parameters: (operand, epsilon=0.00001, use_stats_across_channels=False, do_variance_scaling=True, name='')
return_statement: return mean_variance_normalization(operand, epsilon, use_stats_across_channels, do_variance_scaling, name)
docstring: Computes mean-variance normalization of the specified input operand. 
  
      This operation computes and mean and variance for the entire tensor if use_stats_across_channels is True. 
      If use_stats_across_channels is False the computes mean and variance per channel and normalizes each 
      channel with its own mean and variance. If do_variance_scaling is False, only the mean is subtracted,
      and the variance scaling is omitted.
      
      Example:
          >>> data = np.array([[[0., 2], [4., 6.]], [[0., 4], [8., 12.]]]).astype(np.float32)
          >>> data
          array([[[  0.,   2.],
                  [  4.,   6.]],
          <BLANKLINE>
                 [[  0.,   4.],
                  [  8.,  12.]]], dtype=float32)
          >>> saved_precision = np.get_printoptions()['precision']
          >>> np.set_printoptions(precision=4) # For consistent display upto 4 decimals.
          >>> C.mean_variance_normalization(data).eval()
          array([[[-1.3416, -0.4472],
                  [ 0.4472,  1.3416]],
          <BLANKLINE>
                 [[-1.3416, -0.4472],
                  [ 0.4472,  1.3416]]], dtype=float32)
          >>> np.set_printoptions(precision=saved_precision) # Reseting the display precision.
  
      Args:
          operand: Input tensor, with dimensions :math:`[C \\times H \\times W]`.
          epsilon (double, default 0.00001): epsilon added to the standard deviation to avoid division by 0.
          use_stats_across_channels (bool): If False, mean and variance are computed per channel.
           If True, mean and variance are computed over the entire tensor (all axes).
          do_variance_scaling (bool): If False, only the mean is subtracted. If True, it is also
           scaled by inverse of standard deviation.
          name (str, optional): the name of the Function instance in the network
      Returns:
          :class:`~cntk.ops.functions.Function`
docstring_summary: Computes mean-variance normalization of the specified input operand.
docstring_tokens: Computes mean - variance normalization of the specified input operand .
function: def mean_variance_normalization(operand, epsilon=0.00001, use_stats_across_channels=False, do_variance_scaling=True, name=''):
      '''
      Computes mean-variance normalization of the specified input operand. 
  
      This operation computes and mean and variance for the entire tensor if use_stats_across_channels is True. 
      If use_stats_across_channels is False the computes mean and variance per channel and normalizes each 
      channel with its own mean and variance. If do_variance_scaling is False, only the mean is subtracted,
      and the variance scaling is omitted.
      
      Example:
          >>> data = np.array([[[0., 2], [4., 6.]], [[0., 4], [8., 12.]]]).astype(np.float32)
          >>> data
          array([[[  0.,   2.],
                  [  4.,   6.]],
          <BLANKLINE>
                 [[  0.,   4.],
                  [  8.,  12.]]], dtype=float32)
          >>> saved_precision = np.get_printoptions()['precision']
          >>> np.set_printoptions(precision=4) # For consistent display upto 4 decimals.
          >>> C.mean_variance_normalization(data).eval()
          array([[[-1.3416, -0.4472],
                  [ 0.4472,  1.3416]],
          <BLANKLINE>
                 [[-1.3416, -0.4472],
                  [ 0.4472,  1.3416]]], dtype=float32)
          >>> np.set_printoptions(precision=saved_precision) # Reseting the display precision.
  
      Args:
          operand: Input tensor, with dimensions :math:`[C \\times H \\times W]`.
          epsilon (double, default 0.00001): epsilon added to the standard deviation to avoid division by 0.
          use_stats_across_channels (bool): If False, mean and variance are computed per channel.
           If True, mean and variance are computed over the entire tensor (all axes).
          do_variance_scaling (bool): If False, only the mean is subtracted. If True, it is also
           scaled by inverse of standard deviation.
          name (str, optional): the name of the Function instance in the network
      Returns:
          :class:`~cntk.ops.functions.Function`
      
      '''
      from cntk.cntk_py import mean_variance_normalization
      if epsilon < 0:
          raise ValueError('epsilon must be non-negative.')
      operand = sanitize_input(operand, get_data_type(operand))  
      return mean_variance_normalization(operand, epsilon, use_stats_across_channels, do_variance_scaling, name)
function_tokens: def mean_variance_normalization ( operand , epsilon = 0.00001 , use_stats_across_channels = False , do_variance_scaling = True , name = '' ) : from cntk . cntk_py import mean_variance_normalization if epsilon < 0 : raise ValueError ( 'epsilon must be non-negative.' ) operand = sanitize_input ( operand , get_data_type ( operand ) ) return mean_variance_normalization ( operand , epsilon , use_stats_across_channels , do_variance_scaling , name )