library(keras)
# define custom metric
metric_top_3_categorical_accuracy <-
custom_metric("top_3_categorical_accuracy", function(y_true, y_pred) {
metric_top_k_categorical_accuracy(y_true, y_pred, k = 3)
})
with_custom_object_scope(c(top_k_acc = sparse_top_k_cat_acc), {
# ...define model...
# compile model (refer to "top_k_acc" by name)
model %>% compile(
loss = "binary_crossentropy",
optimizer = optimizer_nadam(),
metrics = c("top_k_acc")
)
# save the model
save_model_hdf5("my_model.h5")
# loading the model within the custom object scope doesn't
# require explicitly providing the custom_object
load_model_hdf5("my_model.h5")
}) with_custom_object_scope
Provide a scope with mappings of names to custom objects
Description
Provide a scope with mappings of names to custom objects
Usage
with_custom_object_scope(objects, expr) Arguments
| Arguments | Description |
|---|---|
| objects | Named list of objects |
| expr | Expression to evaluate |
Details
There are many elements of Keras models that can be customized with user objects (e.g. losses, metrics, regularizers, etc.). When loading saved models that use these functions you typically need to explicitily map names to user objects via the custom_objects parmaeter. The with_custom_object_scope() function provides an alternative that lets you create a named alias for a user object that applies to an entire block of code, and is automatically recognized when loading saved models.