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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Running LSTM model on a big data sample using pyspark
I was wondering how does one run an LSTM model on a big dataframe in pyspark. Ideally, one wants to run the model parallelly on different nodes of a spark cluster. But how does one do that?
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Combining 2D Detection with Disparity Maps to Learn 3D Object Geometry
Since the disparity map above is a representation of the object's distance from the camera's origin, is it reasonable to assume that a network (perhaps a convolutional LSTM) could be trained to ...
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Intuition behind the RNN/LSTM hidden state?
What's the intuition behind the hidden states of RNN/LSTM? Are they similar to the hidden states of HMM (Hidden Markov Model)? Thanks!
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predict next text/word. HMM or LSTM? Which is better and what's the pro and con of each method?
I am trying to do a prediction of next word given some previous words. Would it be better to use HMM model or LSTM model? What's the pro and con of each method? Are there examples? Thank you!
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'list' object has no attribute 'values' when we are using append in python
Here I have a dataset with three inputs. Here I generated y value using append. After the append I got the output like this:
y.append(rec.iloc[0]['y'])
Then I ...
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TLDR Bot - Sentence Tagging w/ BERT
Currently making a bot that condenses news articles. I'm tagging sentences as important or not important using a simple BERT classifier. The results were... not great. I'm really interested in how I ...
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Continous Learning and Shifting Patterns
I've been intensively studying neural networks which try to predict a vector based on a given input matrix.
The input matrix is a N x H matrix and the output vector a N x 1 vector.
The network is ...
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is it possible to implement LSTM with input shape (sample,timestep,timestep,feature)?
I'm new to Keras. I am trying to implement this model https://www.aclweb.org/anthology/D15-1167 for document classification, and I want to use LSTM for getting sentence representation. I have trained ...
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How to plot the LSTM model
I am using the following simple LSTM model that is implemented using Keras.
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Predicting three next word based on input data using LSTM
I am working on an assignment on next word Prediction. I am trying to create a model which can be used to generate next words based on the input like swift key and gives three options to chose as the ...
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Structure of LSTM gates
It is my impression that a single layer LSTM architecture consists of $t$ LSTM cells that are identical duplicates, where $t$ is the number of time steps. Then there are gates within the LSTM cell. I ...
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FastText and CharEmbedding
i have got a question. i don't understand how to develop my DLModel.
I'm working on DGA Detection with this type of dataset:
...
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LSTM fot text classification always returns the same results
Hello fellow Data Scientists,
I'm trying to make a classifier that was to classify sequences of text into some predefined classes, but i always get the same output, can anyone help me understand why?
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How to build Keras Model for Data set which contains both Time_Series Data and other Feature Data?
I have a data set which contains Time Series Data and other features data. Usually we using RNN or any Variant of RNN like LSTM for time series data and simple Dense Layer for features Data. I need to ...
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How to improve model confidence for each category in binary classification problem?
I am new to machine learning and expanding upon an introductory tutorial I followed with some artificial housing data I created. I asked this question on stackoverflow and was referenced to the data ...
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LSTM pulse signal prediction
i'm trying to capture long-term dependencies using LSTM, by creating a unit pulse signal every 62 points.
The idea is to go back 62 time-steps and copy the value for the next time-step, so as to ...
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How many seconds ahead of one paramenter can I try to forcast from the code
I would like to test this code for several (x) seconds of prediction of the output.
How long should the window of the past values in order to x seconds in the future of one value being known all the ...
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Using LSTM for multi label classification
I am trying to use LSTMs to train and predict authors using reviews data and metadata
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transform a supervised neural network to reinforcement learning?
I have a functional LSTM model that works with an acceptable performance. How can I now convert this supervised model to a reinforcement learning model for improving the performane? Is there any ...
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LSTM - Incorporate word embedding in layer with multiple records in same date
I have a time series data having more than one record in a single date. Number of records in a single date is not consistent.
I have 3 input features namely phrase, cost and weight. My goal is to ...
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How can I explain the cause of different performances for two different LSTM models and improve the performance?
I've built two different models for Load Forecasting. Dataset has six features. The performance evaluation metric is the Mean Absolute Percentage Error(MAPE). Both models are based on LSTM. Here is ...
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How to create a seq2seq without specifying a fixed decoder length?
Based on the model presented in this answer:
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High Level Discussion: Generate synthetic sensor data using data from surrounding sensors
Let's assume I have readings coming from sensors. For every sensor, I have the following information:
all the data it reads
its location
Now, given an arbitrary sensor at an arbitrary location ...
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Language modelling for Spell Checker
I am working on spell checkers, I want to create a spell checker, I am confused about which model to use
Word-Level modelling
Character-Level modelling
plus I am preferring neural networks over ...
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Error on custom RNN/LSTM with multiple inputs
I want to implement a custom RNN/LSTM model similar to this. The model should take two separate vectors as input and process them. I was following keras tutorial to implement a custom keras layer and ...
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Pytorch lstm model very high loss in eval mode against train mode
I am using a Siamese network with a 2-layer lstm encoder and dropout=0.5 to classify string similarity.
