diff options
Diffstat (limited to 'tensorflow/python/ops/sparse_ops.py')
| -rw-r--r-- | tensorflow/python/ops/sparse_ops.py | 50 |
1 files changed, 25 insertions, 25 deletions
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index e29248ed6c..4363be820f 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -238,10 +238,10 @@ def sparse_concat(concat_dim, sp_inputs, name=None, expand_nonconcat_dim=False): def sparse_add(a, b, thresh=0): """Adds two tensors, at least one of each is a `SparseTensor`. - If one `SparseTensor` and one `Output` are passed in, returns an `Output`. If + If one `SparseTensor` and one `Tensor` are passed in, returns a `Tensor`. If both arguments are `SparseTensor`s, this returns a `SparseTensor`. The order of arguments does not matter. Use vanilla `tf.add()` for adding two dense - `Output`s. + `Tensor`s. The indices of any input `SparseTensor` are assumed ordered in standard lexicographic order. If this is not the case, before this step run @@ -270,19 +270,19 @@ def sparse_add(a, b, thresh=0): * `thresh == 0.21`: .1, 0, and -.2 will vanish. Args: - a: The first operand; `SparseTensor` or `Output`. - b: The second operand; `SparseTensor` or `Output`. At least one operand + a: The first operand; `SparseTensor` or `Tensor`. + b: The second operand; `SparseTensor` or `Tensor`. At least one operand must be sparse. - thresh: A 0-D `Output`. The magnitude threshold that determines if an + thresh: A 0-D `Tensor`. The magnitude threshold that determines if an output value/index pair takes space. Its dtype should match that of the values if they are real; if the latter are complex64/complex128, then the dtype should be float32/float64, correspondingly. Returns: - A `SparseTensor` or an `Output`, representing the sum. + A `SparseTensor` or a `Tensor`, representing the sum. Raises: - TypeError: If both `a` and `b` are `Output`s. Use `tf.add()` instead. + TypeError: If both `a` and `b` are `Tensor`s. Use `tf.add()` instead. """ sparse_classes = (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue) if not any(isinstance(inp, sparse_classes) for inp in [a, b]): @@ -407,7 +407,7 @@ def sparse_reshape(sp_input, shape, name=None): Args: sp_input: The input `SparseTensor`. - shape: A 1-D (vector) int64 `Output` specifying the new dense shape of the + shape: A 1-D (vector) int64 `Tensor` specifying the new dense shape of the represented `SparseTensor`. name: A name prefix for the returned tensors (optional) @@ -452,7 +452,7 @@ def sparse_split(split_dim, num_split, sp_input, name=None): [ ] Args: - split_dim: A 0-D `int32` `Output`. The dimension along which to split. + split_dim: A 0-D `int32` `Tensor`. The dimension along which to split. num_split: A Python integer. The number of ways to split. sp_input: The `SparseTensor` to split. name: A name for the operation (optional). @@ -509,21 +509,21 @@ def sparse_to_dense(sparse_indices, are checked during execution. Args: - sparse_indices: A 0-D, 1-D, or 2-D `Output` of type `int32` or `int64`. + sparse_indices: A 0-D, 1-D, or 2-D `Tensor` of type `int32` or `int64`. `sparse_indices[i]` contains the complete index where `sparse_values[i]` will be placed. - output_shape: A 1-D `Output` of the same type as `sparse_indices`. Shape + output_shape: A 1-D `Tensor` of the same type as `sparse_indices`. Shape of the dense output tensor. - sparse_values: A 0-D or 1-D `Output`. Values corresponding to each row of + sparse_values: A 0-D or 1-D `Tensor`. Values corresponding to each row of `sparse_indices`, or a scalar value to be used for all sparse indices. - default_value: A 0-D `Output` of the same type as `sparse_values`. Value + default_value: A 0-D `Tensor` of the same type as `sparse_values`. Value to set for indices not specified in `sparse_indices`. Defaults to zero. validate_indices: A boolean value. If True, indices are checked to make sure they are sorted in lexicographic order and that there are no repeats. name: A name for the operation (optional). Returns: - Dense `Output` of shape `output_shape`. Has the same type as + Dense `Tensor` of shape `output_shape`. Has the same