I'm studying this LSTM network: https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis
model = Sequential() model.add(embedding_layer) model.add(Dropout(0.5)) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2)) model.add(Dense(1, activation='sigmoid')) model.summary()
I understand the input part of the embedding layer. Each word get's a unique vector that represents the meaning of the word.
The drop out will deactivate neurons. So the input for the LSTM model is the vocabulary where each token is represented by a vector.
I understand the workflow in an LSTM model. But what exactly does it do with the input? Give it a score by learning? And what is the output of the lstm?
Here is the summary: