Can we use LSTM in CNN?

Can we use LSTM in CNN?

A CNN LSTM can be defined by adding CNN layers on the front end followed by LSTM layers with a Dense layer on the output. It is helpful to think of this architecture as defining two sub-models: the CNN Model for feature extraction and the LSTM Model for interpreting the features across time steps.

Is text classification with CNN better than LSTM?

Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification.

What kind of neural network is a LSTM?

An LSTM (Long Short Term Memory) is a type of Recurrent Neural Network (RNN), where the same network is trained through sequence of inputs across “time”. I say “time” in quotes, because this is just a way of splitting the input vector in to time sequences, and then looping through the sequences to train the network.

Why do we abandon LSTM for the other CNN?

CNN on the other hand stands for Convolutional Neural Network, another type of computer neural network that is often used for classifying images. So why the question about abandoning one (LSTM) for the other (CNN)?

What is the architecture of a CNN LSTM?

This architecture was originally referred to as a Long-term Recurrent Convolutional Network or LRCN model, although we will use the more generic name “CNN LSTM” to refer to LSTMs that use a CNN as a front end in this lesson. This architecture is used for the task of generating textual descriptions of images.

How is a CNN LSTM defined in machine learning?

A CNN LSTM can be defined by adding CNN layers on the front end followed by LSTM layers with a Dense layer on the output. It is helpful to think of this architecture as defining two sub-models: the CNN Model for feature extraction and the LSTM Model for interpreting the features across time steps.