|TensorFlow 1 version|
updates from an existing tensor according to
Compat aliases for migration
See Migration guide for more details.
tf.tensor_scatter_nd_sub( tensor, indices, updates, name=None )
Used in the notebooks
|Used in the guide|
This operation creates a new tensor by subtracting sparse
updates from the
This operation is very similar to
tf.scatter_nd_sub, except that the updates
are subtracted from an existing tensor (as opposed to a variable). If the memory
for the existing tensor cannot be re-used, a copy is made and updated.
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 tensor_scatter_sub is to subtract individual elements from 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 subtract operation would look like this:
indices = tf.constant([, , , ]) updates = tf.constant([9, 10, 11, 12]) tensor = tf.ones(, dtype=tf.int32) updated = tf.tensor_scatter_nd_sub(tensor, indices, updates) print(updated)
The resulting tensor would look like this:
[1, -10, 1, -9, -8, 1, 1, -11]
We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values.
In Python, this scatter add operation would look like this:
indices = tf.constant([, ]) updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8,