Construct a deep learning entity-matching model based on field similarity
encodings. The class wraps the constructor of the DLMatchingModel Python
class from the neer-match package. The class is built using the
tensorflow framework.
Usage
DLMatchingModel(
similarity_map,
initial_feature_width_scales = 10L,
feature_depths = 2L,
initial_record_width_scale = 10L,
record_depth = 4L,
...
)Arguments
- similarity_map
A
SimilarityMapobject.- initial_feature_width_scales
An integer or an integer vector of initial feature width scales for each field-pair network. The scale is multiplied by the number of similarities used in the field-pair network to determine the number of units of the first dense layer. If the input is a scalar, the same value is used for all field-pair networks.
- feature_depths
An integer or an integer vector of feature depths for each field-pair network. The depth is the number of hidden dense layers used in the field-pair network. If the input is a scalar, the same value is used for all field-pair networks.
- initial_record_width_scale
An integer representing the initial record width scale. The scale is multiplied by the number of field-pair networks to determine the number of units of the first dense layer of the record-pair network.
- record_depth
An integer representing the record depth. The depth is the number of hidden dense layers used in the record-pair network.
- ...
Additional arguments passed to the Python constructor. These arguments are passed down to the
tf.keras.Modelconstructor.
Examples
smap <- SimilarityMap(
instructions = list(
`a~c` = list("discrete", "gaussian"),
`b` = list("discrete", "levenshtein")
)
)
model <- DLMatchingModel(smap)