Adjust Single Value within Tensor -- TensorFlow

I feel embarrassed asking this, but how do you adjust a single value within a tensor? Suppose you want to add '1' to only one value within your tensor?

Doing it by indexing doesn't work:

TypeError: 'Tensor' object does not support item assignment

One approach would be to build an identically shaped tensor of 0's. And then adjusting a 1 at the position you want. Then you would add the two tensors together. Again this runs into the same problem as before.

I've read through the API docs several times and can't seem to figure out how to do this. Thanks in advance!

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.

I feel like the fact that tf.assign, tf.scatter_nd, tf.scatter_update functions only work on tf.Variables is not stressed enough. So there it is.

And in later versions of TensorFlow (tested with 1.14), you can use indexing on a tf.Variable to assign values to specific indices (again this only works on tf.Variable objects).

v = tf.Variable(tf.constant([[1,1],[2,3]]))
change_v = v[0,0].assign(4)
with tf.Session() as sess:
  tf.global_variables_initializer().run()
  print(sess.run(change_v))

tf.scatter_update has no gradient descent operator assigned and will generate an error while learning with at least tf.train.GradientDescentOptimizer. You have to implement bit manipulation with low level functions.

If you want to add 1 to element [2,0] (for example) of your tensor v (make sure your tensor is Variable), simply write:

v[2,0].assign(v[2,0]+1)

If you want to replace certain indices, I would create a boolean tensor mask and a broadcasted tensor with the new values at the correct positions. Then use

new_tensor = tf.where(boolen_tensor_mask, new_values_tensor, old_values_tensor)