Questions tagged [attention-mechanism]

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51
votes
4answers
35k views

What is the positional encoding in the transformer model?

I'm trying to read and understand the paper Attention is all you need and in it, there is a picture: I don't know what positional encoding is. by listening to some youtube videos I've found out that ...
18
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4answers
8k views

Gumbel-Softmax trick vs Softmax with temperature

From what I understand, the Gumbel-Softmax trick is a technique that enables us to sample discrete random variables, in a way that is differentiable (and therefore suited for end-to-end deep learning)....
15
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1answer
6k views

Can BERT do the next-word-predict task?

As BERT is bidirectional (uses bi-directional transformer), is it possible to use it for the next-word-predict task? If yes, what needs to be tweaked?
12
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3answers
792 views

How does attention mechanism learn?

I know how to build an attention in neural networks. But I don’t understand how attention layers learn the weights that pay attention to some specific embedding. I have this question because I’m ...
11
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4answers
2k views

In a Transformer model, why does one sum positional encoding to the embedding rather than concatenate it?

While reviewing the Transformer architecture, I realized something I didn't expect, which is that : the positional encoding is summed to the word embeddings rather than concatenated to it. ...
10
votes
2answers
4k views

Variable input/output length for Transformer

I was reading the paper "Attention is all you need" (https://arxiv.org/pdf/1706.03762.pdf ) and came across this site http://jalammar.github.io/illustrated-transformer/ which provided a great ...
10
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1answer
1k views

How do attention mechanisms in RNNs learn weights for a variable length input

Attention mechanisms in RNNs are reasonably common to sequence to sequence models. I understand that the decoder learns a weight vector $\alpha$ which is applied as a weighted sum of the output ...
9
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3answers
7k views

What's the difference between Attention vs Self-Attention? What problems does each other solve that the other can't?

As stated in the question above..is there a difference between attention and self attention mechanism ? Also additionally can anybody share with me tips and tricks about how self attention mechanism ...
7
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2answers
328 views

Transformer model: Why are word embeddings scaled before adding positional encodings?

While going over a Tensorflow tutorial for the Transformer model I realized that their implementation of the Encoder layer (and the Decoder) scales word embeddings by sqrt of embedding dimension ...
6
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2answers
3k views

Why is the decoder not a part of BERT architecture?

I can't see how BERT makes predictions without using a decoder unit, which was a part of all models before it including transformers and standard RNNs. How are output predictions made in the BERT ...
5
votes
1answer
173 views

Transformer decoder output - how is it linear?

I'm not quite sure how's the decoder output is flattened into a single vector. As from my understanding, if we input the encoder with a length N sentence, it's output is N x units (e.g. N x 1000), and ...
4
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1answer
1k views

Why does the transformer positional encoding use both sine and cosine?

In the transformer architecture they use positional encoding (explained in this answer and I get how it is constructed. I am wondering why it needs to use both sine and cosine though instead of just ...
4
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1answer
1k views

Transformer-based architectures for regression tasks

As far as I've seen, transformer-based architectures are always trained with classification tasks (one-hot text tokens for example). Are you aware of any architectures using attention and solving ...
3
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1answer
290 views

Attention for time-series in neural networks

Neural networks in many domains (audio, video, image text/NLP) can achieve great results. In particular in NLP using a mechanism named attention (transformer, BERT) have achieved astonishing results - ...
3
votes
2answers
166 views

Attention mechanism in Tensorflow 2

In the past days, I read up on the theory behind attention, when to apply it and what types there are. I think I have a decent first understanding of the concept, but now I would like to apply some of ...
3
votes
1answer
3k views

SymbolicException: Inputs to eager execution function cannot be Keras symbolic tensors

I am writing Encoder-Decoder architecture with Bahdanau Attention using tf.keras with TensorFlow 2.0. Below is my code This is working with TensorFlow 1.15 but getting the error in 2.0. you can check ...
3
votes
0answers
700 views

How to train tensorflow's transformer model on my own data?

https://github.com/tensorflow/models/blob/master/official/transformer has an implementation of transformer model. I want to train the model on my own data(consisting of two files, src.txt, and tgt.txt)...
3
votes
2answers
2k views

Keras Attention Guided CNN problem

I am working on a CNN for XRay image classification and I can't seem to be able to properly train it. I am trying to implement the following paper in Keras: https://arxiv.org/pdf/1801.09927.pdf In ...
3
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0answers
2k views

Why does Position Embeddings work?

