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

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

unfreeze_weights(object, from = NULL, to = 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

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) { # 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") ) }