What does `tf.strided_slice()` do?

Question

I am wondering what tf.strided_slice() operator actually does.
The doc says,

To a first order, this operation extracts a slice of siz

Answer 1

tf.strided_slice() is used to do numpy style slicing of a tensor variable. It has 4 parameters in general: input, begin, end, strides.The slice continues by adding stride to the begin index until all dimensions are not less than the end. For ex: Let us take a tensor constant named "sample" of dimensions: [3,2,3]

import tensorflow as tf 

sample = tf.constant(
    [[[11, 12, 13], [21, 22, 23]],
    [[31, 32, 33], [41, 42, 43]],
    [[51, 52, 53], [61, 62, 63]]])

slice = tf.strided_slice(sample, begin=[0,0,0], end=[3,2,3], strides=[2,2,2])

with tf.Session() as sess:
    print(sess.run(slice))

Now, the output will be:

[[[11 13]]

 [[51 53]]]

This is because the striding starts from [0,0,0] and goes to [2,1,2] discarding any non-existent data like:

[[0,0,0], [0,0,2], [0,2,0], [0,2,2],
[2,0,0], [2,0,2], [2,2,0], [2,2,2]]

If you use [1,1,1] as strides then it will simply print all the values.