def SecureSum(input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None): keepdims = False if keepdims is None else keepdims axis = math_ops._ReductionDims(input_tensor, axis) return _secure_ops.secure_reduce_sum(input_tensor, reduction_indices=axis, name=name, keep_dims=keepdims)
def SecureSum(input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None): keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims, "keep_dims", keep_dims) keepdims = False if keepdims is None else keepdims axis = math_ops._ReductionDims(input_tensor, axis) return _secure_ops.secure_reduce_sum(input_tensor, reduction_indices=axis, name=name, keep_dims=keepdims)
def rtt_mean( input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None, ): """Computes the mean of elements across dimensions of a tensor.""" keepdims = False if keepdims is None else keepdims axis = math_ops._ReductionDims(input_tensor, axis) input_tensor = rtt_ts.convert_to_rtttensor(input_tensor) _result = rtt_ts.rtt_ops.rtt_reduce_mean(input_tensor, reduction_indices=axis, name=name, keep_dims=keepdims) return rtt_ts.RttTensor(_result)
def MpcMean(input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None): # if axis is None: # axis = reduction_indices # if axis is None: # axis = -1 keepdims = False if keepdims is None else keepdims axis = math_ops._ReductionDims(input_tensor, axis) return _mpcops.mpc_mean(input_tensor, reduction_indices=axis, name=name, keep_dims=keepdims)
def sparse_reduce_sum(sp_input, reduction_axes=None, keep_dims=False): """Computes the sum of elements across dimensions of a SparseTensor. This Op takes a SparseTensor and is the sparse counterpart to `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor` instead of a sparse one. Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained with length 1. If `reduction_axes` has no entries, all dimensions are reduced, and a tensor with a single element is returned. For example: ```python # 'x' represents [[1, ?, 1] # [?, 1, ?]] # where ? is implictly-zero. tf.sparse_reduce_sum(x) ==> 3 tf.sparse_reduce_sum(x, 0) ==> [1, 1, 1] tf.sparse_reduce_sum(x, 1) ==> [2, 1] tf.sparse_reduce_sum(x, 1, keep_dims=True) ==> [[2], [1]] tf.sparse_reduce_sum(x, [0, 1]) ==> 3 ``` Args: sp_input: The SparseTensor to reduce. Should have numeric type. reduction_axes: The dimensions to reduce; list or scalar. If `None` (the default), reduces all dimensions. keep_dims: If true, retain reduced dimensions with length 1. Returns: The reduced Tensor. """ return gen_sparse_ops.sparse_reduce_sum(sp_input.indices, sp_input.values, sp_input.shape, math_ops._ReductionDims( sp_input, reduction_axes), keep_dims)
def rtt_sum( input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None, ): """Computes the sum of elements across dimensions of a tensor.""" keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims, "keep_dims", keep_dims) keepdims = False if keepdims is None else keepdims axis = math_ops._ReductionDims(input_tensor, axis) input_tensor = rtt_ts.convert_to_rtttensor(input_tensor) _result = rtt_ts.rtt_ops.rtt_reduce_sum(input_tensor, reduction_indices=axis, name=name, keep_dims=keepdims) return rtt_ts.RttTensor(_result)