R: Construct a Dense Column

column_embedding {tfestimators}

R Documentation

Construct a Dense Column

Description

Use this when your inputs are sparse, but you want to convert them to a dense representation (e.g., to feed to a DNN). Inputs must be a categorical column created by any of the ⁠column_categorical_*()⁠ functions.

Usage

column_embedding(
  categorical_column,
  dimension,
  combiner = "mean",
  initializer = NULL,
  ckpt_to_load_from = NULL,
  tensor_name_in_ckpt = NULL,
  max_norm = NULL,
  trainable = TRUE
)

Arguments

`categorical_column`	A categorical column created by a `⁠column_categorical_*()⁠` function. This column produces the sparse IDs that are inputs to the embedding lookup.
`dimension`	A positive integer, specifying dimension of the embedding.
`combiner`	A string specifying how to reduce if there are multiple entries in a single row. Currently `"mean"`, `"sqrtn"` and `"sum"` are supported, with `"mean"` the default. `"sqrtn"`' often achieves good accuracy, in particular with bag-of-words columns. Each of this can be thought as example level normalizations on the column.
`initializer`	A variable initializer function to be used in embedding variable initialization. If not specified, defaults to `tf$truncated_normal_initializer` with mean `0.0` and standard deviation `1 / sqrt(dimension)`.
`ckpt_to_load_from`	String representing checkpoint name/pattern from which to restore column weights. Required if `tensor_name_in_ckpt` is not `NULL`.
`tensor_name_in_ckpt`	Name of the `Tensor` in `ckpt_to_load_from` from which to restore the column weights. Required if `ckpt_to_load_from` is not `NULL`.
`max_norm`	If not `NULL`, embedding values are l2-normalized to this value.
`trainable`	Whether or not the embedding is trainable. Default is TRUE.

Value

A dense column that converts from sparse input.

Raises