Questions tagged [lstm]
LSTM stands for Long Short-Term Memory. When we use this term most of the time we refer to a recurrent neural network or a block (part) of a bigger network.
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Gene expression prediction without sliding window in LSTM
i want to implement the LSTM on whole genome of Histone modification on a single RNA strand with ten thousand base pairs (10,000 bp) in each RNA. All the previous approaches used have used sliding ...
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1answer
14 views
Can RNN learn for each `t` in time from a whole new dataset (many entries)
Basically, my data set is not as simple multi-variate time-serie as it's often (to some extent) the case.
For each month, I have N entries (not less than 3000). Can RNN of any variant (Please bear my ...
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7 views
Use Future information to make Forecasts with LSTM
I have a multivariate Timeseries data with me. It have 5 features namely f1, f2, f3, f4 and f5. I want to forecast f1 up to 6 ...
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1answer
24 views
How to learn from time series with multiple values for each time points
A multivariate time-serie has more than one time-dependent variable and it is my case.
Still for each time I have not one entrie of dependent variables but many entries, like:
...
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Dealing with excessive zeros [closed]
ipdb> np.count_nonzero(test==0) / len(ytrue) * 100
76.44815766923736
...
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1answer
20 views
Outputs of an LSTM Cell
from each cell of lstm, what are the output's and what does they signify? i understand that there will be three outputs. A long term memory, short term memory and a output. But, i am little confused ...
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12 views
Combining time-series lstm
I have time series LSTM, which makes fair predictions and might be a first-run model for my needs. An issue that I have is that I have multivariate analysis per user , and so far the LSTM is only ...
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13 views
Using SMAPE as a loss function for an LSTM
I am currently working on a time series forecasting problem and am looking into using an LSTM.
My final accuracy metric that I use to determine whether or not the forecast is good or not is defined ...
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11 views
Normalize between -1 and 1 or 0 and 1 (for LSTM)
Looking at various examples on the Internet I see some people normalize between -1 and 1, and others between 0 and 1. Is there any reason people choose one over the other?
Assuming I'm using the ...
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25 views
Keras TimeSeries - Regression with negative values
I am trying to make regression tasks for time series, my data is like the below, i make window size of 10, and input feature as below, and target is the 5th column. as you see it has data of {70, 110, ...
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LSTM/RNN seems to be failing at testing
I'm relatively new to ML, keras and tensorflow and I working with a dataset (kerastest.csv) that is 400 lines of this
...
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18 views
Which time step output should be used in a LSTM network?
Let's take a LSTM network with one layer and two hidden units. Let's take that the number of time steps are 4, then the input x is:
\begin{align}
x = \big(x\small(t),\space x\small(t-1),\space x\...
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First Neural Network: Poor Quality of Predictions but low val_loss
I am a total newbie to ML, please be gentle :)
I've created a RNN that should learn how to count. Input is a sequence of five consecutive numbers N, N+1, ..., N + 4...
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15 views
Can an LSTM learn correlations between time series and produce skillful predictions for individual time series?
I am trying to build a model that is capable of producing a multi-step forecast for many different time series. To keep the example simple, let's say I have three different time series, $T_1$, $T_2$ ...
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22 views
Loss decreasing faster = lower convergence?
I am trying to optimize a generative deep LSTM network but I am unable to train each model until it converges because it would take a very long time and cost too much on AWS. I am grid searching ...
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1answer
26 views
Sequences of time series data with only 1 output classification
So I'm facing a problem where I have a sequence (30h of data with 10sec intervals) and which is labeled to a class (3 different classes) for the whole sequence. I'm used to work with time series who ...
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Multiple entity extraction with character level RNN
I'm training a neural network to extract a certain kind of entities in a sentence (e.g. company names in a news title). Since I'm handling a multi-language corpus (especially CJK), which could be very ...
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Give Variable Length input to LSTM
My input data consist of list of list. Both list have dynamic length for every example like below.
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1answer
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input_dim for Dense Layer after LSTM layers Keras
Do I need to specify the input_dim (which means the number of features in one row/sample) after adding the first LSTM layer for the later Dense layers?
I was trying to create an architecture with 2 ...
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20 views
What kind of neural network would work best for loosely-defined data, like video game RAM?
I'm trying to build out a network layer map for a neural network to use in an NES AI. Most networks I run across on web searches are CNNs that use image data to identify things. Miles and miles and ...
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7 views
How can GPU speed up RNN with Sequence Data (no shuffle)?
It has been stated that CuDNNLSTM with GPU support greatly speeds up training compared to LSTM on CPU in Keras. But if I am working with sequence data in a time series, how can the data be processed ...
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1answer
19 views
Input sequence ordering for LSTM network
When training a LSTM network with time series data, I guess the order in which this data is fed matters, my question is how should this ordering be...
Let's take a time-series vector which will be ...
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1answer
17 views
Is it better to use a MinMax or a Log Return normalization to predict stock price movements?
I am trying to use a LSTM model to predict d+2 and d+3 closing prices. I am not sure whether I should normalize the data
with a MixMax scaler (-1,+1)
using the log return
(P(n)-P(0))/P(0) for each ...
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35 views
Beyond one-hot encoding for LSTM model in Keras
I have an LSTM model in Keras for categorical classification (20 possible categories). In many cases, my data can fit multiple categories.
Obviously, my current model uses one-hot encoding and fits ...
