Freeze weights in a model or layer so that they are no longer trainable.

freeze_weights(object, from = NULL, to = NULL, which = NULL)

unfreeze_weights(object, from = NULL, to = NULL, which = NULL)

Arguments

object

Keras model or layer object

from

Layer instance, layer name, or layer index within model

to

Layer instance, layer name, or layer index within model

which

layer names, integer positions, layers, logical vector (of length(object$layers)), or a function returning a logical vector.

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

if (FALSE) {
conv_base <- application_vgg16(
  weights = "imagenet",
  include_top = FALSE,
  input_shape = c(150, 150, 3)
)

# freeze it's weights
freeze_weights(conv_base)

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")
)

model
print(model, expand_nested = TRUE)



# 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")
)

conv_base
print(model, expand_nested = TRUE)

# freeze only the last 5 layers
freeze_weights(conv_base, from = -5)
conv_base
# equivalently, also freeze only the last 5 layers
unfreeze_weights(conv_base, to = -6)
conv_base

# Freeze only layers of a certain type, e.g, BatchNorm layers
batch_norm_layer_class_name <- class(layer_batch_normalization())[1]
is_batch_norm_layer <- function(x) inherits(x, batch_norm_layer_class_name)

model <- application_efficientnet_b0()
freeze_weights(model, which = is_batch_norm_layer)
model
# equivalent to:
for(layer in model$layers) {
  if(is_batch_norm_layer(layer))
    layer$trainable <- FALSE
  else
    layer$trainable <- TRUE
}
}