Sequence padding

1. What is sequence padding?
2. What is padding in tokenization?
3. Why padding is required in LSTM?
4. What is padding in Tensorflow?

What is sequence padding?

Introduction. Masking is a way to tell sequence-processing layers that certain timesteps in an input are missing, and thus should be skipped when processing the data. Padding is a special form of masking where the masked steps are at the start or the end of a sequence.

What is padding in tokenization?

Padding adds a special padding token to ensure shorter sequences will have the same length as either the longest sequence in a batch or the maximum length accepted by the model.

Why padding is required in LSTM?

Since LSTMs and CNNs take inputs of the same length and dimension, input images and sequences are padded to maximum length while testing and training. This padding can affect the way the networks function and can make a great deal when it comes to performance and accuracies.

What is padding in Tensorflow?

paddings is an integer tensor with shape [n, 2] , where n is the rank of tensor . For each dimension D of input , paddings[D, 0] indicates how many values to add before the contents of tensor in that dimension, and paddings[D, 1] indicates how many values to add after the contents of tensor in that dimension.