def scatter_nd_add(ref, indices, updates, use_locking=False, name=None): r"""Applies sparse addition to individual values or slices in a Variable. `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. `indices` must be integer tensor, containing indices into `ref`. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. The innermost dimension of `indices` (with length `K`) corresponds to indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th dimension of `ref`. `updates` is `Tensor` of rank `Q-1+P-K` with shape: ``` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]] ``` For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this: ```python ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1], [7]]) updates = tf.constant([9, 10, 11, 12]) add = tf.scatter_nd_add(ref, indices, updates) with tf.Session() as sess: print sess.run(add) ``` The resulting update to ref would look like this: [1, 13, 3, 14, 14, 6, 7, 20] See `tf.scatter_nd` for more details about how to make updates to slices. Args: ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. A mutable Tensor. Should be from a Variable node. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A tensor of indices into ref. updates: A `Tensor`. Must have the same type as `ref`. A tensor of updated values to add to ref. use_locking: An optional `bool`. Defaults to `False`. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `ref`. """ if ref.dtype._is_ref_dtype: return gen_state_ops.scatter_nd_add( ref, indices, updates, use_locking, name) return ref._lazy_read(gen_state_ops.resource_scatter_nd_add( # pylint: disable=protected-access ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype), name=name))
def scatter_nd_add(ref, indices, updates, use_locking=False, name=None): r"""Applies sparse addition to individual values or slices in a Variable. `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. `indices` must be integer tensor, containing indices into `ref`. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. The innermost dimension of `indices` (with length `K`) corresponds to indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th dimension of `ref`. `updates` is `Tensor` of rank `Q-1+P-K` with shape: ``` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]] ``` For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this: ```python ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1], [7]]) updates = tf.constant([9, 10, 11, 12]) add = tf.scatter_nd_add(ref, indices, updates) with tf.Session() as sess: print sess.run(add) ``` The resulting update to ref would look like this: [1, 13, 3, 14, 14, 6, 7, 20] See `tf.scatter_nd` for more details about how to make updates to slices. Args: ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. A mutable Tensor. Should be from a Variable node. indices: A `Tensor`. Must be one of the following types: `int32`, `int64`. A tensor of indices into ref. updates: A `Tensor`. Must have the same type as `ref`. A tensor of updated values to add to ref. use_locking: An optional `bool`. Defaults to `False`. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. name: A name for the operation (optional). Returns: A mutable `Tensor`. Has the same type as `ref`. """ if ref.dtype._is_ref_dtype: return gen_state_ops.scatter_nd_add(ref, indices, updates, use_locking, name) return ref._lazy_read( gen_state_ops.resource_scatter_nd_add( # pylint: disable=protected-access ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype), name=name))