, 4), 
                                 verbose = FALSE)
# get name of second to last layer 
num_layers <- length(model_1$get_config()$layers)
layer_name <- model_1$get_config()$layers[[num_layers-1]]$name
# add dense layer with multi outputs and separate loss/activation functions
model_2 <- remove_add_layers(model = model_1,
                             layer_name = layer_name,
                             dense_layers = list(c(32, 16, 1), c(8, 1), c(12, 5)),
                             losses = list("binary_crossentropy", "mae",
                                           "categorical_crossentropy"),
                             last_activation = list("sigmoid", "linear", "softmax"),
                             freeze_base_model = TRUE,
                             output_names = list("out_1_binary_classsification", 
                                                 "out_2_regression", 
                                                 "out_3_classification")
) 
\dontshow{\}) # examplesIf}
}