parameters: (x, axis=None, keepdims=True, name='')
return_statement: return reduce_mean(x, axis, keepdims, name)
docstring: Computes the mean of the input tensor's elements across a specified axis or a list of specified axes.
  
      Example:
          >>> # create 3x2x2 matrix in a sequence of length 1 in a batch of one sample
          >>> data = np.array([[[5,1], [20,2]],[[30,1], [40,2]],[[55,1], [60,2]]], dtype=np.float32)
  
          >>> C.reduce_mean(data, 0).eval().round(4)
          array([[[ 30.,   1.],
                  [ 40.,   2.]]], dtype=float32)
          >>> np.mean(data, axis=0).round(4)
          array([[ 30.,   1.],
                 [ 40.,   2.]], dtype=float32)
          >>> C.reduce_mean(data, 1).eval().round(4)
          array([[[ 12.5,   1.5]],
          <BLANKLINE>
                 [[ 35. ,   1.5]],
          <BLANKLINE>
                 [[ 57.5,   1.5]]], dtype=float32)
          >>> np.mean(data, axis=1).round(4)
          array([[ 12.5,   1.5],
                 [ 35. ,   1.5],
                 [ 57.5,   1.5]], dtype=float32)
          >>> C.reduce_mean(data, (0,2)).eval().round(4)
          array([[[ 15.5],
                  [ 21. ]]], dtype=float32)
  
          >>> x = C.input_variable((2,2))
          >>> C.reduce_mean( x * 1.0, (C.Axis.default_batch_axis(), 1)).eval({x: data}).round(4)
          array([[ 15.5],
                 [ 21.      ]], dtype=float32)
  
      Args:
          x: input tensor
          axis (int or :class:`~cntk.axis.Axis` or a :obj:`list` or :obj:`tuple` of int or :class:`~cntk.axis.Axis`): axis along which the reduction will be performed
          keepdims (boolean): Keep the reduced dimension or not, default True mean keep reduced dimension
          name (str, optional): the name of the Function instance in the network
  
      Returns:
          :class:`~cntk.ops.functions.Function`
  
       Note that CNTK keeps the shape of the resulting tensors when reducing over multiple static axes.
docstring_summary: Computes the mean of the input tensor's elements across a specified axis or a list of specified axes.
docstring_tokens: Computes the mean of the input tensor s elements across a specified axis or a list of specified axes .
function: def reduce_mean(x, axis=None, keepdims=True, name=''):
      '''
      Computes the mean of the input tensor's elements across a specified axis or a list of specified axes.
  
      Example:
          >>> # create 3x2x2 matrix in a sequence of length 1 in a batch of one sample
          >>> data = np.array([[[5,1], [20,2]],[[30,1], [40,2]],[[55,1], [60,2]]], dtype=np.float32)
  
          >>> C.reduce_mean(data, 0).eval().round(4)
          array([[[ 30.,   1.],
                  [ 40.,   2.]]], dtype=float32)
          >>> np.mean(data, axis=0).round(4)
          array([[ 30.,   1.],
                 [ 40.,   2.]], dtype=float32)
          >>> C.reduce_mean(data, 1).eval().round(4)
          array([[[ 12.5,   1.5]],
          <BLANKLINE>
                 [[ 35. ,   1.5]],
          <BLANKLINE>
                 [[ 57.5,   1.5]]], dtype=float32)
          >>> np.mean(data, axis=1).round(4)
          array([[ 12.5,   1.5],
                 [ 35. ,   1.5],
                 [ 57.5,   1.5]], dtype=float32)
          >>> C.reduce_mean(data, (0,2)).eval().round(4)
          array([[[ 15.5],
                  [ 21. ]]], dtype=float32)
  
          >>> x = C.input_variable((2,2))
          >>> C.reduce_mean( x * 1.0, (C.Axis.default_batch_axis(), 1)).eval({x: data}).round(4)
          array([[ 15.5],
                 [ 21.      ]], dtype=float32)
  
      Args:
          x: input tensor
          axis (int or :class:`~cntk.axis.Axis` or a :obj:`list` or :obj:`tuple` of int or :class:`~cntk.axis.Axis`): axis along which the reduction will be performed
          keepdims (boolean): Keep the reduced dimension or not, default True mean keep reduced dimension
          name (str, optional): the name of the Function instance in the network
  
      Returns:
          :class:`~cntk.ops.functions.Function`
  
       Note that CNTK keeps the shape of the resulting tensors when reducing over multiple static axes.
      '''
      from cntk.cntk_py import reduce_mean
      x = sanitize_input(x)
      axis = sanitize_multi_axis_reduction_list(axis)
      return reduce_mean(x, axis, keepdims, name)
function_tokens: def reduce_mean ( x , axis = None , keepdims = True , name = '' ) : from cntk . cntk_py import reduce_mean x = sanitize_input ( x ) axis = sanitize_multi_axis_reduction_list ( axis ) return reduce_mean ( x , axis , keepdims , name )