Function reduce_mean_v1

tensorflow/python/ops/math_ops.py:1752–1812

Computes the mean 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.

(input_tensor,
                   axis=None,
                   keepdims=None,
                   name=None,
                   reduction_indices=None,
                   keep_dims=None)

@tf_export(v1=["math.reduce_mean", "reduce_mean"])
def reduce_mean_v1(input_tensor,
                   axis=None,
                   keepdims=None,
                   name=None,
                   reduction_indices=None,
                   keep_dims=None):
  """Computes the mean 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., 1.], [2., 2.]])
  tf.reduce_mean(x)  # 1.5
  tf.reduce_mean(x, 0)  # [1.5, 1.5]
  tf.reduce_mean(x, 1)  # [1.,  2.]
  ```

  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 for the operation (optional).
    reduction_indices: The old (deprecated) name for axis.
    keep_dims: Deprecated alias for `keepdims`.

  Returns:
    The reduced tensor.

  @compatibility(numpy)
  Equivalent to np.mean

  Please note that `np.mean` 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_mean` has an aggressive type inference from `input_tensor`,
  for example:

  ```python
  x = tf.constant([1, 0, 1, 0])
  tf.reduce_mean(x)  # 0
  y = tf.constant([1., 0., 1., 0.])
  tf.reduce_mean(y)  # 0.5
  ```

  @end_compatibility
  """
  axis = deprecation.deprecated_argument_lookup("axis", axis,
                                                "reduction_indices",
                                                reduction_indices)