Questions tagged [attention-mechanism]
The attention-mechanism tag has no usage guidance.
119
questions
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Self Attention vs LSTM with Attention for NMT
I am trying to compare the
A: Transformer based architecture for Neural Machine Translation (NMT) from the Attention is All You Need paper with
B: an architecture based on Bi-directional LSTM's in the ...
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0answers
8 views
Pytorch different result when using `torch.matmul` and `for-loop` to pass input through linear layers
I have been at this for about two days now. I am working on a model that takes on an input x and passes it through several linear layers, and concatenates the ...
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8 views
How to reference length of input sequence inside Keras model?
Basically I am trying to implement a "Transformer-like" architecture for physiological time-series classification. One of the specific design criteria is for the model to be able to process ...
1
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1answer
34 views
Multioutput prediction using LSTM encoder decoder with Attention
(I am working on Jupter notebook with python version 3.6.12, running Tensorflow 2.4.0 version.)
I have a dataset that consists of 5 input features and 3 output features (that requires to be predicted)....
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16 views
Can a reformer model really handle long-range dependency?
I read this article about new attention model called Reformer. Here is the main strength of this model:
The Reformer pushes the limit of longe sequence modeling by its ability to process up to half a ...
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60 views
nn.embedding alternative for float numbers
I have found this pytorch code of transformers suitable for machine translation:
...
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1answer
10 views
Where do Q vectors come from in Attention-based Sequence-to-Sequence Transformers?
I'm taking a course on Attention-based NLP but I'm not understanding the calculation and application of Attention, based on the use of Q, K, and V vectors. My understanding is that the K and V ...
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0answers
15 views
Attention to get context of words
The W2V techniques define context as a window of k words around the term, and using this learn the vector representations for words in the corpus. Attention networks can help us get the important ...
1
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1answer
46 views
How is attention different from linear MLPs?
Each output for both the attention layer (as in transformers) and MLPs or feedforward layer(linear-activation) are weighted sums of previous layer. So how they are different?
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33 views
Empty prediction with keras Seq2Seq with attention mechanism
I have a simply seq2seq model with attention mechanism in keras. My problem is that the inference model only gives me empty prediction. However, if I remove the attention it suvessfully gives me the ...
3
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1answer
43 views
What reccent alternatives to LSTM are there for regression problems?
I have been working for a while on a regression problem - predicting the air pollution in a city based on meteorological features (humidity, temperature, wind velocity a.o.). I have trained an LSTM ...
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10 views
Rstudio Keras Attention manual calculation disagrees with the layer output
I am trying to reproduce the math inside the Keras Attention (dot-product), based on Rstudio Keras wrapper.
In R I use the ouput of an LSTM layer: sequence representation (query) and last hidden state ...
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1answer
77 views
time series anomaly detection
I want to ask for time series anomaly detection we can apply tnn on multiple features or not?
I used transformer for sentiment analysis where I have to provide a sentence and it predicts its output as ...
0
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1answer
50 views
Self-Attention Summation and Loss of Information
In self-attention, the attention for a word is calculated as:
$$
A(q, K, V) = \sum_{i} \frac{exp(q.k^{<i>})}{\sum_{j} exp(q.k^{<j>})}v^{<i>}
$$
My question is why we sum over the ...
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0answers
12 views
How to prepare data for TpyTorch's 3d attn_mask argument in MultiHeadAttention
I'm currently trying to implement an Encoder-Decoder architecture for text summarization based on Transformers. Thus I need ti apply MultiHeadAttention on the Decoder site of the model. Since I want ...
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0answers
14 views
Which layer actually contains attention and how to map with input for classification scenario
I am writing a binary classification case and want to use Attention. The code is here - Code and model graph is shown below with attention area highlighted .
I want to visualize which which word is ...
0
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1answer
81 views
Does Keras MultiHeadAttention with 1 head equals normal self attention?
Keras multihead attention if used as single head num_heads=1, then how is it different than Keras Attention ?
Also, Is multihead attention by default self-...
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40 views
How to use flatten with SeqSelfAttention
I want to use SeqSelfAttention , but in final layer the dimension need to be reduced. However, adding Flatten gives following error : ...
1
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1answer
66 views
Why does Keras only have 3 types of attention layers?
The Keras library list only has 3 types of attentions - keras attention layers, which are :
MultiHeadAttention layer
Attention layer
AdditiveAttention layer
However, in theory there are multiple ...
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20 views
0
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1answer
53 views
why multiple attention heads learn differently
In transformer architecture multi head attention blocks are used. While visualizing their output it can be seen that every layer has learnt different relations of words. e.g., layer 5 has learnt that &...
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1answer
42 views
Attention weights - change during learning and prediction
Assume a simple LSTM Followed by Attention layer or a full transformer architecture. The attention weights are learnt during training, which get multiplied with keys, queries and values.
