There’s github issue #206 to support this nicely, meanwhile you have to resort to verbose work-arounds
The first example can be done with tf.select that combines two same-shaped tensors by selecting each element from one or the other
tf.reset_default_graph()
row_indices = tf.constant([1, 1, 2])
col_indices = tf.constant([0, 2, 3])
x = tf.zeros((3, 4))
sess = tf.InteractiveSession()
# get list of ((row1, col1), (row2, col2), ..)
coords = tf.transpose(tf.pack([row_indices, col_indices]))
# get tensor with 1's at positions (row1, col1),...
binary_mask = tf.sparse_to_dense(coords, x.get_shape(), 1)
# convert 1/0 to True/False
binary_mask = tf.cast(binary_mask, tf.bool)
twos = 2*tf.ones(x.get_shape())
# make new x out of old values or 2, depending on mask
x = tf.select(binary_mask, twos, x)
print x.eval()
gives
[[ 0. 0. 0. 0.]
[ 2. 0. 2. 0.]
[ 0. 0. 0. 2.]]
The second one could be done with scatter_update, except scatter_update only supports on linear indices and works on variables. So you could create a temporary variable and use reshaping like this. (to avoid variables you could use dynamic_stitch, see the end)
# get linear indices
linear_indices = row_indices*x.get_shape()[1]+col_indices
# turn 'x' into 1d variable since "scatter_update" supports linear indexing only
x_flat = tf.Variable(tf.reshape(x, [-1]))
# no automatic promotion, so make updates float32 to match x
updates = tf.constant([5, 4, 3], dtype=tf.float32)
sess.run(tf.initialize_all_variables())
sess.run(tf.scatter_update(x_flat, linear_indices, updates))
# convert back into original shape
x = tf.reshape(x_flat, x.get_shape())
print x.eval()
gives
[[ 0. 0. 0. 0.]
[ 5. 0. 4. 0.]
[ 0. 0. 0. 3.]]
Finally the third example is already supported with gather_nd, you write
print tf.gather_nd(x, coords).eval()
To get
[ 5. 4. 3.]
Edit, May 6
The update x[cols,rows]=newvals can be done without using Variables (which occupy memory between session run calls) by using select with sparse_to_dense that takes vector of sparse values, or relying on dynamic_stitch
sess = tf.InteractiveSession()
x = tf.zeros((3, 4))
row_indices = tf.constant([1, 1, 2])
col_indices = tf.constant([0, 2, 3])
# no automatic promotion, so specify float type
replacement_vals = tf.constant([5, 4, 3], dtype=tf.float32)
# convert to linear indexing in row-major form
linear_indices = row_indices*x.get_shape()[1]+col_indices
x_flat = tf.reshape(x, [-1])
# use dynamic stitch, it merges the array by taking value either
# from array1[index1] or array2[index2], if indices conflict,
# the later one is used
unchanged_indices = tf.range(tf.size(x_flat))
changed_indices = linear_indices
x_flat = tf.dynamic_stitch([unchanged_indices, changed_indices],
[x_flat, replacement_vals])
x = tf.reshape(x_flat, x.get_shape())
print x.eval()