tf.contrib.layers.real_valued_column

tf.contrib.layers.real_valued_column(
    column_name,
    dimension=1,
    default_value=None,
    dtype=tf.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 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.