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A Long Short Term Memory (LSTM) is a neural network architecture that contains recurrent NN blocks that can remember a value for an arbitrary length of time.

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(Why) Is the ouput's dimension in a LSTM bound to the number of recurrent units, and how are recurrent outputs passed?

Background: Looking for specific/interesting information on the equations within LSTM-Networks, I found the paper LSTM: A Search Space Odyssey. It is frequently mentioned in other articles. To gain a ...
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Mean Test Set Performance of LSTM & Evaluation

In the paper "Greff et al, 2017 - LSTM A Search Space Odyssey" they evaluate different variants of LSTM architectures against different tasks/datasets. Could you help me to understand the evaluation? ...
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15 views

Testing an LSTM making predictions 1 timestep into the future

Say I have a time series data set of 100 sequential timesteps, and I want to train and test an LSTM on the data set, but only on forecasting a single timestep into the future. I want more than one ...
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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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47 views

How to define a time series classification problem?

I have 3 sets of time series data generated from sensors, I believe they have some correlation themselves. Certain "modes" of the system can be defined from the patterns from these signals. The signal ...
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21 views

LSTM model using Keras, two layers, each layer with different size, make sense? [closed]

a friend of mine asked me for help, and asked if the scenario is legal: First Lstm hidden layer size is: 256, and the second hidden layer is: 128. This is the code he sent me (Keras): ...
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26 views

Deep learning with a lot of training data

I am building a bidirectional LSTM to do a sequential text-tagging task (particularly, automatic punctuation). Usually, the training is done in iterations, where in each iteration, the entire training ...
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Interpreting accuracy graph for a LSTM model | Keyword Prediction

I have created a LSTM model for keyword prediction. I am using RMSE optimizer for training. I observe that the train and test accuracy decreases at first and then fluctuates without much difference in ...
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21 views

Best practices to apply Layer normalization in recurrent networks

I'm trying to add layer normalization (in the encoder-level) to the Listen-attend-and-spell model for speech recognition tasks. To do so, I have done many experiments (all of them failed) to make my ...
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14 views

NaN output from Bidirectional LSTM in Keras

I am creating a bi-directional LSTM using tf.keras APIs: input_layer = tf.reshape(emb_seqs, [const.TRN_BATCH_SIZE, -1, const.VECTOR_SIZE]) lstm_layer = tf.keras.layers.LSTM(units=LSTM_UNITS, ...
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42 views

How does LSTM learn [duplicate]

I am confused on how LSTM learn from word embedding. I know that LSTM accepts 3D input (sample, timesteps, features). So, when we use embedding layer (word2vec) and we have 300-d vector representation,...
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23 views

How to understand what is going on based on the loss curves?

I have been implementing a many-to-one LSTM and have been searching for the best hyperparameters. However, sometimes I am a little confused on the reason the loss acts as it does. Here is a screenshot ...
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1answer
14 views

Getting ValueError while implementing LSTM in keras

I am getting this error while implementing LSTM in Keras: """"Error when checking input: expected lstm_16_input to have 3 dimensions, but got array with shape (156060, 1)"""" I have 156060 text ...
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1answer
6 views

Clarification about RNN/LSTM Sequence Models with Word Vector inputs

Say that we are trying to train a language model with an RNN/LSTM i.e. the inputs are words in a sentence and the outputs are the same words shifted by one such that for each input word the output is ...
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16 views

How to overfit an LSTM for acoustic features?

Aim I would like to train an LSTM to learn the mapping between the EMA signals and audio signals in the MOCHA-TIMIT dataset. I've looked at publications using similar approaches to see if I can ...
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12 views

Different ways to deepen LSTMs

Quick introduction of the notation I'm working with: With regular RNNs we have $$h^{\left\langle t\right\rangle }=g_{h}\left(W_{h}\left[h^{\left\langle t-1\right\rangle },x^{\left\langle t\right\...
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92 views

Loss decreasing but highly oscillating over all training examples

I'm training a sequence model. I have 40 sequences of length 70,000. I have divided these into minibatches of size $[40, 200]$. Since I want to utilise the stateful nature of the GRU I'm training, I ...
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81 views

State-of-the-art algorithms for the training of neural networks with GRU or LSTM units

I recently read a lot about neural networks using GRU or LSTM units. There are many easy to use frameworks like tensorflow that do not even require high knowledge about programming. Unfortunately, I ...
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Getting OSerror while loading my model in keras [closed]

I created a model in keras and then save it to h5 file then I created a package for that model and used load_model function of keras to load that saved model of mine but I am getting this error: ""...
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22 views

LSTM Training loss decreases and increases

I am new to LSTM and deep learning. I have 3000 reviews which I am trying to train on gensim pretrained model via word embedding. I have the following model where ...
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16 views

Using an LSTM unrolled into multiple timesteps for training, and just a single timestep for inference

I have built an LSTM in TensorFlow that is unrolled into n timesteps and trains successfully on sequences of lengths n. I would now like to use my trained model to make m daily predictions one by one....
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1answer
36 views

Why do we need second tanh() in LSTM cell

My question is why do we apply tanh() function to C second time when it has already been applied to it during the update procedure or, in case if we didn't update it, in the previous LSTM cell. I mean ...
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Find association rules between events and a failure

I am dealing with machine that provide logs of its current activity. These logs have over 4000 different messages (Info,Warning,Error,...). I would like to predict a failure of that machine using ...
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21 views

Word embedding as input or raw text?

