In [1]:

    

import tensorflow as tf
import numpy as np


    

In [5]:

    

input = [
    [[11, 12, 13], [14, 15, 16]],
    [[21, 22, 23], [24, 25, 26]],
    [[31, 32, 33], [34, 35, 36]],
    [[41, 42, 43], [44, 45, 46]],
    [[51, 52, 53], [54, 55, 56]],
    ]
print(np.asarray(input).shape)
s1 = tf.slice(input, [1, 0, 0], [1, 1, 3])
s2 = tf.slice(input, [2, 0, 0], [3, 1, 2])
s3 = tf.slice(input, [0, 0, 1], [4, 1, 1])
s4 = tf.slice(input, [0, 0, 1], [1, 0, 1])
s5 = tf.slice(input, [2, 0, 2], [-1, -1, -1]) # negative value means the function cutting tersors automatically
tf.global_variables_initializer()
with tf.Session() as s:
    print (s.run(s1))
    print (s.run(s2))
    print (s.run(s3))
    print (s.run(s4))


    

    




(5, 2, 3)
[[[21 22 23]]]
[[[31 32]]

 [[41 42]]

 [[51 52]]]
[[[12]]

 [[22]]

 [[32]]

 [[42]]]
[]

In [10]:

    

input = [[1,  2,  3,  4,  5 ], 
         [11, 22, 33, 44, 55], 
         [111, 222, 333, 444, 555]]    
    
print(np.asarray(input).shape)
print(input)
s1 = tf.slice(input, [0, 0], [-1,2])

tf.global_variables_initializer()
with tf.Session() as s:
    print (s.run(s1))


    

    




(3, 5)
[[1, 2, 3, 4, 5], [11, 22, 33, 44, 55], [111, 222, 333, 444, 555]]
[[  1   2]
 [ 11  22]
 [111 222]]