Questions tagged [attention-mechanism]
The attention-mechanism tag has no usage guidance.
76
questions
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1answer
20 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 ...
0
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1answer
18 views
Attention in NER
I am building a named entity recognition model and it is having BERT+BiLSTM+CRF in it. Now I am planning to introduce an attention layer. My question is - what type of attention I should use here and ...
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0answers
10 views
Language Model with Attention not learning
Language model with attention layer is not learning after 20 epochs. Both training and validation loss increase together, while the accuracy flattens at around 7%
The way input data is pipelined is by ...
1
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1answer
62 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 ...
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1answer
16 views
Transformer architecture question
I am hand-coding a transformer (https://arxiv.org/pdf/1706.03762.pdf) based primarily on the instructions I found at this blog: http://jalammar.github.io/illustrated-transformer/.
The first attention ...
2
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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 ...
0
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1answer
28 views
How do the linear layers in the attention mechanism work?
I think I now the answer to my question but I dont really get confirmation.
When taking a look at the multi-head-attention block as presented in "Attention Is All You Need" we can see that ...
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0answers
22 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 ...
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2answers
194 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 ...
1
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1answer
22 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. ...
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0answers
17 views
Basic of the attention mechanism
Background
Having gone through articles.
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Attention in Neural Networks
Visualizing A Neural Machine Translation Model (...
0
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1answer
30 views
Predict customer behaviour with Transformer(attention is all you need)
Please advice, am I thinking correctly: is it possible to represent customer behavior data from an online store as a sequence data? Because it is describing interactions of the customer with the shop ...
3
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1answer
244 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 - ...
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2answers
67 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 ...
0
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1answer
65 views
Why this TensorFlow Transformer model has Linear output instead of Softmax?
I am checking this official TensorFlow tutorial on a Transformer model for Portuguese-English translation.
I am quite surprised that when the Transformer is created, their final output is a Dense ...
0
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0answers
10 views
Using Transcoder Model for language to language conversion
I have a problem statement like Converting deprecated code into a modern version of the same language. I'm currently converting with a custom Rule-based engine. But the modern version of the language ...
1
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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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1answer
47 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 ...
0
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2answers
39 views
Understanding Transformer's Self attention calculations
I was going through this link: https://www.analyticsvidhya.com/blog/2019/06/understanding-transformers-nlp-state-of-the-art-models/?utm_source=blog&utm_medium=demystifying-bert-groundbreaking-nlp-...
0
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1answer
37 views
Is a dense layer required for implementing Bahdanau attention?
I saw that everyone adds Dense( ) layer in their custom Bahdanau attention layer, which I think isn't needed.
This is an image from a tutorial here. Here, we are just multiplying 2 vectors and then ...
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0answers
20 views
Question about Relative-Position-Representation code
In https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/layers/common_attention.py
In _relative_attention_inner method, which I think is one of the ...
0
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1answer
29 views
Practical attention models
Attention is all you need is a nice paper that suggests using
positional encodings as an alternative to RNNs in their Transformer architecture.
GPT-2 and GPT-3 are examples of using this architecture ...
1
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1answer
232 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 ...
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0answers
48 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,...
2
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1answer
52 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 ...
1
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2answers
70 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), ...
0
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2answers
101 views
What are the hidden states in the Transformer-XL? Also, how does the recurrence wiring look like?
After exhaustively reading the many blogs and papers on Transformers-XL, I still have some questions before I can say that I understand Transformer-XL (and by extension XLNet). Any help in this regard ...
2
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1answer
76 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 ...
0
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1answer
84 views
NLP Transformers - understanding the multi-headed attention visualization (Attention is all you need)
I am new to NLP and I just finished reading the paper "Attention is all you need".
I'm struggling to understand the interpretability of the multi-headed attention, and specifically how these ...
1
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1answer
32 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 ...
0
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1answer
19 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 ...
0
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1answer
65 views
How to understand Inconsistent and ambiguous dimensions of matrices used in the Attention layer?
Attention-scoring mechanism seems to be a commonly-used component in various seq2seq models, and I was reading about the original "Location-based Attention" in Bahadanau well-known paper at https://...
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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
131 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 ...
0
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1answer
33 views
Is the number of bidirectional LSTMs in encoder-decoder model equal to the maximum length of input text/characters?
I'm confused about this aspect of RNNs while trying to learn how seq2seq encoder-decoder works at https://machinelearningmastery.com/configure-encoder-decoder-model-neural-machine-translation/.
It ...
1
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1answer
559 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
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1answer
52 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:
...
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0answers
9 views
how many spectogram frames per input character does text-to-speech (TTS) system Tacotron-2 generate?
I've been reading on Tacotron-2, a text-to-speech system, that generates speech just-like humans (indistinguisahble from humans) using the github https://github.com/Rayhane-mamah/Tacotron-2.
I'm very ...
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2answers
745 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 ...
1
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1answer
29 views
Attention network without hidden state?
I was wondering how useful the encoder's hidden state is for an attention network. When I looked into the structure of an attention model, this is what I found a model generally looks like:
...
1
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1answer
238 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
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2answers
92 views
What are good toy problems for testing Transformer architectures?
I am testing various variants for Transformers and Transformer architectures. But training on full language tasks is a rather time consuming affair. What are good toy problems to test if a transformer ...
1
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2answers
44 views
In “Attention Is All You Need”, why are the FFNs in (2) the same as two convolutions with kernel size 1?
In addition, why do we need a FFN in each layer when we already have attention?
Here's a screenshot of the relevant section from Vaswani et al. (2017):
0
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1answer
107 views
Attention model with seq2seq over sequence
On the official tensorflow page there is one exmple of a decoder (https://www.tensorflow.org/tutorials/text/nmt_with_attention#next_steps):
...
1
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1answer
31 views
Attention to multiple areas of same sentence
Lets consider some sentences below:
"Datascience exchange is a wonderful platform to get answers to datascience related queries and it helps to learn various concepts too"
"Can company1 buy company2? ...
2
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2answers
823 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 ...
3
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1answer
910 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 ...
0
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3answers
581 views
How are Q, K, and V Vectors Trained in a Transformer Self-Attention?
I am new to transformers, so this may be a silly question, but I was reading about transformers and how they use attention, and it involves the usage of three special vectors. Most articles say that ...
1
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1answer
361 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 ...
3
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2answers
145 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 ...
