I have a tensor like this:
sim_topics = [[0.65 0. 0. 0. 0.42 0. 0. 0.51 0. 0.34 0.]
[0. 0.51 0. 0. 0.52 0. 0. 0. 0.53 0.42 0.]
[0. 0.32 0. 0.50 0.34 0. 0. 0.39 0.32 0.52 0.]
[0. 0.23 0.37 0. 0. 0.37 0.37 0. 0.47 0.39 0.3 ]]
I want to get indices in this tensor based on a tensor condition:
masked_t = [True False True False True True False True False True False]
So the output should be like this:
[[0.65 0. 0. 0. 0.42 0. 0. 0.51 0. 0.34 0.]
[0. 0. 0. 0. 0.52 0. 0. 0. 0. 0.42 0.]
[0. 0. 0. 0. 0.34 0. 0. 0.39 0. 0.52 0.]
[0. 0. 0.37 0. 0. 0.37 0. 0. 0. 0.39 0.]]
So the condition is working on the columns of the initial tensor. Actually I need the indices of the elements which they are True in the maske_t.
So the indices should be:
[[0, 0],
[1,0],
[2, 0],
[3,0],
[0,2],
[1,2],
[2,2],
[3,2],
....]]
Actually this approach works when Im doing row wise, but here I want to select specific columns based on a condition so it raise s incompatibility error:
out = tf.cast(tf.zeros(shape=tf.shape(sim_topics), dtype=tf.float64), tf.float64)
indices = tf.where(tf.where(masked_t, out, sim_topics))
You can directly obtain your required tensor like this:
result = tf.multiply(sim_topics, tf.cast(masked_t, dtype=tf.float64))
Let the broadcasting do the work for masked_t to be same size as sim_topics