Adjust Single Value within Tensor -- TensorFlow

UPDATE: TensorFlow 1.0 includes a tf.scatter_nd() operator, which can be used to create delta below without creating a tf.SparseTensor.

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This is actually surprisingly tricky with the existing ops! Perhaps somebody can suggest a nicer way to wrap up the following, but here's one way to do it.

Let's say you have a tf.constant() tensor:

c = tf.constant([[0.0, 0.0, 0.0],
                 [0.0, 0.0, 0.0],
                 [0.0, 0.0, 0.0]])

...and you want to add 1.0 at location [1, 1]. One way you could do this is to define a tf.SparseTensor, delta, representing the change:

indices = [[1, 1]]  # A list of coordinates to update.

values = [1.0]  # A list of values corresponding to the respective
                # coordinate in indices.

shape = [3, 3]  # The shape of the corresponding dense tensor, same as `c`.

delta = tf.SparseTensor(indices, values, shape)

Then you can use the tf.sparse_tensor_to_dense() op to make a dense tensor from delta and add it to c:

result = c + tf.sparse_tensor_to_dense(delta)

sess = tf.Session()
sess.run(result)
# ==> array([[ 0.,  0.,  0.],
#            [ 0.,  1.,  0.],
#            [ 0.,  0.,  0.]], dtype=float32)

How about tf.scatter_update(ref, indices, updates) or tf.scatter_add(ref, indices, updates)?

ref[indices[...], :] = updates
ref[indices[...], :] += updates

See this.