tf.keras.ops.scatter_update

Update inputs via updates at scattered (sparse) indices.

tf.keras.ops.scatter_update(
    inputs, indices, updates
)

At a high level, this operation does inputs[indices] = updates. Assume inputs is a tensor of shape (D0, D1, ..., Dn), there are 2 main usages of scatter_update.

  1. indices is a 2D tensor of shape (num_updates, n), where num_updates is the number of updates to perform, and updates is a 1D tensor of shape (num_updates,). For example, if inputs is zeros((4, 4, 4)), and we want to update inputs[1, 2, 3] and inputs[0, 1, 3] as 1, then we can use:
inputs = np.zeros((4, 4, 4))
indices = [[1, 2, 3], [0, 1, 3]]
updates = np.array([1., 1.])
inputs = keras.ops.scatter_update(inputs, indices, updates)

2 indices is a 2D tensor of shape (num_updates, k), where num_updates is the number of updates to perform, and k (k < n) is the size of each index in indices. updates is a n - k-D tensor of shape (num_updates, inputs.shape[k:]). For example, if inputs = np.zeros((4, 4, 4)), and we want to update inputs[1, 2, :] and inputs[2, 3, :] as [1, 1, 1, 1], then indices would have shape (num_updates, 2) (k = 2), and updates would have shape (num_updates, 4) (inputs.shape[2:] = 4). See the code below:

inputs = np.zeros((4, 4, 4))
indices = [[1, 2], [2, 3]]
updates = np.array([[1., 1., 1, 1,], [1., 1., 1, 1,])
inputs = keras.ops.scatter_update(inputs, indices, updates)

inputs A tensor, the tensor to be updated.
indices A tensor or list/tuple of shape (N, inputs.ndim), specifying indices to update. N is the number of indices to update, must be equal to the first dimension of updates.
updates A tensor, the new values to be put to inputs at indices.

A tensor, has the same shape and dtype as inputs.