Hi BSP,
In MATLAB, the functions 'classify' and 'classifyAndUpdateState' are used for sequence classification tasks with recurrent neural networks (RNNs), such as LSTM (Long Short-Term Memory) networks. Both functions are used to classify sequences, but they differ in their handling of the network's hidden state and cell state.
- 'classify' function: The 'classify' function takes a sequence as input and performs classification without updating the network's hidden state or cell state. It assumes that each sequence is independent, and the network starts from an initial state for each new sequence. This function is suitable when you want to classify individual sequences without considering any temporal dependencies or when you want to manually manage the network's state.
- 'classifyAndUpdateState' function: The 'classifyAndUpdateState' function, on the other hand, not only classifies the input sequence but also updates the hidden state and cell state of the LSTM layers. It assumes that the sequences have temporal dependencies, and the network needs to maintain its internal state across multiple time steps. This function is useful when you have sequential data and want to classify it while considering the temporal context and dependencies between consecutive time steps.
The purpose of updating the hidden state and cell state in 'classifyAndUpdateState' is to allow the LSTM network to capture long-term dependencies and carry information from previous time steps to the current time step. By updating the state, the network can maintain a memory of past observations, which can be valuable for sequence classification tasks.
In summary, you should use the 'classify' function when you have independent sequences or want to manually manage the network's state. On the other hand, use 'classifyAndUpdateState' when you have sequential data with temporal dependencies and want the network to maintain its internal state across multiple time steps to capture long-term dependencies.
