How to insert certain values at certain indices of a tensor in tensorflow?

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Let's say I have a tensor input of shape 100x1 and another tensor inplace of shape 20x1 and an index_tensor of shape 100x1. The index_tensor represents places of input where I want to insert the values from inplace. The index_tensor has only 20 True values and rest of its values are False. I try to explain the desired operation below. enter image description here How can this operation be achieved using tensorflow.

assign operation works only for tf.Variable while I want to apply it on the output of tf.nn.rnn.

I read one can use tf.scatter_nd but it requires inplace and index_tensor to be of the same shape.

The reason I want to use this is that I get an output from rnn, then I extract some values from and feed them to some dense layer and this output from dense layer, I want to insert back in the original tensor which I obtained from rnn operation. I do not want to apply dense layer operation on the whole output from rnn due to certain reasons and if I do not insert the result of dense layer back in output of rnn, then the dense layer is kind of useless.

Any suggestion will be really appreciated.

tensor keras tensorflow mutable python
1 Answers

Because the tensor you have is immutable, you can't assign a new value to it nor change it in place. What you have to do is modify its value using standard operations. Below is how you can do it:

input_array = np.array([2, 4, 7, 11, 3, 8, 9, 19, 11, 7])
inplace_array = np.array([10, 20])
indices_array = np.array([0, 0, 1, 0, 0, 0, 1, 0, 0, 0])
# [[2], [6]] 
indices = tf.cast(tf.where(tf.equal(indices_array, 1)), tf.int32)
# [0, 0, 10, 0, 0, 0, 20, 0, 0, 0]
scatter = tf.scatter_nd(indices, inplace_array, shape=tf.shape(input_array))
# [1, 1, 0, 1, 1, 1, 0, 1, 1, 1]
inverse_mask = tf.cast(tf.math.logical_not(indices_array), tf.int32)
# [2, 4, 0, 11, 3, 8, 0, 19, 11, 7]
input_array_zero_out = tf.multiply(inverse_mask, input_array)
# [2, 4, 10, 11, 3, 8, 20, 19, 11, 7]
output = tf.add(input_array_zero_out, tf.cast(scatter, tf.int32))
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