tf_gist: strided_slice

strided_slice.py

# strided_slice(
#     input_,
#     begin,
#     end,
#     strides=None,
#     begin_mask=0,
#     end_mask=0,
#     ellipsis_mask=0,
#     new_axis_mask=0,
#     shrink_axis_mask=0,
#     var=None,
#     name=None
# )

# If you're familiar with numpy arrays, you'll know that you can make slices
# via input[start1:end1:step1, start2:end2:step2, ... startN:endN:stepN].
# Basically, a very succinct way of writing for loops
# to get certain elements of the array.

# Well, strided_slice just allows you to do this fancy indexing without
# the syntactic sugar. The numpy example from above just becomes

# input[start1:end1:step1, start2:end2:step2, ... startN:endN:stepN]

# >>>>>>>>>>>>>>>>>>>

# tf.strided_slice(input,
#                  [start1, start2, ..., startN],
#                  [end1, end2, ..., endN],
#                  [step1, step2, ..., stepN])

import tensorflow as tf
import numpy as np


my_input = np.array([[1, 2, 3],
                     [4, 5, 6],
                     [7, 8, 9]])


result1 = my_input[0:3:1, 0:3:1]
result2 = my_input[0:3:2, 0:3:2]

tf_result1 = tf.strided_slice(my_input, [0, 0], [3, 3], [1, 1])
tf_result2 = tf.strided_slice(my_input, [0, 0], [3, 3], [2, 2])

sess = tf.InteractiveSession()

print(result1)
print()
print(sess.run(tf_result1))
print()
print()
print(result2)
print()
print(sess.run(tf_result2))