Questions tagged [lstm]

For questions about LSTM (long-short term memory) networks.

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Why doesnt my lstm model for time series prediction improve after certain level of performance?

I created an lstm model which predicts multioutput sequeances. It takes variable length sequences as input. These sequences are padded with zero to obtain equal length. Note that the time series are ...
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20 views

Custom optimizer and word-vector evaluator lstm

I’m using Keras LSTM layers and building a model that is trained off ethics text. I have a problem of often over fitting (the network basically remembers my input corpus as it is very small). I was ...
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13 views

Keras CRNN implementation with multiple input images

Hello I am trying to implement a CRNN with multiple input images (in my context it is 6 images) This is a regression problem and output is two real value. And for the CNN block I am thinking of using ...
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21 views

How Seq2Seq with Bidirectional RNN works?

First of all the scope of the question is as follows - we have Sequence2Sequence architecture with: Decoder: Bidirectional LSTM Encoder: regular (single directional) LSTM What I know: When you ...
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78 views

Why can we approximate the joint probability distribution using the output vector of an LSTM?

In the paper, Contextual String Embeddings for Sequence Labeling, the authors state that \begin{equation} P(x_{0:T}) = \prod_{t=0}^T P(x_t|x_{0:t-1}) \end{equation} They also state that, in the LSTM ...
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1answer
32 views

Transformer based decoding

Can the decoder in a transformer model be parallelized like the encoder? As far as I understand the encoder has all the tokens in the sequence to compute the self-attention scores. But for a decoder ...
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41 views

LSTM Architecture

There is plenty of literature describing LSTMs in a lot of detail and how to use them for multi-variate or uni-variate forecasting problems. What I couldn't find though, is any papers or discussions ...
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56 views

Time series RNN vs DNN

Understandably RNNs are very good at solving problems involving audio, video and text processing due to arbitrary input length of this sort of data. What I don't understand is why RNNs are also ...
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1answer
15 views

Error using Keras clear_session while multiple calibration

I am working through the tutorial here. I just cleaned up the code for myself but other than that code is as in original tutorial. I am using the pollution data from the following link When I try to ...
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1answer
72 views

Why do we need multiple LSTM units in a layer?

What is the point of having multiple LSTM units in a single layer? Surely if we have a single unit it should be able to capture (remember) all the data anyway and using more units in the same layer ...
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1answer
21 views

Modifying LSTM to include forecast

I am looking at LSTM example here However, I am not sure how to modify the setup if I have forecast available (assuming perfect forecast) for TEMP: Temperature and PRES: Pressure at time t. i.e. <...
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47 views

Details of implementing an LSTM in Reinforcement Learning

I'm currently looking into the context of adding an LSTM to my PPO pytorch implementation. My plan is to add one LSTM layer right after the last convolutional layer. I'm wondering now whether it is ...
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1answer
123 views

Object IN/OUT counting using CNN+RNN

I am building a video analytics program for counting moving things in a video. I am detecting bicycles and nothing else. I run object detection using the SSD mobile-net model in all the frames and ...
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32 views

Unsupervised LSTM

I have a big amount of light curves (image below) and I am trying to label the points as signal or background (the signal appears usually periodically, several times, for a given light curve). However,...
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12 views

Benefits in using multiple LSTM layers?

I am working on a time series forecasting problem and I am in the process of choosing the optimum network structure. Currently I have a 200 cell LSTM layer fully connected to 100 neurons in an ...
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7 views

Training LSTM with class imbalance

I need to train an LSTM on some light curves, in order to find a signal (there are 2 classes signal and background). However the signal (data points corresponding to signal) is around 100 times less ...
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80 views

What can be considered a deep recurrent neural network?

In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the DRQN is described as DQN with the first post-convolutional fully-connected layer replaced by a recurrent LSTM. I have DQN ...
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38 views

RNN: Different test results on balanced and unbalanced data

I trained a recurrent neural network (if it matters - it contains three CuDNNLSTM cells and 3 Dense layers, Dropout = 0.2). The result of data preparation is one array of ~330.000 sequences. Each ...
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26 views

How do the relative number of cells between neighboring stacked LSTM layers affect the network's behavior?

It seems that stacking LSTM layers can be beneficial for some problem settings in order to learn higher levels of abstraction of temporal relationships in the data. There is already some discussion on ...
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1answer
41 views

DQN Q-values are static

I am working on a DDQN with 5 LSTM layers and 3 actions as output and state space of 21 features. I am dividing the dataset into episodes of 720 timesteps, for each episode the agent acts greedily for ...
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18 views

Multi-field text input for LSTM

I'm using LSTM to categorize medium-sized pieces of text. Each item to be categorized has several free-form text fields, in addition to several categorical fields. What is the best approach to using ...
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17 views

Why validation performance is unstable for my LSTM based model (labelling problems)?

I have trained a recurrent neural network based on 1 stack of LSTM cells. I use it to solve a classification problem. The RNN cell has 48 hidden states. The output of the last unfolded LSTM cell is ...
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1answer
76 views

LSTM is not converging

I am writing my first LSTM network and I would really appreciate if someone can tell me if it is right (the loss seems to go down very slowly and before playing around with hyper parameters I want to ...
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35 views

Predicting sine using LSTM: Small output range and delayed output?

I have coded a very basic LSTM with forget gates (no libraries used). I'm trying to predict $0.5*sin(t + N)$ given $0.5*sin(t)$ as an exercise. I have tweaked the model, changing the output layer ...
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18 views

How to train chat bot on infinite non-stationary data?

