Contents

- 1 Can a LSTM be used for text classification?
- 2 Which is the next layer in the LSTM?
- 3 How is the activation function used in LSTM?
- 4 Can a LSTM solve a long term dependency problem?
- 5 How to classify a vector in Python stacked LSTM?
- 6 Why are stacked LSTM models a poor fit?
- 7 What are the different types of LSTM architecture?
- 8 How to do train test split with LSTM?
- 9 Which is the key to understanding a LSTM network?
- 10 What can LSTM be used for in the future?
- 11 When was long short term memory ( LSTM ) introduced?

## Can a LSTM be used for text classification?

Automatic text classification or document classification can be done in many different ways in machine learning as we have seen before. This article aims to provide an example of how a Recurrent Neural Network (RNN) using the Long Short Term Memory (LSTM) architecture can be implemented using Keras.

## Which is the next layer in the LSTM?

The next layer is the LSTM layer with 100 memory units. The output layer must create 13 output values, one for each class. Activation function is softmax for multi-class classification.

## How is the activation function used in LSTM?

The next layer is the LSTM layer with 100 memory units. The output layer must create 13 output values, one for each class. Activation function is softmax for multi-class classification. Because it is a multi-class classification problem, categorical_crossentropy is used as the loss function.

## Can a LSTM solve a long term dependency problem?

LSTM is a type of RNNs that can solve this long term dependency problem. In our docu m ent classification for news article example, we have this many-to- one relationship. The input are sequences of words, output is one single class or label.

## How to classify a vector in Python stacked LSTM?

My data is essentially a big matrix (38607, 150), where 150 is the number of features and 38607 is the number of samples, with a target vector including 4 classes. If I understand your problem, your samples are 150-dimensional vectors which is to be classified into one of four categories.

## Why are stacked LSTM models a poor fit?

If I understand your problem, your samples are 150-dimensional vectors which is to be classified into one of four categories. If so, an LSTM architecture is a very poor fit because there are no relations between your samples. I.e what is in the n:th sample doesn’t impact what is in the n + 1:th sample.

## What are the different types of LSTM architecture?

Different LSTM Models. 1 Classic LSTM. This architecture consists of 4 gating layers through which the cell state works, i.e., 2-input gates, forget gate and output gates. The 2 Stacked LSTM. 3 Bidirectional LSTM. 4 GRU (Gated Recurrent Unit) 5 BGRU (Bidirectional GRU)

## How to do train test split with LSTM?

Truncate and pad the input sequences so that they are all in the same length for modeling. Converting categorical labels to numbers. Train test split. The first layer is the embedded layer that uses 100 length vectors to represent each word.

## Which is the key to understanding a LSTM network?

The key to LSTMs is the cell state, the horizontal line running through the top of the diagram. The cell state is kind of like a conveyor belt. It runs straight down the entire chain, with only some minor linear interactions. It’s very easy for information to just flow along it unchanged.

## What can LSTM be used for in the future?

Future Work: 1 We can filter the specific businesses like restaurants and then use LSTM for sentiment analysis. 2 We can use much larger dataset with more epochs to increase the accuracy. 3 More hidden dense layers can be used to improve the accuracy. We can tune other hyper parameters as well.

## When was long short term memory ( LSTM ) introduced?

Now, let’s dig deeper into the architecture of LSTMs. Long short-term memory network was first introduced in 1997 by Sepp Hochreiter and his supervisor for a Ph.D. thesis Jurgen Schmidhuber. It suggests a very elegant solution to the vanishing gradient problem.