R/model-persistence.R

save_model_weights_hdf5

Save/Load model weights using HDF5 files

Description

Save/Load model weights using HDF5 files

Usage

 
save_model_weights_hdf5(object, filepath, overwrite = TRUE) 
 
load_model_weights_hdf5( 
  object, 
  filepath, 
  by_name = FALSE, 
  skip_mismatch = FALSE, 
  reshape = FALSE 
)

Arguments

Arguments Description
object Model object to save/load
filepath Path to the file
overwrite Whether to silently overwrite any existing file at the target location
by_name Whether to load weights by name or by topological order.
skip_mismatch Logical, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weight (only valid when by_name = FALSE).
reshape Reshape weights to fit the layer when the correct number of values are present but the shape does not match.

Details

The weight file has:

  • layer_names (attribute), a list of strings (ordered names of model layers).

  • For every layer, a group named layer.name

  • For every such layer group, a group attribute weight_names, a list of strings (ordered names of weights tensor of the layer).

  • For every weight in the layer, a dataset storing the weight value, named after the weight tensor.

    For load_model_weights(), if by_name is FALSE (default) weights are loaded based on the network’s topology, meaning the architecture should be the same as when the weights were saved. Note that layers that don’t have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don’t have weights. If by_name is TRUE, weights are loaded into layers only if they share the same name. This is useful for fine-tuning or transfer-learning models where some of the layers have changed.

See Also

Other model persistence: get_weights(), model_to_json(), model_to_yaml(), save_model_hdf5(), save_model_tf(), serialize_model()