Function reduce_std

tensorflow/python/ops/math_ops.py:1922–1964

Computes the standard deviation of elements across dimensions of a tensor. Reduces `input_tensor` along the dimensions given in `axis`. Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in `axis`. If `keepdims` is true, the reduced dimensions are retained with

(input_tensor, axis=None, keepdims=False, name=None)

@tf_export("math.reduce_std")
def reduce_std(input_tensor, axis=None, keepdims=False, name=None):
  """Computes the standard deviation of elements across dimensions of a tensor.

  Reduces `input_tensor` along the dimensions given in `axis`.
  Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
  entry in `axis`. If `keepdims` is true, the reduced dimensions
  are retained with length 1.

  If `axis` is None, all dimensions are reduced, and a
  tensor with a single element is returned.

  For example:

  ```python
  x = tf.constant([[1., 2.], [3., 4.]])
  tf.reduce_std(x)  # 1.1180339887498949
  tf.reduce_std(x, 0)  # [1., 1.]
  tf.reduce_std(x, 1)  # [0.5,  0.5]
  ```

  Args:
    input_tensor: The tensor to reduce. Should have numeric type.
    axis: The dimensions to reduce. If `None` (the default), reduces all
      dimensions. Must be in the range `[-rank(input_tensor),
      rank(input_tensor))`.
    keepdims: If true, retains reduced dimensions with length 1.
    name: A name scope for the associated operations (optional).

  Returns:
    The reduced tensor, of the same dtype as the input_tensor.

  @compatibility(numpy)
  Equivalent to np.std

  Please note that `np.std` has a `dtype` parameter that could be used to
  specify the output type. By default this is `dtype=float64`. On the other
  hand, `tf.reduce_std` has an aggressive type inference from `input_tensor`,
  @end_compatibility
  """
  name = name if name else "reduce_std"
  with ops.name_scope(name):
    variance = reduce_variance(input_tensor, axis=axis, keepdims=keepdims)
    return gen_math_ops.sqrt(variance)


@tf_export("math.reduce_prod", "reduce_prod", v1=[])