|TensorFlow 1 version|
updates into an existing tensor according to
Compat aliases for migration
See Migration guide for more details.
tf.tensor_scatter_nd_update( tensor, indices, updates, name=None )
This operation creates a new tensor by applying sparse
updates to the passed
This operation is very similar to
tf.scatter_nd, except that the updates are
scattered onto an existing tensor (as opposed to a zero-tensor). If the memory
for the existing tensor cannot be re-used, a copy is made and updated.
indices contains duplicates, then their updates are accumulated (summed).
indices is an integer tensor containing indices into a new tensor of shape
shape. The last dimension of
indices can be at most the rank of
indices.shape[-1] <= shape.rank
The last dimension of
indices corresponds to indices into elements
indices.shape[-1] = shape.rank) or slices
indices.shape[-1] < shape.rank) along dimension
updates is a tensor with shape
indices.shape[:-1] + shape[indices.shape[-1]:]
The simplest form of scatter is to insert individual elements in a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements.
In Python, this scatter operation would look like this:
indices = tf.constant([, , , ])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones(, dtype=tf.int32)
print(tf.tensor_scatter_nd_update(tensor, indices, updates))
tf.Tensor([ 1 11 1 10 9 1 1 12], shape=(8,), dtype=int32)
We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted