To pad strings: >>> n = ‘4’ >>> print(n.zfill(3)) 004 To pad numbers: >>> n = 4 >>> print(f'{n:03}’) # Preferred method, python >= 3.6 004 >>> print(‘%03d’ % n) 004 >>> print(format(n, ’03’)) # python >= 2.6 004 >>> print(‘{0:03d}’.format(n)) # python >= 2.6 + python 3 004 >>> print(‘{foo:03d}’.format(foo=n)) # python >= 2.6 … Read more
numpy.pad with constant mode does what you need, where we can pass a tuple as second argument to tell how many zeros to pad on each size, a (2, 3) for instance will pad 2 zeros on the left side and 3 zeros on the right side: Given A as: A = np.array([1,2,3,4,5]) np.pad(A, (2, … Read more
I have stumbled upon this problem too and below is what I figured out. When training RNN (LSTM or GRU or vanilla-RNN), it is difficult to batch the variable length sequences. For example: if the length of sequences in a size 8 batch is [4,6,8,5,4,3,7,8], you will pad all the sequences and that will result … Read more
To pad strings: >>> n = ‘4’ >>> print(n.zfill(3)) 004 To pad numbers: >>> n = 4 >>> print(f'{n:03}’) # Preferred method, python >= 3.6 004 >>> print(‘%03d’ % n) 004 >>> print(format(n, ’03’)) # python >= 2.6 004 >>> print(‘{0:03d}’.format(n)) # python >= 2.6 + python 3 004 >>> print(‘{foo:03d}’.format(foo=n)) # python >= 2.6 … Read more