reversing a tensor for rnn stuff

reversing_tensors.py

from __future__ import print_function
import theano
import theano.tensor as T
import numpy as np
import random

A = np.arange(1,31).reshape(5,6).astype(np.float32)
sizes = np.zeros(5, dtype='int16')
for i in range(5):
    sizes[i] = random.randint(1,5)
    A[i,sizes[i]:] = 0
    
sizes = 6 - sizes
A_r = A[:,::-1]

def step_wrapper(seq1, indexer):
    out = T.zeros_like(seq1)
    N = seq1.shape[0]
    return T.set_subtensor(out[:N-indexer], seq1[indexer:])

M = T.matrix()
S = T.ivector()
result,_  = theano.scan(fn=step_wrapper, 
                        sequences=[M,S])
F = theano.function(inputs=[M,S], outputs=result)
print("Original matrix: \n", A)
print("reversed before: \n", A_r)
print("reversed after: \n", F(A_r, sizes))

'''
Output:

Original matrix: 
 [[  1.   2.   3.   4.   0.   0.]
 [  7.   0.   0.   0.   0.   0.]
 [ 13.   0.   0.   0.   0.   0.]
 [ 19.  20.  21.  22.  23.   0.]
 [ 25.  26.  27.  28.   0.   0.]]
reversed before: 
 [[  0.   0.   4.   3.   2.   1.]
 [  0.   0.   0.   0.   0.   7.]
 [  0.   0.   0.   0.   0.  13.]
 [  0.  23.  22.  21.  20.  19.]
 [  0.   0.  28.  27.  26.  25.]]
reversed after: 
 [[  4.   3.   2.   1.   0.   0.]
 [  7.   0.   0.   0.   0.   0.]
 [ 13.   0.   0.   0.   0.   0.]
 [ 23.  22.  21.  20.  19.   0.]
 [ 28.  27.  26.  25.   0.   0.]]
 '''