Can you use LSTM for classification?

Building the LSTM in Keras First, we add the Keras LSTM layer, and following this, we add dropout layers for prevention against overfitting. For the LSTM layer, we add 50 units that represent the dimensionality of outer space. The return_sequences parameter is set to true for returning the last output in output.1 Şub 2021

For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems) . A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate.

Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data .