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1answer
21 views
LSTM - How to prepare train from a dataset which contains multiple observations for different events
I m using LSTM in a project related to MobiFall dataset which contains falls and daily activitives - such as walking, sitting etc - sensed by accelerometer, gyroscope and orientation sensors in x,y,z ...
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Training a RNN, Sequence to Sequence VS Sequence to One?
I would like my RNN to be able to be able to predict wether the person is likely to make a purchase based on website visits.
Let's assume that I have data for a user in the following format:
...
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15 views
How can you implement an LSTM with exogenous data (intended to be similar to ARIMA)?
I have a model trying to improve predictiveness on two correlated variables with a set of exogenous variables. I thought to do this with LSTM instead of vector ARIMA with exog (interestingly, the ...
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1answer
54 views
Stacking LSTM layers
Can someone please tell me the difference between those stacked LSTM layers?
First image is given in this question and second image is given in this article. So far what I learned about stacking LSTM ...
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1answer
25 views
How to normalize data of a different nature?
I am working a price prediction LTSM model for the stock market. I am using multiple features: Open, Close, High and I would like to add the Volume.
The 3 first features are of the same nature but ...
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21 views
Keras model input and output value
I have a dataframe of 2 columns, both text - one is title and other is the label to it.
Unique label count is around 40k so one hot encode was out of question.
I used word2vec with size=150 for both, ...
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3answers
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Keras LSTM accuracy stuck at 50%
I'm trying to train an LSTM for sentiment analysis on the IMDb review dataset.
As input to the word embedding layer, I transform each review to a list of indices (that corresponds to word index in ...
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1answer
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practical improvements worth trying over plain LSTM in text classification?
I have a dataset of about 1 million tweets corresponding to about 30,000 user accounts, labelled with binary data (classifying the tweet as written by a bot).
With that amount of data, I could use a ...
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Any practical improvements worth trying over plain LSTM in text classification?
I have a dataset of about 1 million tweets corresponding to about 30,000 user accounts, labelled with binary data (classifying the tweet as written by a bot).
With that amount of data, I could use a ...
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1answer
83 views
Model Not Learning with Sparse Dataset (LSTM with Keras)
This classification problem is apparently simple and I have no idea why it's not working, perhaps I'm doing a conceptual mistake. I'm trying to make a predictor which will classify minutes on a clock ...
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1answer
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How to implement keras LSTM time series [closed]
I am learning how to implement Keras LSTM on a simple time series data.
The dataset I'm using has $12$ columns and $300k$ rows. Each group of $200$ rows represents ...
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2answers
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What is the relation between input into LSTM and number of cells?
I want to train an LSTM network for time-series predictions, and want to get to the bottom of LSTM's.
In my understanding, the number of cells in a single LSTM layer can vary. However, since each cell ...
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1answer
50 views
Is my model over-fitting (LSTM,GRU)
I have small corpus max 150 text utterances, which is again distributed among 5 categories.
To test I started with basic deep learning model, where i used word2vec embedding, added 1D convolution ...
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Understanding LSTM/RNN structure
In keras when we apply LSTM/RNN model, we specify the node [i.e.,LSTM(128)]. I have a doubt how it actually works. From the LSTM/RNN unfolding image or description, I found that each RNN cell take one ...
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1answer
25 views
Why does my LSTM perform better when randomizing training subset vs. standard batch training?
I am training a simple LSTM network using Keras to predict time series values. It is a simple 2-layer LSTM.
I get the best performance when I train on subsets of the training set that start at random ...
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Backpropagation through time - How many Layers will an unfold produce?
In terms of Recurrent Neural Networks a backpropagation through time is used. That means, a RNN oder LSTM layer in Keras will be unfolded to x layers and backpropagation is performed on this unfolded ...
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Support Vector Machines VS LSTMs: How well it is justifiable to use LSTM for its Generalization properties?
The question is pretty straightforward, How well one can justify using LSTMs(Neural Networks) for text classification task in terms of "Generalization" compared to classic support vector machines(SVM) ...
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49 views
Classification of text using RNN LSTM
I havea problem statement of “Classification of text”. I am very new to machine learning and neural networks. So far, I have found this great example using pytorch.
This implements basic RNN for ...
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LSTM for prediction of data rates
I'm creating a LSTM to predict data rates.
I've created X input. I'm stuck at defining the Y output and then defining the model (i.e. sequential, adam).
See below
...
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36 views
How to train LSTM
Imagine 5 matrices, each with shape 200 x 20. I want take 5 rows of a matrix and predict the 6th (and so on). This will result in 196 samples per matrix (5 x 196 in total). The Keras LSTM layer ...
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1answer
51 views
Recurrent neural network (LSTM) dimensions error
I have data in a dataframe named ddf as follows:
...
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1answer
20 views
Structure of a multilayered LSTM neural network?
I implemented a LSTM neural network model in Keras. However, how the codes worked under the hood was not quite clear. I want to know if it worked the way I guessed how it worked?
For example:
Say ...
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how does LSTM and GRU gates decide which word to keep in the memory
the update gate in a GRU decides which word to keep in the cell or to be clear what is the cell state. how does the update gate in gru decide when to be close to 1 and when to be close to 0? Basically,...
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Input shape for forecasting time series with Keras LSTM
For the last few days I have been trying to get familar with time series forecasting using LSTM in Keras. I have been mostly following tutorials on https://machinelearningmastery.com. Overall the ...