I have continual simulated data of million sentences of two simulated persons talking to each other in a room and I want to model one of the persons speech. Now, during this period things in the room ...
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135 views

Experiment shows that LSTM does worse than Random Forest… Why?

LSTM is supposed to be the right tool to capture path-dependency in time-series data. I decided to run a simple experiment (simulation) to assess the extent to which LSTM is better able to understand ...
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1answer
57 views

Is my Neural Network program fully connected?

I have the following program for my neural network: ...
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30 views

How does a neural network output text box location data?

I'm interested in creating a convolutional neural network or LSTM to locate text in an image. I don't want to OCR the text yet, just find the text regions. Yes, I know Tesseract and other systems can ...
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23 views

Kera's (normal) LSTM uses the GPU?

I'm running Kera's LSTM (not CuDNNLSTM) but I notice my GPU is under load. I need recurrent dropout, so I can only stick with <...
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34 views

how to estimate which item is bought most using AI

Problem: Maintaining the products is a big task for a retailer. If we can estimate using AI to predict which products will sell the most, we can maintain sufficient stocks of the product without ...
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1answer
70 views

LSTM language model not working

I am trying to use a Keras LSTM neural network for character level language modelling. As the input, I give it the last 50 characters and it has to output the next one. It has 3 layers of 400 neurons ...
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165 views

Number of nodes in hidden layer for LSTM

I am starting to learn LSTM by understanding how it is used for creating a char-RNN and had a fundamental question. Does the number of nodes in the hidden layer need to be the same as that of the ...
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2answers
30 views

Is there bidirection sequence-to-sequence neural machine translation?

I have heard about bidirectional RNN LSTM units (endcoders-decoders), but my question is - is there bidirectional neural machine translation, that uses A->B weights for the translation in the opposite ...
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1answer
38 views

Neurological interpretation of LSTMs

I am searching for an Interpretation of LSTMs and recurrent neural Networks within Cognitive Neuroscience. Is there a mechanism in the human brain, that works analog to LSTMs? How does Long-term ...
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1answer
23 views

Reduce the effect of excessive zeros

I am working on an autoregression problem where I use sequential LSTM. My target is well defined, but I think I am facing a problem with the features. As the features were non-stationary, then I ...
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1answer
151 views

Most efficient neural network for human activity recognition

A paper from machinelearningmastery.com on human activity recognition states that 1D convolutional neural networks work the best on classification of human activities using data from accelometer. But, ...
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33 views

How could I learn tree paths given word embeddings?

I need to map from a vector space representation onto a tree structure. A possible solution: given a word vector as input, produce a path in the tree from the root down to the node that most closely ...
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1answer
52 views

Price difference predictions curve almost vanished

With a team, we are studying how it is possible to predict the price movement with high-frequency. Instead of predicting the price directly, we have decided to try predicting price difference as well ...
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2answers
106 views

Price Movement Forecasting Issue

I am working on a project for price movement forecasting and I am stuck with poor quality predictions. At every time-step I am using an LSTM to predict the next 10 time-steps. The input is the ...
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22 views

How to wire up bi-directional LSTMs?

I am interested in creating the neural network architecture described in this recent paper. However, I am not sure how to get started. Does TFLearn or Keras allow us to wire the LSTM in such ways?
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20 views

If there are several computers on a subnet, can training time be reduced by distributing the work across them?

We have multiple computers and the ability to ssh between them. What are options using either Java, C/C++, JavaScript, or Python to distribute our learning tasks? We will be using DCNN, DQN, and LSTM ...
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35 views

How to understand marginal loglikelihood objective function as loss function (explanation of an article)?

I am reading article https://allenai.org/paper-appendix/emnlp2017-wt/ http://ai2-website.s3.amazonaws.com/publications/wikitables.pdf about training neural network and the loss function is mentioned ...
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1answer
91 views

Difficulty understanding Keras LSTM fitting data

I'm try to train a RNN with a chunk of audio data, where X and Y are two audio channels loaded into numpy arrays. The objective is to experiment with different NN designs to train them to transform ...
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89 views

Structure of a multilayered LSTM neural network?

I implemented a LSTM neural network in Pytorch. It worked but I want to know if it worked the way I guessed how it worked. Say there's a 2-layer LSTM network with 10 units in each layer. The inputs ...
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1answer
1k views

Where can I find the original paper that introduced RNNs?

I was able to find the original paper on LSTM, I was not able to find the paper that introduced "vanilla" RNNs. Where can I find it?
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1answer
66 views

Why would giving my AI more data make it perform worse?

So I trained an AI to generate shakespeare, which it did somewhat well. I used this 10,000 character sample. Next I tried to get it to generate limericks using these 100,000 limericks. It generated ...
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1answer
85 views

LSTM RNN structure

I am currently looking into LSTMs. I found this nice blog post, which is already very helpful, but still, there are things I don't understand, mostly because of the collapsed layers. The input $X_t$,...
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1answer
4k views

How does LSTM in deep reinforcement learning differ from experience replay?

In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the author processed the Atari game frames with an LSTM layer at the end. My questions are: How does this method differ from the ...
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1answer
45 views

Using different timesteps for features and target value

I would like to know whether it's wrong; when working with time series data; to use daily prices as features and the price after 3 days as target. Is this correct or should I use the next-day price ...
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1answer
88 views

What would be the best approach to teach an AI to learn how to “sing” along a beat?

I have heard and read about HyperGAN, LSTM and a few other techniques, but I have a hard time piecing the overall concept together. End Goal Being able to input an instrumental and get an output of ...