| get_config {keras} | R Documentation |
Layer/Model configuration
Description
A layer config is an object returned from get_config() that contains the
configuration of a layer or model. The same layer or model can be
reinstantiated later (without its trained weights) from this configuration
using from_config(). The config does not include connectivity information,
nor the class name (those are handled externally).
Usage
get_config(object)
from_config(config, custom_objects = NULL)
Arguments
object |
Layer or model object |
config |
Object with layer or model configuration |
custom_objects |
list of custom objects needed to instantiate the layer,
e.g., custom layers defined by |
Value
get_config() returns an object with the configuration,
from_config() returns a re-instantiation of the object.
Note
Objects returned from get_config() are not serializable. Therefore,
if you want to save and restore a model across sessions, you can use the
model_to_json() function (for model configuration only, not weights) or
the save_model_tf() function to save the model configuration and weights
to the filesystem.
See Also
Other model functions:
compile.keras.engine.training.Model(),
evaluate.keras.engine.training.Model(),
evaluate_generator(),
fit.keras.engine.training.Model(),
fit_generator(),
get_layer(),
keras_model(),
keras_model_sequential(),
multi_gpu_model(),
pop_layer(),
predict.keras.engine.training.Model(),
predict_generator(),
predict_on_batch(),
predict_proba(),
summary.keras.engine.training.Model(),
train_on_batch()
Other layer methods:
count_params(),
get_input_at(),
get_weights(),
reset_states()