For each batch, I am randomly generating similar and ...
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Connection between Embedding and LSTM and Dense layer
I am building a "predict next word" model using the following model architecture. The codes fine, but I have a few questions:
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ReLU for combating the problem of vanishing gradient in RNN?
For solving the problem of vanishing gradients in feedforward neural networks, ReLU activation function can be used.
When we talk about solving the vanishing gradient problem in RNN, we use a more ...
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LSTM to predict Sin(x) from x
Hi I want to pass a series of values x1, x2... as input to the model to predict y1 = sin(x1), y2 = sin(x2)...
-I created dataset: x=[0.1,0.2,...] and y=[sin(0.1),sin(0.2),...]
-I normalize x in [0,1]...
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1answer
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Running out of memory when training Keras LSTM model for binary classification on image sequences
I'm trying to come up with a Keras model based on LSTM layers that would do binary classification on image sequences.
The input data has the following shape: ...
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The loss and accuracy of this LSTM both drop to nearly 0 at the same epoch
I'm trying to train an LSTM to predict the the Nth token using the N-1 tokens preceding it
For each One-Hot encoded token, I ...
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Spatial and temporal information processing together (CNN and LSTM)
I have small problem that requires to process both spatial and temporal information.
I need to predict vehicle's trajectory based on previous trajectory information and map information. My current ...
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1answer
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Autoencoders for the compression of time series
I am trying to use autoencoder (simple, convolutional, LSTM) to compress time series.
Here are the models I tried.
Simple autoencoder:
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How can I build a seq2seq model , which is topic aware
I have developed a chatbot, which is basically a seq2seq LSTM network. Which can generate text based on input text. But the problem I am having right now is it is not topic aware.
As an example :
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Forecasting via LSTM or XGBoost… is it really a forecast or
I guess I understand the idea of predictions made via LSTM or XGBoost models, but want to reach out to the community to confirm my thoughts.
This tutorial does a nice job explaining step by step of ...
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How can RNN handle variable sized inputs?
I came across this answer which is specific to Keras. But my question is at concept level. I am getting confused, How can RNN handle variable size inputs?
here
Let us suppose we want to do a ...
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Padding the sentences is consuming huge memory
I prepared a lstm model using tensorflow which has a max_sequence_length of 5000 and I'm padding the small sentences with 0's. I then deployed and tested the model ...
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How to normalize a data set of multiple time series?
I have the a data set representing the electricity consumption of 25 000 customer.
The electricity readings are taken from each smart meter each 15 min for a period of 3 days. The data is takes from ...
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Why can we not split train test data with 0.01 as parameter or 99% training data
Most of the blogs mention about a good thumb rule to be 80-20 split for the train and test respectively.
My special case is a time series dataset and it is for the stock prices, which IMO is very ...
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Using LSTM for binary text Classification, getting almost same accuracy at each epoch
I am doing Twitter sentiment classification. For that I am using LSTM with pretrained 50d GloVe word embeddings(not training them as of now, might do in future).
The tweets are of variable lengths ...
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How to get a output of a hidden layer of a single-layer LSTM
How can get the hidden layer outputs in a simple one-layer lstm?
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Could the Input shape of the LSTM layer not be a constant?
I am trying a vanilla LSTM on my dataset.
According to this course; the LSTM layer should be built as follows:
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LSTM forcasting: time series as input and a unique value for each as output
I am sorry for the mistakes I will make in this post, I'm new to deep learning ^^'
I am trying to build an LSTM model that can help me predict some unique value according to time series indices and ...
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Generate new sentences based on keywords
For example, for a domain specific neural network in Fashion, with the Keywords light, dress, orange, cotton. It could output: This gorgeous orange summer dress is great for wearing on sunny camping ...
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NLP based Data Preprocessing Method to Improve Disease Name Prediction Using CRF and Word Embedding
I built a model( using CRF along bi lstm) to Predict New Disease Name/Entities from medical text data but the problem is Disease name appears only 5,6 times in 1 text file but on average text file ...
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pass tf.data.Dataset to input layer in tf.keras functional api in LSTM models
While trying to use an Input layer prior to embedding layer in Keras functional design model, the Batch dimensions are not ignored by input layer automatically.
Here is my ...
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LSTM to multivariate sequence classification
How can I train multivariate to multiclass sequence using LSTM in keras?
I have 50000 sequences, each in the length of 100 timepoints.
At every time point, I have 3 features (So the width is 3).
I ...
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How to use TimeDistributed fo CNN+LSTM?
I am trying to classify 6 classes time-frequency domain signal (STFT spectrogram) with a size of 3601x217 pixels. Assume that for each classes have 70 training samples, 20 validation samples, and 10 ...
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Loss and accuracy remains constant in time series classification by LSTM
I have a time series data with a classification label of 1 and 0. I am using a LSTM model to classify the series by taking 100 consecutive timestamps as input with a single label.
Even after training ...
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Do timesteps must have the same temporal distance in training a RNN?
I have a recurrent neural network with LSTM units that I want to train with batches of 6 timesteps. Each timestep is a record of a dataset and represents the temporal aggregation over 5 minutes of ...
