Questions tagged [attention-mechanism]
The attention-mechanism tag has no usage guidance.
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Custom Simulator for Deep Reinforcement Learning
I am trying to develop a control method for a specific process in industry. I have a time-series of data for the process and want to develop a prediction model base on attention mechanism to estimate ...
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How to Visualize attention weights in a Attention based Encoder-Decoder network in Time series forecasting
Below is one example Attention-based Encoder-decoder network for multivariate time series forecasting task. I want to visualize the attention weights.
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Do the multiple heads in Multi head attention actually lead to more parameters or different outputs?
I am trying to understand Transformers. While I understand the concept of the encoder-decoder structure and the idea behind self-attention what I am stuck at is the "multi head part" of the &...
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Document understanding - sentence length prediction
the subject is taken almost verbatim from this paper https://arxiv.org/pdf/2108.02923.pdf. One of the tasks , is to be able to tell, in a document, if 2 words are part of the same phrase. For e.g. if ...
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How to implement Early stopping in Neural Machine Translation with Attention or Transformers?
I am trying to implement early stopping to my model where I am performing Machine Translation using Seq2Seq with attention. I am mostly used to writing my own models in steps, something like this:
<...
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Input 0 is incompatible with layer repeat_vector_40: expected ndim=2, found ndim=1
I am developing an LSTM autoencoder model for anomaly detection. I have my keras model setup as below:
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For an LSTM-based seq2seq model, is reversing the input still necessary or advised when using attention?
The original seq2seq paper reversed the input sequence and cited multiple reasons for doing so. See:
Why does LSTM performs better when the source target is reversed? (Seq2seq)
But when using ...
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Positional encoding without input embedding
Does it make sense to use a positional encoding in attention when the input tokens do not go through an embedding layer?
In NLP models, the embedding maps a word to real numbers. ...
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How to extract attention weight from MultiHeadAttention layer in Keras?
How to extract attention weights from MultiHeadAttention layer in Keras?
With the attention weights, I hope to plot attention heatmaps.
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How to use the keras.layers.AdditiveAttention correctly?
My understanding on the topic is superficial at best, so do bear with me. I have a couple questions (specifically on how to use keras.layers.AdditiveAttention) which I hope is suitable to be asked ...
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keras_self_attention vs attention layer
can i get a help pleas what is the diffreent btween if i bulid attention class like this to my Bi LSTM MODEL and keras_self_attention ?
from tensorflow.keras.layers import Layer
from tensorflow.keras ...
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Why does Bahdanau Attention Have to be Causal?
Using the Bahdanau attention layer on Tensorflow for time series prediction, although conceptually it is similar to NLP applications.
This is how the minimal example code for a single layer looks like....
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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 ...
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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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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 ...
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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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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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nn.embedding alternative for float numbers
I have found this pytorch code of transformers suitable for machine translation:
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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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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 ...
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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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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 ...
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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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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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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 ...
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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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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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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 ...
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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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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 : ...
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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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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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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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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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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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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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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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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 ...
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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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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 ...
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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 ...
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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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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 ...
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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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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 ...
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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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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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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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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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