save_model_weights_tf: Save model weights in the SavedModel format
In dfalbel/keras: R Interface to 'Keras'

Save model weights in the SavedModel format

save_model_weights_tf(object, filepath, overwrite = TRUE)

load_model_weights_tf(
  object,
  filepath,
  by_name = FALSE,
  skip_mismatch = FALSE,
  reshape = FALSE
)

`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.

When saving in TensorFlow format, all objects referenced by the network are saved in the same format as tf.train.Checkpoint, including any Layer instances or Optimizer instances assigned to object attributes. For networks constructed from inputs and outputs using tf.keras.Model(inputs, outputs), Layer instances used by the network are tracked/saved automatically. For user-defined classes which inherit from tf.keras.Model, Layer instances must be assigned to object attributes, typically in the constructor.

See the documentation of tf.train.Checkpoint and tf.keras.Model for details.

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