Is padding necessary for RNN?

In order to train the model in batches, all time series need to have the same length, and according to many papers in the literature padding should not affect the performance of the network.

Is padding required for LSTM?

However, when we pre-process and use the texts as inputs for our model e.g. LSTM, not all the sentences have the same length. In other words, naturally, some of the sentences are longer or shorter. We need to have the inputs with the same size, this is where the padding is necessary.

What is a sequence in LSTM?

The core components of an LSTM network are a sequence input layer and an LSTM layer. A sequence input layer inputs sequence or time series data into the network. An LSTM layer learns long-term dependencies between time steps of sequence data. The network starts with a sequence input layer followed by an LSTM layer.

What is padding in RNN?

Padding is a special form of masking where the masked steps are at the start or the end of a sequence. Padding comes from the need to encode sequence data into contiguous batches: in order to make all sequences in a batch fit a given standard length, it is necessary to pad or truncate some sequences.

Why do we use padding in NLP?

As in the NER problem you do padding as to extract more useful features from the context, however in a translation problem, you do padding to identify the end of a sentence because the decoder is trained sentence-by-sentence.

Is RNN a sequence to sequence model?

A typical sequence to sequence model has two parts – an encoder and a decoder. So when such an input sequence is passed though the encoder-decoder network consisting of LSTM blocks (a type of RNN architecture), the decoder generates words one by one in each time step of the decoder’s iteration.

What are padded sequences?

Why do we padding?

Padding is used to create space around an element’s content, inside of any defined borders. This element has a padding of 70px.

https://www.youtube.com/watch?v=1rOCxV0fSyM