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

I experimented a bit with this method, which gave me some insights, which I think might be of some use. let's say we have a tensor.

a = np.array([[[1, 1.2, 1.3], [2, 2.2, 2.3], [7, 7.2, 7.3]],
              [[3, 3.2, 3.3], [4, 4.2, 4.3], [8, 8.2, 8.3]],
              [[5, 5.2, 5.3], [6, 6.2, 6.3], [9, 9.2, 9.3]]]) 
# a.shape = (3, 3, 3)

strided_slice() requires 4 required arguments input_, begin, end, strides in which we are giving our a as input_ argument. As the case with tf.slice() method, the begin argument is zero-based and rest of args shape-based. However in the docs begin and end both are zero-based.

The functionality of method is quite simple:
It works like iterating over a loop, where begin is the location of element in the tensor from where the loop initiates and end is where it stops.

tf.strided_slice(a, [0, 0, 0], [3, 3, 3], [1, 1, 1])

# output =  the tensor itself

tf.strided_slice(a, [0, 0, 0], [3, 3, 3], [2, 2, 2])

# output = [[[ 1.   1.3]
#            [ 7.   7.3]]
#           [[ 5.   5.3]
#            [ 9.   9.3]]]

strides are like steps over which the loop iterates, here the [2,2,2] makes method to produce values starting at (0,0,0), (0,0,2), (0,2,0), (0,2,2), (2,0,0), (2,0,2) ..... in the a tensor.

tf.strided_slice(input3, [1, 1, 0], [2, -1, 3], [1, 1, 1]) 

will produce output similar to tf.strided_slice(input3, [1, 1, 0], [2, 2, 3], [1, 1, 1]) as the tensora has shape = (3,3,3).