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1. While Graph Neural Networks are used in recommendation systems at Pinterest, Alibaba and Twitter, a more subtle success story is the Transformer architecture, which has taken the NLP world by storm.
2. Through this post, I want to establish a link between Graph Neural Networks (GNNs) and Transformers.
3. Initially introduced for machine translation, Transformers have gradually replaced RNNs in mainstream NLP.
4. Transformers overcome issue (2) with LayerNorm, which normalizes and learns an affine transformation at the feature level.
5. The transformer is a component used in many neural network designs for processing sequential data, such as natural language text, genome sequences, sound signals or time series data.
6. A transformer neural network can take an input sentence in the form of a sequence of vectors, and converts it into a vector called an encoding, and then decodes it back into another sequence.
7. Crucially, the attention mechanism allows the transformer to focus on particular words on both the left and right of the current word in order to decide how to translate it.
8. Transformer neural networks replace the earlier recurrent neural network (RNN), long short term memory (LSTM), and gated recurrent (GRU) neural network designs.
9. We need one more technical detail to make Transformers easier to understand: Attention.
10. This is specific to the Transformer architecture because we do not have RNNs where we can input our sequence sequentially.
11. I hope that these descriptions have made the Transformer architecture a little bit clearer for everybody starting with Seq2Seq and encoder-decoder structures.
12. We have seen the Transformer architecture and we know from literature and the ‘Attention is All you Need’ authors that the model does extremely well in language tasks.
13. Transformers were recently used by OpenAI in their language models, and also used recently by DeepMind for AlphaStar — their program to defeat a top professional Starcraft player.
14. Transformers were developed to solve the problem of sequence transduction, or neural machine translation.
15. That’s why Transformers were created, they are a combination of both CNNs with attention.
16. For example, instead of only paying attention to each other in one dimension, Transformers use the concept of Multihead attention.
17. The paper is based on the application of transformer on NMT(Neural Machine Translator).
18. While in transformer, it is not like that, we can pass all the words of a sentence simultaneously and determine the word embedding simultaneously.
19. Now the main essence of the transformer comes in, “Self Attention”.
20. So, this is how the transformer works, and it is now the state-of-the-art technique in NLP.
21. However, unlike RNNs, Transformers do not require that the sequential data be processed in order.
22. Since the Transformer model facilitates more parallelization during training, it has enabled training on larger datasets than was possible before it was introduced.
23. The fact that Transformers do not rely on sequential processing, and lend themselves very easily to parallelization, allows Transformers to be trained more efficiently on larger datasets.
24. When a sentence is passed into a Transformer model, attention weights are calculated between every token simultaneously.
25. Be sure to check out the Tensor2Tensor notebook where you can load a Transformer model, and examine it using this interactive visualization.
26. , the transformer adds a vector to each input embedding.
27. The following steps repeat the process until a special symbol is reached indicating the transformer decoder has completed its output.
28. Now that we’ve covered the entire forward-pass process through a trained Transformer, it would be useful to glance at the intuition of training the model.
29. Transformers with Self-Attention mechanism where introduced in 2017 by a team at Google with Vaswani et al.
30. Giuliano Giacaglia in How Transformers Work observes that Transformers were devised with the aim of solving the problem of neural machine translation also known as sequence transduction.
31. A Transformer architecture also been used alongside an LSTM and Deep Reinforcement Learning in a gaming context by DeepMind in the strategy game AlphaStar.
32. Tied Transformers: Neural Machine Translation with Shared Encoder and Decoder.
33. This tutorial trains a Transformer model to translate a Portuguese to English dataset.
34. The core idea behind the Transformer model is self-attention—the ability to attend to different positions of the input sequence to compute a representation of that sequence.
35. A transformer model handles variable-sized input using stacks of self-attention layers instead of RNNs or CNNs.
36. The values used in the base model of transformer were; num_layers=6, d_model = 512, dff = 2048.
37. Now we provide an overview of the Transformer architecture in Fig.
38. In the following, we will implement the rest of the Transformer model.
39. Training¶ Let us instantiate an encoder-decoder model by following the Transformer architecture.
40. Similar to Section 9.7.4, we train the Transformer model for sequence to sequence learning on the English-French machine translation dataset.
41. This example demonstrates transformer neural nets (GPT and BERT) and shows how they can be used to create a custom sentiment analysis model.
42. The transformer architecture then processes the vectors using 12 structurally identical self-attention blocks stacked in a chain.
43. Several research articles report that transformers outperform recurrent nets for many language tasks.
44. Transformer architectures can learn longer-term dependency.
45. (2018) proposed the idea of applying the Transformer model for language modeling.
46. During the evaluation phase, the representations from the previous segments can be reused instead of being computed from scratch (as is the case of the Transformer model).
47. To better understand what a machine learning transformer is, and how they operate, let’s take a closer look at transformer models and the mechanisms that drive them.
48. To put that another way, an attention mechanism lets the transformer model process one input word while also attending to the relevant information contained by the other input words.
49. The transformer produces a sequence of word vector embeddings and positional encodings.
50. This allows the neural network model to preserve information regarding the relative position of the input words, even after the vectors move through the layers of the transformer network.
51. Transformer models have become the defacto standard for NLP tasks.
52. But even outside of NLP, you can also find transformers in the fields of computer vision and music generation.
53. That said, for such a useful model, transformers are still very difficult to understand.
54. It took me multiple readings of the Google research paper first introducing transformers, and a host of blog posts to really understand how transformers work.
55. The “Attention is all you need“ paper we mentioned previously introduces one more interesting concept that Transformers utilize called self-attention.
56. Like recurrent neural networks (RNNs), Transformers are designed to handle sequential data, such as natural language, for tasks such as translation and text summarization.
57. The method has benefited from the recent progress in the neural machine translation field, where the Transformer architecture demonstrated state-of-the-art results34.
58. The self-attention in Transformer architecture operates on both the input amino acid sequence and the already generated part of the SMILES string, giving access to any part of them at any time.
59. One type of network built with attention is called a transformer (explained below).
60. If you understand the transformer, you understand attention.
61. And the best way to understand the transformer is to contrast it with the neural networks that came before.
62. Transformers use attention mechanisms to gather information about the relevant context of a given word, and then encode that context in the vector that represents the word.
63. The spatial transformer module consists of layers of neural networks that can spatially transform an image.
64. In STNs, the transformer module knows where to apply the transformation to properly scale, resize, and crop and image.
65. In simple words, we can say that the spatial transformer module acts as an attention mechanism and knows where to focus on the input data.
66. We can include a spatial transformer module almost anywhere in an existing CNN model.
67. In this tutorial, you will learn how to augment your network using a visual attention mechanism called spatial transformer networks.
68. Spatial transformer networks (STN for short) allow a neural network to learn how to perform spatial transformations on the input image in order to enhance the geometric invariance of the model.
69. Using a standard convolutional network augmented with a spatial transformer network.
70. Depicting spatial transformer networks¶ Spatial transformer networks boils down to three main components : The localization network is a regular CNN which regresses the transformation parameters.

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