I'm trying to implement a neural network for text recognition and I'm a little bit confused about text inputs. The goal of the network is to classify a comment, toy example: ...
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1answer
17 views

User behavior prediction using LSTMs

Let a user U with 3 possible states: A, B and C. From A you can go everywhere (including A), from B or C you can only go to A. LSTM are a good to model Markov-problems with an extra notion of long ...
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Multiple output NN vs multiple single output NN [duplicate]

I'm trying to build a 5 input-5 output model using LSTM, where all the outputs are the same features as the inputs, predicted in the future. My question is: is it better to build 5 models, each with ...
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Best method for predicting a binary DV from multiple IVs with time series data in R or Python?

Let's say I have a dataset of 100.000 cases that contains 5 variables recorded over a period of time in long format, like this: ...
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1answer
49 views

Feedforward Nets, RNNs, and LSTMs Theory

It is my understanding that feed-forward neural nets learn by backpropegating the error from comparing an outcome to the ground truth. How is this process not 'recurrent', as in recurrent nets, data ...
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1answer
25 views

What neural network to use to classify time series data?

I am a self-learning machine learning, so this question may be a bit trivial. I've been reading "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili; however, I am still uncertain what ...
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1answer
18 views

What is the reason that reduce training time over epoch for LSTM?

I am training and recurrent neural network and observed less time is needed over time. What could be the reason? I would think calculating the gradient, and update the parameters in the network would ...
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13 views

Train on Sequential and Make Predictions on Non-Sequential Data using LSTM architecture in Keras

I am working with multivariate time series data with multiple examples to train LSTM on, and Y is either 0 or 1 binary classification. Currently, I am using pad sequence layer in combination with a ...
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141 views

Difference between a single unit LSTM and 3-unit LSTM neural network

The LSTM in the following Keras code input_t = Input((4, 1)) output_t = LSTM(1)(input_t) model = Model(inputs=input_t, outputs=output_t) print(model.summary()) ...
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Sequential Long-Text Classification with Recurrent and Convolutional Neural Networks

I am thinking to build a model for predicting events from news. Before I start this task I wanted to ask if someone have tried to build something like in the link(https://arxiv.org/pdf/1603.03827.pdf) ...
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73 views

How to choose suitable Autoencoder (LSTM) architecture?

I am new to Autoencoders and I am a bit confused on which model to try for my situation and what is the difference between all the different models I have seen in tutorials. So, I have a set of time-...
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98 views

RNN LSTM overfitting

I'm trying to build a dynamic RNN network for 2-class classification, and I just can't get rid of the overfitting. I have 5500 samples of class A, and 8000 for class B (total 13500). From that I take ...
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How to pass MDLSTM activations to a feedforward layer?

I am currently trying to implement this paper by Alex Graves on Arabic handwriting recognition. In the architecture presented in Figure 4 on page 11, it says that the image is passed to 4X2 MDLSTM, i....
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1answer
37 views

Generating sequences of musical chords

I'd like to create a model capable of emulating music that has been presented to it. The model ought to be specifically designed for that purpose, not just another generic, stacked LSTM. For the ...
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66 views

Vanishing Gradient for LSTM on State Classification Problem [closed]

I'm trying to do sequence classification on an LSTM. The problem setup is that we get data from a machine which is our input. We don't know what state the machine is in, so we perform classification ...
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19 views

LSTM frame time series to a supervised learning problem

I just begun to play around with LSTM. Therefore I read the guide from this site Multivariate Time Series Forecasting with LSTMs in Keras The task is to predict the air pollution. I understand the ...
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How to sample a language model?

I've successfully trained a language model using LSTMs. But I have a confusion about sampling. On sampling, we generate a probability distribution at each time step. It will be of length vocabulary ...
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21 views

LSTM Training with High Score Variace

I am training a NN as conv -> conv -> dense -> lstm -> softmax, and got the scores/iteration as the figure shows. The dataset has been normalized with ...
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1answer
113 views

Is it possible that a stacked LSTM for text generation is performing worse than a one layer LSTM?

Does anyone know if the generated text from a stacked LSTM is performing worse than a one layer LSTM? Is a possible answer that the Model is overfitting? In my case after the first few epochs the ...
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1answer
45 views

What is the purpose of unrolling an LSTM into multiple time steps if you can just use a stateful LSTM of 1 time step?

As far as I understand the follwoing two models are essentially identical: Having a stateful LSTM with just a single time step and passing 10 time-series data points into it one by one, and using the ...
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1answer
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31 views

LSTM Cell backpropagation through time & independence of gates

I am calculating the backpropagation across LSTM cell gates, and I notice that many online sources treat the gates as independent. For example, in this Aidan Gomez blog, he derives the backpropagation ...
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23 views

Deep learning unerfitting/overfitting

I started to perform deep learning for sentiment analysis on word embedding. I have plot the model loss and accuracy graph for each epochs to understand the performance better. I read the following ...
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11 views

Do LSTM models mimic using n-grams?

I understand that LSTM models mimic using n-grams because of the memory process it uses -- and that they are much more context oriented. But is this only in cases for sequence to sequence predictions?...
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1answer
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Extensions of LSTM for huge data

Consider dealing with a huge high frequency financial data forecasting, RNN/LSTM is a popular way to solve the task. But the problem is that say you have 1 million data points and you want to predict ...
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1answer
108 views

How to handle big vocabulary size with keras tokenizer?

I am actually working on a neural language model developed with keras. I have an encoder and a decoder and the output of the decoder is a dense vector on the vocabulary..so quite big depending on the ...