In the papers "Convolutional Sequence to Sequence Learning" and "Attention Is All You Need", positions embeddings are simply added to the input words embeddings to give the model a sense of the order ...
2
votes
2answers
948 views

Does BERT use GLoVE?

From all the docs I read, people push this way and that way on how BERT uses or generates embedding. I GET that there is a key and a query and a value and those are all generated. What I don't know ...
2
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2answers
986 views

How do Bahdanau - Luong Attentions use Query, Value, Key vectors?

In the latest TensorFlow 2.1, the tensorflow.keras.layers submodule contains AdditiveAttention() and ...
2
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3answers
1k views

Any good Implementations of Bi-LSTM bahdanau attention in Keras?

From past few weeks I'm trying to learn sequence to sequence machine translation modelling but I couldn't find any good examples/tutorials with bahdanau attention implemented. I did come across a ton ...
2
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1answer
79 views

Explanation about i//2 in positional encoding in tensorflow tutorial about transformers

I was implementing the transformer architecture in tensorflow. I was following the tutorial : https://www.tensorflow.org/tutorials/text/transformer#setup_input_pipeline They implement the positional ...
2
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1answer
40 views

Does multi-head attention remove the need for self-attention?

The title may be confusing but suppose I were to build Transformer Neural Network with a masking network that utilizes multi-head attention (like that in SepFormer), would adding self-attention in the ...
2
votes
1answer
37 views

Nutritional image classification task

I need a model that is able to receive as input an image of a nutritional information chart and tell the level of sugar that the product has. It would be a 3-class classification problem (low if sugar ...
2
votes
1answer
77 views

Transformer masking during training or inference?

I'm working through Attention is All you Need, and I have a question about masking in the decoder. It's stated that masking is used to ensure the model doesn't attend to any tokens in the future (not ...
2
votes
1answer
2k views

ValueError: Cannot convert a partially known TensorShape to a Tensor: (?, 256)

I'm working on a sequence to sequence approach using LSTM and a VAE with an attention mechanism. ...
2
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0answers
328 views

Keras value error: Operands could not be broadcast with with shapes(100,100) - GRU

I am trying to use Hierarchical Attention Networks for classification of news articles using 20 newsgroup dataset that i downloaded from the internet. I came across this code of the implementation and ...
1
vote
1answer
271 views

Can BERT be used for predicting words?

I have a question regarding the pre-training section (in particular, the Masked Language Model). In the example Let's stick to improvisation in this skit, by masking the word improvisation, after ...
1
vote
1answer
23 views

Why are convolutions still used in some Transformer networks for speech enhancement?

So I’ve read in Attention is All You Need that Transformers remove the need for recurrence and convolutions entirely. However, I’ve seen some TNNs (such as SepFormer, DPTNet, and TSTNN) that still ...
1
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1answer
60 views

Why does an attention layer in a transformer learn context?

I understand the transformer architecture (from "Attention is All You Need"), as well as how the attention is computed in the multi-headed attention layers. What I'm confused on is why the ...
1
vote
2answers
2k views

What is the advantage of positional encoding over one hot encoding in a transformer model?

I'm trying to read and understand the paper Attention is all you need and in it, they used positional encoding with sin for even indices and cos for odd indices. In the paper (Section 3.5), they ...
1
vote
1answer
53 views

Decoder Transformer feedforward

I have a question about the decoder transformer feed forward during training. Let's pick an example: input data "i love the sun" traduction i want to ...
1
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1answer
35 views

Pytorch Luong global attention: what is the shape of the alignment vector supposed to be?

