Questions tagged [sequence-to-sequence]
The sequence-to-sequence tag has no usage guidance.
119
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Is this problem a time series regression or seq2seq regression or some other type of problem?
I measure sequences of 3 parameters in my system. 2 are independent and the 3rd dependent. Let's call the independent ones $x$ and $y$, and the dependent one $z$. They are each measured once per hour ...
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416
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How to Use Generative AI for Time Series Forecasting?
What I have
A time series dataset of time stamps (hourly resolution), some covariates (like store foot-traffic) and items sold.
What to forecast
Number of items sold for next 24 hours, i.e. 24 ...
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73
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Seq2seq Transformer Autoencoder returns same results when unfolded using only memory and initial value
I have numeric signals from two sensors, and I would like to create a mapping using sequence-to-sequence autoencoder. I used the transformer architecture, and it seems to be learning - the loss is ...
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Which language model to use for this use case? [Finetuning on custom dataset]
An example from my train data is as follows:
...
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Error in seq2seq translation when passing predicted output to rnn due to input shape not always being the same
I'm working on a language translator and I'm getting an error I'm unsure about.
During the decoding process when using argmax on the predicted output I am sometimes getting an RuntimeError ...
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26
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GRU as a Classifier
Hello Data Scientists,
I have sensor data dataset consists of (10 features and labels), the labels classify the data to (normal or 4 type of attacks). The Label is encoded (0,1,2,3,4). There is a ...
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25
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Pretrained model for RNN Encoder-Decoder?
Our team are implementing a paper called Cold-Start-Reinforcement-Learning-with-Softmax-Policy-Gradient.
Although the paper didn't mention. We want to use a pre-trained model, which is a RNN Encoder-...
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537
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Why does a decoder generate all hidden states during inference?
Seems that in Vanilla transformers at least (a la AIAYN), during inference time, the hidden states are generated for all tokens in the input sequence, but only the last one is used to predict the next ...
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In abstractive document summarization task, if I have multiple target sequences for one input document, what is the ideal loss form?
I use autoregressive model like T5 to tackle the abstractive document summarization task. In my case there are multiple target summarizations for one input document. Is there some related works about ...
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75
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What is exactly the input to a second lstm layer?
I am often confused about the lstm with more than one layer.
Imagine i have two lstm layer with 3 cells each layer.
What is exactly the input to the second lstm layer ?
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658
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What are the advantages of autoregressive over seq2seq?
Why are recent dialog agents, such as ChatGPT, BlenderBot3, and Sparrow, based on the decoder architecture instead of the encoder-decoder architecture?
I know the difference between the attention of ...
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I can't figure out why even when training the seq2seq chatbot neural network, it doesn't give adequate answers
When training with 50 thousand pairs of questions and loss 0.2 accuracy 0.9 it does not give adequate answers
...
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182
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What loss function to use for predicting discrete output sequence given a discrete input sequence?
I am working on sequence-to-sequence tasks where the input is an n-length sequence of discrete values from a finite set S (say ...
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34
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Batch inference and seq2seq
I am following this guide to the Seq2Seq architecture. It is clear that the author suggests batch training during the teacher forcing step. However, during the inference step, the author restricts the ...
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742
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Requirements for variable length output in transformer
I have been working on modifying the transformer from the article The Annotated Transformer. One of the features I would like to include is the ability to pass a sequence of fixed length, and receive ...
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Which of these 2 approaches is the best route to learn to build a question answer chatbot?
Quick background on what I am trying to accomplish:
I have been working on a project in my company that requires about 300 people across the world to follow quite a large set of rules and guidelines. ...
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47
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Siamese network for a sequence-to-sequence generation
Shall I use the siamese network for a sequence-to-sequence generation problem in machine learning?
Eg:
Input 1: Sentence 1 (sequence)
Input 2: Sentence 2 (sequence)
Output: Newly Generated sentence (...
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56
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Dialogue history encoding for multi-turn dialogues using Seq2seq
In single-turn dialogue seq2seq models where the goal is to produce a good answer y to a query x, sentences are usually encoded such that x is fed to the encoder, while the decoder is only given a &...
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How to make a pipeline for Videos Dataset for TensorFlow [Sequence Matters] & train Model Effectively with Low Memory System
I am working on a Deep Learning project and I am facing an issue with the size of the dataset. I want to make a pipeline for video dataset [Sequence Matters]. Because if I try the load the whole ...
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54
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Forecasting of Images using ConvLSTM2D
I am working on a problem of seq2seq modelling using ConvLSTM2D layer in keras. Implementation of convLSTM in keras allows user to control over output sequence using 'return_sequence' option. When ...
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37
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RNN to model DNA sequencing classification
I have a DNA sequence dataset each mapped to a certain class.
e,g
TCAGCCGAGAGCTCATCGATCGTACGT 2
ATGCAGTGCATCGATCGATCGTAGAAC 3
Where the number after the sequence specifies the type of protein this ...
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334
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Seq2Seq loss function
I was reading the paper neural_approach_conversational_ai.pdf. And in the section Seq2Seq for Text Generation there is a formula that i feel a bit wrong
[1]: https://i.stack.imgur.com/sX0it.png
Can ...
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57
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String together a set of tokens into a sequence
I have this problem scenario - Given a set of tokens, string them or a subset of the tokens together using stop words into a sequence. I am clear that I can have potentially infinite pre-training data ...
