MullOverThing

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# How can RNN be used in sequence classification?

## How can RNN be used in sequence classification?

In this s ection, we will discuss how we can use RNN to do the task of Sequence Classification. In Sequence Classification, we will be given a corpus of sentences and the corresponding labels i.e…sentiment of the sentences either positive or negative.

## When does a RNN process the sequence of words?

Remember that RNN will process the sequence of words only after it encounters “ Start-of-sequence ” token and “ End-of-sequence ” token signals to the network that the input has reached the end and the output needs to be the finalized.

Is the runtime of a RNN reduced by parallelization?

The runtime is O (τ) and cannot be reduced by parallelization because the forward propagation graph is inherently sequential; each time step may be computed only after the previous one. States computed in the forward pass must be stored until they are reused during the backward pass, so the memory cost is also O (τ).

### Is it possible to implement a RNN in Python?

Implementing an RNN from scratch in Python. The main objective of this post is to implement an RNN from scratch and provide an easy explanation as well to make it useful for the readers. Implementing any neural network from scratch at least once is a valuable exercise.

### How are sequence models different from FNN and CNN?

Sequence models compute the probability of occurrence of a number of words in a particular sequence. Unlike the FNN and CNN, in sequence modeling, the current output not only dependent on the current input but also on the previous input. In the sequence model, the length of the input is not fixed.

What makes the sequence classification problem so difficult?

Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time and the task is to predict a category for the sequence. What makes this problem difficult is that the sequences can vary in length,…

#### What’s the difference between a static and dynamic RNN?

In contrast, there are static RNNs, which expect to run the entire fixed RNN length. There are cases where you might prefer to do this, such as if you are padding your inputs to max_sequence_length anyway.