freeze_weights: Freeze and unfreeze weights

In dfalbel/keras: R Interface to 'Keras'

Description

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

Usage

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

## Not run: 
# 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 
  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 
  loss = "binary_crossentropy",
  optimizer = optimizer_rmsprop(lr = 2e-5),
  metrics = c("accuracy")
)


## End(Not run)

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.