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472
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What happens when the length of input is shorter than length of output in transformer architecture?
Given standard transformer architecture with encoder and decoder.
What happens when the input for the encoder is shorter than the expected output from the decoder?
The decoder is expecting to receive ...
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159
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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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149
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Predict sequence using seqGAN
I am trying to create a GAN model in which I am using this seq2seq as Generator and the following architecture as Discriminator:
...
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390
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Is it possible to target a specific output length range with BART seq2seq?
I'm currently working on an extractive summary model based on Facebook's BART model. Consistent absolute length output would be highly desirable. The problem is that input length may vary wildly. That ...
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210
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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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1
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19
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Why is sequence prediction always the objective in RNN and LSTM like algorithms
The title is pretty much my question. I haven't seen any literature yet that uses a different training objective. The goal is to find the hidden states eventually, then why is it that only 1 method is ...
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Why the LSTM on Keras does not work correctly when it is necessary to predict several steps forward
I used AirPassenger Dataset. And based on several previous values(for examples 20) I want to predict several(3 or 5) steps in future.
Like
X -> y
[10,20,30,....200]->[210,220,230]
[20,30,40,.......
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In keras seq2seq model, what is the difference between `model.predict()` and the inference model?
I am looking into seq2seq model in keras, for example, this blog post from keras or this. All the examples I have seen have some inference model, that depicts the original model. That inference model ...
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Sequence learning from farm operations data
I need to generalize a single sequence from N sequences entailing farming tasks/operations and ultimately plotting it on Gantt chart.
There are a total let's assume N sequences = n (total fields) * t (...
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210
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Multi-step forecasts of factory production data using a Seq2Seq Encoder-Decoder Model with Attention
I am attempting to use a Seq2Seq model to make forecasts of factory production data using an Encoder-Decoder model augmented with Attention. I have become a little stuck as the output of the model ...
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740
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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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41
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Preprocess multi-sample time series data: encode each sample separately or in aggregate?
Let's say I have 3 dense sequences of uniform length. Should I fit a scaler on them separately or together?
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Workaround / fallback value for tfp.distributions.Categorical.log_prob in tensorflow graph mode
Is there a way to avoid tfp.distributions.Categorical.log_probraising an error if the input is a label out of range?
I am passing a batch of samples to the ...
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1k
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Calculating confidence score in NER
I am working on a problem on Named Entity Recognition. Given a text, my model is detecting the Named Entities and extracting that info for the end-user. Now the ask is end-user needs a confidence ...
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Multi-output, multi-timestep sequence prediction with Keras
I've been searching for about three hours and I can't find an answer to a very simple question.
I have a time series prediction problem. I am trying to use a Keras LSTM model (with a Dense at the end) ...
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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 to implement sequence to sequence models?
I have a dataset with patient demographics, diagnosis history, hospital visit dates, drugs consumed etc.
All these events have time stamp information (except static info like demographics such gender, ...
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Pytorch: understanding the purpose of each argument in the forward function of nn.TransformerDecoder
According to https://pytorch.org/docs/stable/generated/torch.nn.TransformerDecoder.html, the forward function of nn.TransformerDecoder contemplates the following arguments:
tgt – the sequence to the ...
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106
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Different training method for encoder-decoder model
Trying to learn the encoder-decoder model for some NLP problems.
I am referring to this Keras tutorial.
During the model training phase, this tutorial just uses the following:
...
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Minimal working example or tutorial showing how to use Pytorch's nn.TransformerDecoder for batch text generation in training and inference modes?
I want to solve a sequence-to-sequence text generation task (e.g. question answering, language translation, etc.).
For the purposes of this question, you may assume that I already have the input part ...
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Dummy Variables of Weights in RNN Backpropagation Through Time
In the deep learning book RNN chapter (https://www.deeplearningbook.org/contents/rnn.html), it is mentioned that -
To resolve this ambiguity, we introduce dummy variables $W^{(t)}$ that are defined to ...
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2k
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How does the Transformer predict n steps into the future?
I have barely been able to find an implementation of the Transformer (that is not bloated nor confusing), and the one that I've used as reference was the PyTorch implementation. However, the Pytorch ...
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525
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Difference between zero-padding and character-padding in Recurrent Neural Networks
For RNN's to work efficiently we vectorize the problem which results in an input matrix of shape
(m, max_seq_len)
where m is the number of examples, e.g. ...
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2
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377
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Timeseries LSTM: does test data need to come after training data?
I have one single, very long time series. I want to train an LSTM to distinguish between two behaviours (A or B) at every timestep (sequence-to-sequence).
Because the time series is very long, I plan ...
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2k
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How to use BERT in seq2seq model? [closed]
I would like to use pretrained BERT as encoder of transformer model. The decoder has the same vocabulary as encoder and I am going to use shared embeddings. But I need ...
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267
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Build a corpus for machine translation
I want to train an LSTM with attention for translation between French and a "rare" language. I say rare because it is an african language with less digital content, and especially databases ...
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Long range forecasting with sequence-to-sequence models
I have a task where I want to forecast daily observations for 1 year or 2 years in advance at multiple locations--so 365 or 730 days in advance. I actually have a pretty good dataset, meaning daily ...
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