New answers tagged lstm
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Is there such a thing as RNN-LSTM
RNN is a general term for Neural network architecture having recurrent connections. This architecture is built from a single entity called "cell". These so called cells are used repeatedly ...
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How RNN or LSTM delays the input
The LSTM is just applied to each of the time steps in the input. The information from the distant time steps is passed from time step to time step in the hidden state of the LSTM. That's how it can ...
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Why are the hidden states of an RNN initialised every epoch instead of every batch?
Hidden states of an RNN are initialized every epoch ONLY if you are using truncated back-propagation through time (TBPTT).
If you are not using TBPTT, then you would reset the RNN hidden states at ...
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LSTMs how to forecast out N steps
So this is what I have to predict 96 samples into the future which is 24 hours 15 minutes at a crack:
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LSTMs how to forecast out N steps
You can use the prediction of the network as an actual value, and create a new input tensor using the previous input with a new column to the right (and removing the first column, since you imposed ...
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How to train-test split a timeseries?
For a single time series, I usually make the split in various train test splits ie:
You have data from August of 2020 to August 2021.
Split taking 2 months by 2 months, this process is called ...
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Using the whole GloVe pre-trained embedding matrix or minimize the matrix based on the number of words in vocabulary
Long Short Term Memory (LSTM) can take a long time to train because of the complexity of the architecture.
If you think the size of the embedding space is also slowing down training, you can reduce ...
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Could you explain if this plot is good or bad. It is a sentiment analysis modelusing LSTM layers
It is not a good plot because:
The axis lack labels.
The legend labels are not very meaningful, "Validation" and "Test" would be better choices.
The plot relies solely on color to ...
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Could you explain if this plot is good or bad. It is a sentiment analysis modelusing LSTM layers
"Good" or "bad" is always relative in data science. You need to establish a benchmark for comparison.
First of all, you need to know that accuracy is not a very good performance ...
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Could you explain if this plot is good or bad. It is a sentiment analysis modelusing LSTM layers
It's a fairly good looking plot but it seems like your model if slightly overfitting to the train_dataset
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Are there any practical advantages of LSTMs over transformers?
LSTMs/GRUs have lower computational and memory requirements than transformers.
Depending on the case, using an LSTM instead of a Transformer may make sense due to those factors. For instance, using ...
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Start & End Tokens in LSTM when making predictions
Yes, if it's trained to have the start and end tokens than you will need to include them because otherwise it will incur a domain shift since the network in question is trained strictly on sequences ...
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