tfestimators: column_embedding – R documentation

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column_embedding

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

ValueError: if dimension not > 0.
ValueError: if exactly one of ckpt_to_load_from and tensor_name_in_ckpt is specified.
ValueError: if initializer is specified and is not callable.

column_embedding

Description

Usage

Arguments

Value

Raises

See Also

tfestimators

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