From 3baa0c969375f90b5dfd63923d9f32f34f06612a Mon Sep 17 00:00:00 2001 From: purvasingh96 Date: Thu, 4 Mar 2021 11:58:59 +0530 Subject: [PATCH] master: Why transformer is better than basic seq2seq architecture. --- .../1. Transformer Models/Readme.md | 24 +++++++++++++++++- .../images/2. basic encoder-decoder.png | Bin 0 -> 318847 bytes .../images/3. transformer model.png | Bin 0 -> 309011 bytes .../images/4. multi-head attention.png | Bin 0 -> 238662 bytes .../images/5. positional encoding.png | Bin 0 -> 216571 bytes 5 files changed, 23 insertions(+), 1 deletion(-) create mode 100644 Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/images/2. basic encoder-decoder.png create mode 100644 Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/images/3. transformer model.png create mode 100644 Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/images/4. multi-head attention.png create mode 100644 Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/images/5. positional encoding.png diff --git a/Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/1. Transformer Models/Readme.md b/Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/1. Transformer Models/Readme.md index 6e3e59c4..57270512 100644 --- a/Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/1. Transformer Models/Readme.md +++ b/Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/1. Transformer Models/Readme.md @@ -7,4 +7,26 @@ the translation and as we know, with large sequences, the information tends to g LSTMs and GRUs can help to overcome the vanishing gradient problem, but even those will fail to process long sequences.


-2. \ No newline at end of file +2. In a conventional Encoder-decoder architeture, the model would again take T timesteps to compute the translation.

+
+ +## Transformers - Basics +```buildoutcfg +TLDR: +1. In RNNs, parallel computing is difficult to implement. +2. For long sequences in RNN, there is loss of information. +3. RNNs face the problem of vanishing gradient. +4. Transformer architecture is the solution. +``` + +1. Transformers are based on attention and don't require any sequential computation per layer, only one single step is needed. +2. Additionally, the gradient steps that need to be taken from the last output to the first input in a transformer is just one. +3. Transformers don't suffer from vanishing gradients problems that are related to the length of the sequences.

+
+4. Transformer differs from sequence to sequence by using multi-head attention layers instead of recurrent layers.

+
+ +5. Transformers also use positional encoding to capture sequential information. The positional encoding out puts values to be added to the embeddings. That's where every input word that is given to the model you have some of the information about it's order and the position. +
+ +6. Unlike the recurrent layer, the multi-head attention layer computes the outputs of each inputs in the sequence independently then it allows us to parallelize the computation. But it fails to model the sequential information for a given sequence. That is why you need to incorporate the positional encoding stage into the transformer model. diff --git a/Chapter-wise code/Code - PyTorch/7. Attention Models/2. Neural Text Summarization/images/2. basic encoder-decoder.png b/Chapter-wise code/Code - PyTorch/7. Attention Models/2. 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