tf.contrib.layers.real_valued_column

Creates a _RealValuedColumn for dense numeric data.

tf.contrib.layers.real_valued_column(
    column_name, dimension=1, default_value=None, dtype=tf.dtypes.float32,
    normalizer=None
)

Args
`column_name`	A string defining real valued column name.
`dimension`	An integer specifying dimension of the real valued column. The default is 1.
`default_value`	A single value compatible with dtype or a list of values compatible with dtype which the column takes on during tf.Example parsing if data is missing. When dimension is not None, a default value of None will cause tf.io.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 dimension. If a list of values is provided, the length of the list should be equal to the value of `dimension`. Only scalar default value is supported in case dimension is not specified.
`dtype`	defines the type of values. Default value is tf.float32. Must be a non-quantized, real integer or floating point type.
`normalizer`	If not None, a function that can be used to normalize the value of the real valued column after default_value is applied for parsing. Normalizer function takes the input tensor as its argument, and returns the output tensor. (e.g. lambda x: (x - 3.0) / 4.2). Note that for variable length columns, the normalizer should expect an input_tensor of type `SparseTensor`.

Returns
A _RealValuedColumn.

Raises
`TypeError`	if dimension is not an int
`ValueError`	if dimension is not a positive integer
`TypeError`	if default_value is a list but its length is not equal to the value of `dimension`.
`TypeError`	if default_value is not compatible with dtype.
`ValueError`	if dtype is not convertible to tf.float32.

Args