Construct a Real-Valued Column

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

Arguments

...	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.

A numeric column.