How to do slice assignment in Tensorflow

Question

I found that Tensorflow provides scatter_update() to assign values to the slice of a tensor in the 0 dimension. For example, if the tensor T is three dimensional, I can assign value v[1, :, :] to T[i, :, :].

a = tf.Variable(tf.zeros([10,36,36]))    value = np.ones([1,36,36])    d = tf.scatter_update(a,[0],value)  with tf.Session() as sess:     sess.run(tf.initialize_all_variables())     print a.eval()     sess.run(d)     print a.eval() 

But how to assign values v[1,1,:] to T[i,j,:]?

a = tf.Variable(tf.zeros([10,36,36]))    value1 = np.random.randn(1,1,36)     e = tf.scatter_update(a,[0],value1) #Error  with tf.Session() as sess:     sess.run(tf.initialize_all_variables())     print a.eval()     sess.rum(e)     print a.eval() 

Is there any other function that TF provide or a simple way to do this?

Answer 1

Currently, you can do slice assignment for variables in TensorFlow. There is no specific named function for it, but you can select a slice and call assign on it:

my_var = my_var[4:8].assign(tf.zeros(4)) 

First, note that (after having looked at the documentation) it seems that the return value of assign, even when applied to a slice, is always a reference to the whole variable after applying the update.

EDIT: The information below is either deprecated, imprecise or was always wrong. The fact is that the returned value of assign is a tensor that can be readily used and already incorporates the dependency to the assignment, so simply evaluating that or using it in further operations will ensure it gets executed without need for an explicit tf.control_dependencies block.

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Note, also, that this will only add the assignment op to the graph, but will not run it unless it is explicitly executed or set as a dependency of some other operation. A good practice is to use it in a tf.control_dependencies context:

with tf.control_dependencies([my_var[4:8].assign(tf.zeros(4))]):     my_var = tf.identity(my_var) 

You can read more about it in TensorFlow issue #4638.