mappers.py 文件源码

def scale_by_min_max(x, output_min=0.0, output_max=1.0, name=None):
  """Scale a numerical column into the range [output_min, output_max].

  Args:
    x: A numeric `Tensor`.
    output_min: The minimum of the range of output values.
    output_max: The maximum of the range of output values.
    name: (Optional) A name for this operation.

  Returns:
    A `Tensor` containing the input column scaled to [output_min, output_max].

  Raises:
    ValueError: If output_min, output_max have the wrong order.
  """
  with tf.name_scope(name, 'scale_by_min_max'):
    if output_min >= output_max:
      raise ValueError('output_min must be less than output_max')

    x = tf.to_float(x)
    min_x_value = analyzers.min(x)
    max_x_value = analyzers.max(x)

    x_shape = tf.shape(x)

    # If min==max, the result will be the mean of the requested range.
    # Note that both the options of tf.where are computed, which means that this
    # will compute unused NaNs.
    scaled_result = tf.where(
        tf.fill(x_shape, min_x_value < max_x_value),
        (x - min_x_value) / (max_x_value - min_x_value), tf.fill(x_shape, 0.5))

    return (scaled_result * (output_max - output_min)) + output_min