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Generalization of tf.compat.v1.scatter_update to axis different than 0. (deprecated)
tf.compat.v1.batch_scatter_update(
ref, indices, updates, use_locking=True, name=None
)
Analogous to batch_gather. This assumes that ref, indices and updates
have a series of leading dimensions that are the same for all of them, and the
updates are performed on the last dimension of indices. In other words, the
dimensions should be the following:
num_prefix_dims = indices.ndims - 1
batch_dim = num_prefix_dims + 1
updates.shape = indices.shape + var.shape[batch_dim:]
where
updates.shape[:num_prefix_dims]
== indices.shape[:num_prefix_dims]
== var.shape[:num_prefix_dims]
And the operation performed can be expressed as:
var[i_1, ..., i_n, indices[i_1, ..., i_n, j]] = updates[i_1, ..., i_n, j]
When indices is a 1D tensor, this operation is equivalent to
tf.compat.v1.scatter_update.
To avoid this operation there would be 2 alternatives:
1) Reshaping the variable by merging the first ndims dimensions. However,
this is not possible because tf.reshape returns a Tensor, which we
cannot use tf.compat.v1.scatter_update on.
2) Looping over the first ndims of the variable and using
tf.compat.v1.scatter_update on the subtensors that result of slicing the
first
dimension. This is a valid option for ndims = 1, but less efficient than
this implementation.
See also tf.compat.v1.scatter_update and tf.compat.v1.scatter_nd_update.
Args | |
|---|---|
ref
|
Variable to scatter onto.
|
indices
|
Tensor containing indices as described above. |
updates
|
Tensor of updates to apply to ref.
|
use_locking
|
Boolean indicating whether to lock the writing operation. |
name
|
Optional scope name string. |
Returns | |
|---|---|
Ref to variable after it has been modified.
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Raises | |
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ValueError
|
If the initial ndims of ref, indices, and updates are
not the same.
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