I am looking at the Luong paper on Attention models and global attention. I understand how the alignment vector is computed from a dot product of the encoder hidden state and the decoder hidden state. ...
1
vote
1answer
352 views

What would be the target input for Transformer Decoder during test phase?

The Transformer Decoder takes in two inputs, the encoder's output, and the target sequence. How the target is fed into the decoder has been provided in this answer I am having confusion about what ...
1
vote
2answers
141 views

Splitting into multiple heads — multihead self attention

So, I have a doubt in Attention is all you need: The implementation of transformers on tensorflow's official documentation says: Each multi-head attention block gets three inputs; Q (query), K (key), ...
1
vote
1answer
34 views

what is the difference between positional vector and attention vector used in transformer model?

what is the difference between positional vector and attention vector used in transformer model ? , i saw a video in youtue and the defintion for positional vector was give as :* "vector that ...
1
vote
1answer
452 views

What is the feedforward network in a transformer trained on?

After reading the 'Attention is all you need' article, I understand the general architecture of a transformer. However, it is unclear to me how the feed forward neural network learns. What I learned ...
1
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1answer
149 views

What is difference between attention mechanism and cognitive function?

How are attention mechanism used in different deep learning algorithms different from the cognitive function attention?
1
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2answers
207 views

Attention Mechanism: Why use context vector instead of attention weights?

In attention, the context vector ($c$) is derived from the sum of the attention weights ($\alpha$) multiplied by the encoder hidden states ($h$), where the weights are obtained by multiplying the ...
1
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0answers
19 views

How to use in built Keras ADDITIVE ATTENTION Layer for image captioning?

I have Designed an Encoder-Decoder Model for Image Captioning. Now, I want to improve my Model. So, I thought of putting an Attention Layer in my Encoder-Decoder model. But, I am struggling with how ...
1
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0answers
19 views

SAGAN - what is the correct architecture?

Hi, in the original paper the following scheme of the self-attention appears: https://arxiv.org/pdf/1805.08318.pdf In a later overview: https://arxiv.org/pdf/1906.01529.pdf this scheme appears: ...
1
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1answer
76 views

How do attention mechanism in CNN for images?

I read some attention mechanism papers, but I could not understand how it can be applied to an image (classification, detection, etc) using a CNN model. How does it affect the alignment scores and the ...
1
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0answers
24 views

how is the linear relation between positional encoding helping attention?

I'm reading the annotated transformer, and interested in the mechanics behind the positional encoding. I understand the linear relation between position $t$ and position $t+\phi$, and understand that ...
1
vote
2answers
91 views

Role of decoder in Transformer?

I understand the mechanics of Encoder-Decoder architecture used in the Attention Is All You Need paper. My question is more high level about the role of the decoder. Say we have a sentence translation ...
1
vote
1answer
27 views

Working Behavior of BERT vs Transformers vs Self-Attention+LSTM vs Attention+LSTM on the scientific STEM data classification task?

So I just used BERT pre-trained with Focal Loss to classify Physics, Chemistry, Biology and Mathematics and got a good f-1 macro of 0.91. It is good given it only had to look for the tokens like ...
1
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0answers
58 views

What is the difference between additive and multiplicative attention? [closed]

This paper (https://arxiv.org/abs/1804.03999) implements additive addition. I think the attention module used in this paper (https://arxiv.org/abs/1805.08318) is an example of multiplicative attention,...
1
vote
1answer
20 views

Predicting point sequence in image

My training set is a set of images (either 3 channel or 1 ofc i use only one type of channel). And the labels are a sequence of points in a specific order that i want to predict from the images. I am ...
1
vote
1answer
670 views

How to add a Decoder & Attention Layer to Bidirectional Encoder with tensorflow 2.0

I am a beginner in machine learning and I'm trying to create a spelling correction model that spell checks for a small amount of vocab (approximately 1000 phrases). Currently, I am refering to the ...
1
vote
1answer
64 views

How to add attention mechanism to my sequence-to-sequence architecture in Keras?

Based on this blog entry, I have written a sequence to sequence deep learning model in Keras: ...