column_numeric: Construct a Real-Valued Column
In tfestimators: Interface to 'TensorFlow' Estimators

Construct a Real-Valued Column

column_numeric(
  ...,
  shape = c(1L),
  default_value = NULL,
  dtype = tf$float32,
  normalizer_fn = NULL
)

`...`	Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.
`shape`	An integer vector that specifies the shape of the tensor. An integer can be given which means a single dimension tensor with given width. The tensor representing the column will have the shape of `batch_size + shape`.
`default_value`	A single value compatible with `dtype` or an iterable of values compatible with `dtype` which the column takes on during parsing if data is missing. A default value of `NULL` will cause `tf$parse_example` to fail if an example does not contain this column. If a single value is provided, the same value will be applied as the default value for every item. If an iterable of values is provided, the shape of the `default_value` should be equal to the given `shape`.
`dtype`	The types for values contained in the column. The default value is `tf$float32`. Must be a non-quantized, real integer or floating point type.
`normalizer_fn`	If not `NULL`, a function that can be used to normalize the value of the tensor after `default_value` is applied for parsing. Normalizer function takes the input `Tensor` as its argument, and returns the output tensor. (e.g. `function(x) {(x - 3.0) / 4.2)}`. Please note that even though the most common use case of this function is normalization, it can be used for any kind of Tensorflow transformations.