Please ...
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0answers
62 views
Struggling to understand/implement Transformer Decoder
I'm struggling to understand the decoder in a Transformer model, specifically with regards to some aspects of its architecture as well as how it actually handles the data during training.
What I have ...
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2answers
64 views
How to encode a sentence using an attention mechanism?
Recently, I read about one of the state-of-the-art method called Attention models. This method use a Encoder-Decoder model. It can find a better encoding for each word in a sentence. But how can I ...
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0answers
34 views
An algorithm to extract the purpose of a document
I want to build an algorithm to extract the purpose of the document (scientific papers for example) by extracting the sentences that state the purpose. I don't have many annotated data so I might use ...
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0answers
19 views
Why do we need dot product as part of the Transformer's training process?
I do understand that dot product conveys the meaning of similarity in a vector space. At the same time it looks like during the training process we are learning the weights( or how much attention) ...
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0answers
64 views
Understanding difference between benefits of having multi-head attention and of the process of learning Q,K,V embeddings in a single head
From reading different papers and blogposts I got an understanding that learning embeddings with a single attention head and with multiple heads serves essentially the same purpose.
In this blogpost ...
1
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1answer
105 views
In Transformer's multi-headed attention, how attending "different representation subspaces at different positions" is achieved?
Question partially inspired by this post about the need of multi-head attention mechanism.
For me though it is still not clear how we will be able to initialise those attention heads in a diverse way(...
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0answers
14 views
Computing Attention on multi-dimensional sequences?
Is it possible to compute attention/adapt existing transformer architectures (like longformer) to be used on multi-dimensional sequence input?
As in, instead of a ...
2
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1answer
172 views
What exactly is the linear layer in the transformer model?
Please see this image:
There are linear layers to modify the Query, key and value matrices and one linear layer after the multi head attention as they mention also from here:
Are these linear layers ...
1
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1answer
37 views
Can the attention mask hold values between 0 and 1?
I am new to attention-based models and wanted to understand more about the attention mask in NLP models.
attention_mask: an optional torch.LongTensor of shape [...
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0answers
43 views
Visualization of SeqSelfAttention layer from Keras
Can someone help me to find, explain and visualize the SeqSelfAttention layer from Keras. I found a lot of flowchart that use the figures from the "attention is all you need paper" where ...
1
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1answer
137 views
A simple attention based text prediction model from scratch using pytorch
I first asked this question in codereview SE but a user recommended to post this here instead.
I have created a simple self attention based text prediction model using pytorch. The attention formula ...
2
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0answers
39 views
How does attention for feature fusion works
I am struggling to understand how would a self-attention layer be used for features of different modalities fusion.
What I understand until now is that :
Every unique modality is fed into a self-...
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0answers
28 views
Output shapes of Keras AdditiveAttention Layer
Trying to use the AdditiveAttention layer in Keras. On manual implementation of the layer from tensorflow tutorial https://www....
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0answers
23 views
Feature fusion multiple modalities example
I am trying to replicate this paper. In their architecture, there is an attention-fusion subnet explained as follows:
In the multimodal activity recognition problem, not all modalities are
equally ...
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0answers
19 views
Feature fusion with attention layer
I have three different modalities, each of which has a shape of (n-users, m-timestep, p-features). I want to do feature level fusion, by adding an attention layer that would compute the scores of ...
1
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1answer
44 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 ...
2
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1answer
113 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 ...
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0answers
49 views
How does multi-head attention on "multiple attention axes" works?
I would like to apply an self-attention mechanism on a multichannel audio spectrogram, so a 3D tensor. In the original Transformer paper, self-attention is applied on vector (embedded words) within a ...
0
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2answers
38 views
Intuition of "Head" in Attention models (Transformer)?
I keep seeing the "head" in attention models (transformers). Aside from the mathematical formula, could anyone please share the intuition behinds the idea "head" ? Thanks a lot!
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183 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 ...
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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:
...
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0answers
130 views
Why do relative positional embeddings instead of absolute positional encoding improve the Transformer?
I've been researching the Music Transformer, the paper for which introduced an efficient algorithm to compute Relative Positional Embeddings in a Transformer. I know that Relative Positional ...
0
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1answer
30 views
There could be a problem with the linear layer after the attention inside a transformer?
My question regards this image:
It seems that after the multi head attention there is a linear layer as they mention also from here:
the linearity is given by the weights W^{o}. my quesion is: for ...
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1answer
2k views
Class token in ViT and BERT
I'm trying to understand the architecture of the ViT Paper, and noticed they use a CLASS token like in BERT.
To the best of my understanding this token is used to gather knowledge of the entire class, ...
1
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1answer
102 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
80 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
17 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 ...
2
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1answer
117 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 ...
