Compiles the model. The method prepares a neural network for estimation by setting the optimizer, loss function, and other parameters as needed.
DLMatchingModel
The method calls the
tf.keras.compile method of the tf.keras.Model class.
NSMatchingModel
Neural symbolic models are fit using a custom training loop so there is
no need to calls
tf.keras.compile method of the tf.keras.Model class. The method
sets the optimizer for the model. The loss (for hybrid neural-symbolic and
deep learning models) is set to binary cross-entropy (
tf.keras.losses.BinaryCrossentropy)
RefutationModel
Neural symbolic models are fit using a custom training loop so there is
no need to calls
tf.keras.compile method of the tf.keras.Model class. The method
sets the optimizer for the model. The loss (for hybrid neural-symbolic and
deep learning models) is set to binary cross-entropy (
tf.keras.losses.BinaryCrossentropy)
Usage
compile(object, ...)
# S4 method for class 'neer_match.matching_model.DLMatchingModel'
compile(object, ...)
# S4 method for class 'neer_match.matching_model.NSMatchingModel'
compile(object, optimizer = tensorflow::tf$keras$optimizers$Adam())
# S4 method for class 'neer_match.reasoning.RefutationModel'
compile(object, optimizer = tensorflow::tf$keras$optimizers$Adam())Arguments
- object
A matching model object.
- ...
Additional arguments passed to tf.keras.compile.
- optimizer
A tf.keras.optimizers optimizer object.
Examples
smap <- SimilarityMap(
instructions = list(
`movie ~film` = list("jaro_winkler", "damerau_levenshtein"),
`studio` = list("jaccard", "levenshtein"),
`reviews~score` = list("euclidean")
)
)
model <- DLMatchingModel(smap)
compile(model, optimizer = tensorflow::tf$keras$optimizers$Adam())