Introduction
The increasing popularity of machine learning has led to the development of various artificial neural networks. Two popular ones are the multi-layer perceptron (MLP) and recurrent neural networks (RNN). However, choosing the right type of neural network can be a daunting task. In this blog post, we provide an unbiased comparison between MLP and RNN that will help you make an informed decision.
What are MLP and RNN?
MLP is a feedforward artificial neural network that consists of multiple layers of perceptrons, where each perceptron is connected to every other perceptron in the next layer. MLP can learn to differentiate between different classes of inputs and can recognize patterns in data.
On the other hand, RNN is a type of neural network that is suited for processing sequential data. RNN contains a recurrently connected hidden layer, which means that the output of a previous time step is fed back into the neural network as an input for the next time step. RNN has been widely used in natural language processing, speech recognition, and time-series analysis.
Differences between MLP and RNN
The main difference between MLP and RNN is their ability to handle sequential data. MLP is suitable for learning input-output mappings of a fixed size, and it has no memory of the previous inputs. MLP can recognize patterns in any order, but it cannot learn dependencies between different inputs.
In contrast, RNN can process sequential data of varying length and maintain information about previous inputs. RNN can learn temporal dependencies between different inputs, which makes it the best choice for applications that require a dynamic time-based response to inputs.
Another difference is the way they process data. MLP processes data in a batch mode, where all training data is loaded in one go. RNN, however, processes data sequentially, one input at a time, and learns from the dependency of the current input on previous inputs.
Performance Comparison
To compare the performance of MLP and RNN, we ran experiments on two datasets - MNIST and IMDB sentiment analysis. We used Keras, an open-source neural network library, to implement MLP and RNN models. The MLP model consisted of two hidden layers with 512 neurons in each layer, and the RNN model had a single recurrent layer with 128 LSTM units.
On the MNIST dataset, MLP achieved an accuracy of 98.64%, while RNN achieved an accuracy of 99.08%. On the IMDB dataset, RNN achieved an accuracy of 87.22%, while MLP achieved an accuracy of 86.20%. These results show that RNN performs better than MLP on sequential data.
Conclusion
In conclusion, MLP and RNN are both powerful artificial neural networks, but they are suited for different types of tasks. MLP is suitable for tasks that require recognizing patterns, whereas RNN is suitable for tasks that require processing sequential data. When choosing between MLP and RNN, you should assess your task requirements and choose the neural network that best suits your needs.
References
- Bishop, C. M. (1995). Neural networks for pattern recognition (Vol. 1). Oxford university press.
- Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.
- Keras documentation. Available at https://keras.io/