tf.compat.v1.batch_scatter_update  |  TensorFlow v2.16.1 (original) (raw)

tf.compat.v1.batch_scatter_update

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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 updateshave 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 totf.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 usingtf.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.