What does the seq2seq model provide?

Introduction: Sequence to Sequence (often abbreviated to seq2seq) models is a special class of Recurrent Neural Network architectures that we typically use (but not restricted) to solve complex Language problems like Machine Translation, Question Answering, creating Chatbots, Text Summarization, etc.

Why Transformers are better than Lstms?

The Transformer model is based on a self-attention mechanism. The Transformer architecture has been evaluated to out preform the LSTM within these neural machine translation tasks. Thus, the transformer allows for significantly more parallelization and can reach a new state of the art in translation quality.

How are matrices used to train seq2seq model?

To train our seq2seq model we will use three matrices of one-hot vectors, Encoder input data, Decoder input data, and Decoder output data. The reason we are using two matrices for the Decoder is a method called teacher forcing which is used by the seq2seq model while training.

How to get maximum value from seq2seq model?

The way to get the maximum value from it is to use tf.reduce_max. On the decoder side, we need two different kinds of input for training and inference purposes repectively. While training phase, the input is provided as target label, but they still need to be embeded.

What are the sub-models of seq2seq model?

You can separate the entire model into 2 small sub-models. The first sub-model is called as [E] Encoder, and the second sub-model is called as [D] Decoder. [E] takes a raw input text data just like any other RNN architectures do.

What does the second None Mean in seq2seq?

The second None means the lengths of sentences. The maximum length of setence is different from batch to batch, so it cannot be set with the exact number. One option is to set the lengths of every sentences to the maximum length across all sentences in every batch.

What does the Seq2Seq model provide?

Introduction: Sequence to Sequence (often abbreviated to seq2seq) models is a special class of Recurrent Neural Network architectures that we typically use (but not restricted) to solve complex Language problems like Machine Translation, Question Answering, creating Chatbots, Text Summarization, etc.

How do seq2seq models work?

A Seq2Seq model is a model that takes a sequence of items (words, letters, time series, etc) and outputs another sequence of items. In the case of Neural Machine Translation, the input is a series of words, and the output is the translated series of words.

What does none mean in seq2seq model?

The first None means the batch size, and the batch size is unknown since user can set it. The second None means the lengths of sentences. The maximum length of setence is different from batch to batch, so it cannot be set with the exact number.

Which is the best dataset to train seq2seq?

For training our seq2seq model, we will use Cornell Movie — Dialogs Corpus Dataset which contains over 220,579 conversational exchanges between 10,292 pairs of movie characters. And it involves 9,035 characters from 617 movies. Here one of the conversations from the data set:

Which is a part of the encoding model?

As depicted in Fig 3, the encoding model consists of two different parts. The first part is the embedding layer. Each word in a sentence will be represented with the number of features specified as encoding_embedding_size. This layer gives much richer representative power for the words useful explanation.

Where can I find seq2seq model in TensorFlow?

Some codes/functions (save, load, measuring accuracy, etc) are provided by Udacity. However, majority part is implemented by myself along with much richer explanations and references on each section. Also, base figures (about model) is borrowed from Luong (2016).