R/freeze.R

Defines functions apply_trainable unfreeze_weights freeze_weights

Documented in freeze_weights unfreeze_weights

#' Freeze and unfreeze weights
#'
#' Freeze weights in a model or layer so that they are no longer trainable.
#'
#' @param object Keras model or layer object
#' @param from Layer instance, layer name, or layer index within model
#' @param to Layer instance, layer name, or layer index within model
#'
#' @note The `from` and `to` layer arguments are both inclusive.
#'
#'   When applied to a model, the freeze or unfreeze is a global operation
#'   over all layers in the model (i.e. layers not within the specified 
#'   range will be set to the opposite value, e.g. unfrozen for a call to
#'   freeze).
#'
#'   Models must be compiled again after weights are frozen or unfrozen.
#'
#' @examples \dontrun{
#' # instantiate a VGG16 model
#' conv_base <- application_vgg16(
#'   weights = "imagenet",
#'   include_top = FALSE,
#'   input_shape = c(150, 150, 3)
#' )
#'
#' # freeze it's weights
#' freeze_weights(conv_base)
#'
#' # create a composite model that includes the base + more layers
#' model <- keras_model_sequential() %>%
#'   conv_base %>%
#'   layer_flatten() %>%
#'   layer_dense(units = 256, activation = "relu") %>%
#'   layer_dense(units = 1, activation = "sigmoid")
#'
#' # compile
#' model %>% compile(
#'   loss = "binary_crossentropy",
#'   optimizer = optimizer_rmsprop(lr = 2e-5),
#'   metrics = c("accuracy")
#' )
#'
#' # unfreeze weights from "block5_conv1" on
#' unfreeze_weights(conv_base, from = "block5_conv1")
#'
#' # compile again since we froze or unfroze weights
#' model %>% compile(
#'   loss = "binary_crossentropy",
#'   optimizer = optimizer_rmsprop(lr = 2e-5),
#'   metrics = c("accuracy")
#' )
#'
#' }
#'
#' @export
freeze_weights <- function(object, from = NULL, to = NULL) {
  
  # check for from and to and apply accordingly
  if (missing(from) && missing(to)) {
    object$trainable <- FALSE
  } else {
    object$trainable <- TRUE
    apply_trainable(object, from, to, FALSE)
  }
    
  # return model invisibly (for chaining)
  invisible(object)
}


#' @rdname freeze_weights
#' @export
unfreeze_weights <- function(object, from = NULL, to = NULL) {
  
  # object always trainable after unfreeze
  object$trainable <- TRUE
  
  # apply to individual layers if requested
  if (!missing(from) || !missing(to))
    apply_trainable(object, from, to, TRUE)
  
  # return model invisibly (for chaining)
  invisible(object)
}

apply_trainable <- function(object, from, to, trainable) {
  
  # first resolve from and to into layer names
  layers <- object$layers
  
  # NULL means beginning and end respectively
  if (is.null(from))
    from <- layers[[1]]$name
  if (is.null(to))
    to <- layers[[length(layers)]]$name
  
  # layer instances become layer names
  if (is_layer(from))
    from <- from$name
  if (is_layer(to))
    to <- to$name
  
  # layer indexes become layer names
  if (is.numeric(from))
    from <- layers[[from]]$name
  if (is.numeric(to))
    to <- layers[[to]]$name
  
  # apply trainable property
  set_trainable <- FALSE
  for (layer in layers) {
    
    # flag to begin applying property
    if (layer$name == from)
      set_trainable <- TRUE
    
    # apply property
    if (set_trainable)
      layer$trainable <- trainable
    else
      layer$trainable <- !trainable
    
    # flag to stop applying property
    if (layer$name == to)
      set_trainable <- FALSE
  }
}

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keras documentation built on Oct. 9, 2019, 1:04 a.m.