type as `sparse_values`. """ return gen_sparse_ops._sparse_to_dense( @@ -540,7 +540,7 @@ def sparse_reduce_sum(sp_input, axis=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 `Output` + `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 @@ -1043,14 +1043,14 @@ def sparse_fill_empty_rows(sp_input, default_value, name=None): def serialize_sparse(sp_input, name=None): - """Serialize a `SparseTensor` into a string 3-vector (1-D `Output`) object. + """Serialize a `SparseTensor` into a string 3-vector (1-D `Tensor`) object. Args: sp_input: The input `SparseTensor`. name: A name prefix for the returned tensors (optional). Returns: - A string 3-vector (1D `Output`), with each column representing the + A string 3-vector (1D `Tensor`), with each column representing the serialized `SparseTensor`'s indices, values, and shape (respectively). Raises: @@ -1063,12 +1063,12 @@ def serialize_sparse(sp_input, name=None): def serialize_many_sparse(sp_input, name=None): - """Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` string `Output`. + """Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` string `Tensor`. The `SparseTensor` must have rank `R` greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the `SparseTensor` must be sorted in increasing order of this first dimension. The serialized - `SparseTensor` objects going into each row of the output `Output` will have + `SparseTensor` objects going into each row of the output `Tensor` will have rank `R-1`. The minibatch size `N` is extracted from `sparse_shape[0]`. @@ -1078,7 +1078,7 @@ def serialize_many_sparse(sp_input, name=None): name: A name prefix for the returned tensors (optional). Returns: - A string matrix (2-D `Output`) with `N` rows and `3` columns. + A string matrix (2-D `Tensor`) with `N` rows and `3` columns. Each column represents serialized `SparseTensor`'s indices, values, and shape (respectively). @@ -1137,7 +1137,7 @@ def deserialize_many_sparse(serialized_sparse, dtype, rank=None, name=None): shape = [2 50] Args: - serialized_sparse: 2-D `Output` of type `string` of shape `[N, 3]`. + serialized_sparse: 2-D `Tensor` of type `string` of shape `[N, 3]`. The serialized and packed `SparseTensor` objects. dtype: The `dtype` of the serialized `SparseTensor` objects. rank: (optional) Python int, the rank of the `SparseTensor` objects. @@ -1526,7 +1526,7 @@ def _add_sparse_to_tensors_map(sp_input, container=None, name: A name prefix for the returned tensors (optional). Returns: - A string 1-vector (1D `Output`), with the single element representing the + A string 1-vector (1D `Tensor`), with the single element representing the a unique handle to a `SparseTensor` stored by the `SparseTensorMap` underlying this op. @@ -1547,7 +1547,7 @@ def _add_many_sparse_to_tensors_map(sp_input, container=None, The `SparseTensor` must have rank `R` greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the `SparseTensor` must be sorted in increasing order of this first dimension. The serialized - `SparseTensor` objects going into each row of the output `Output` will have + `SparseTensor` objects going into each row of the output `Tensor` will have rank `R-1`. The minibatch size `N` is extracted from `sparse_shape[0]`. @@ -1560,7 +1560,7 @@ def _add_many_sparse_to_tensors_map(sp_input, container=None, name: A name prefix for the returned tensors (optional). Returns: - A string matrix (2-D `Output`) with `N` rows and `1` column. + A string matrix (2-D `Tensor`) with `N` rows and `1` column. Each row represents a unique handle to a `SparseTensor` stored by the `SparseTensorMap` underlying this op. @@ -1623,7 +1623,7 @@ def _take_many_sparse_from_tensors_map( Args: sparse_map_op: The `Operation` that created the original handles. Usually this is, e.g., `add_sparse_to_tensors_map(...).op`. - sparse_handles: 2-D `Output` of type `string` of shape `[N, 1]`. + sparse_handles: 2-D `Tensor` of type `string` of shape `[N, 1]`. The serialized and packed `SparseTensor` objects. rank: (optional) Python int, the rank of the `SparseTensor` objects. name: A name prefix for the returned tensors (optional) |