tf.contrib.layers.real_valued_column(
column_name,
dimension=1,
default_value=None,
dtype=tf.dtypes.float32,
normalizer=None
)
Defined in tensorflow/contrib/layers/python/layers/feature_column.py
.
Creates a _RealValuedColumn
for dense numeric data.
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.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 ofdimension
. 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 typeSparseTensor
.
Returns:
A _RealValuedColumn.
Raises:
TypeError
: if dimension is not an intValueError
: if dimension is not a positive integerTypeError
: if default_value is a list but its length is not equal to the value ofdimension
.TypeError
: if default_value is not compatible with dtype.ValueError
: if dtype is not convertible to tf.float32.