Welcome to part ten of the Deep Learning with Neural Networks and TensorFlow tutorials. In this tutorial, we're going to cover the Recurrent Neural Network's theory, and, in the next, write our own RNN in Python with TensorFlow.
Most people are currently using the Convolutional Neural Network or the Recurrent Neural Network. The Recurrent Neural Network attempts to address the necessity of understanding data in sequences. Despite what we're told in school, we have more than 5 senses. We have at least one more sense, and that's a temporal sense, a sense of time.
Consider a sentence like
"Harrison drove the car," where each word is a feature. Thus, our features are
["Harrison","drove","the","car"], and maybe we even have an End of Sentence (
EOS) character for the period, but let's ignore that for now.
In a traditional neural network,
"Harrison drove the car" is treated exactly the same as
"The car drove Harrison". In the former case, we have an ordinary vehicle, under the control of a human. In the latter, we have a self-driving car.
While Recurrent Neural Networks have gained a lot of exposure through use in language, they also have use in the physical world as well. We judge distance, velocity, and acceleration, all thanks to our temporal sense.
You're in a field. You have a bunch of data of a baseball in the air. Some of the data says the ball is close to you, other data says it is close to another human, and other data says it's between you and the another human. Should you try to catch the ball? Should you duck? Should you do nothing? How quickly should you do these things? Unless you've included a temporal sense, you might get lucky, or you may wind up with a black eye.
With a Recurrent Neural Network, your input data is passed into a cell, which, along with outputting the activiation function's output, we take that output and include it as an input back into this cell.
Another way to look at this is more like this:
This can work, but this means we have a new set of problems: How should we weight incoming new data? How should we handle the recurring data? How should we handle/weight the relationship of the new data to the recurring data? What about as we continue down the line? If we're not careful, that initial signal could dominate everything down the line.
This is where the Long Short Term Memory (LSTM) Cell comes into play. An LSTM cell looks like:
The idea here is that we decide what to do with the recurring data, what new to add, and then what to output and repeat in the process.
Recurring data goes through what is referred to as the
Keep Gate or
Forget Gate, basically which decides what to keep and what to remove from the recurring data. From here, we get to the new input data, determining what new to add from it, then, finally, we decide what our new output will be.
If you would like more information on the Recurrent Neural Network and the LSTM, check out Understanding LSTM Networks. In the next tutorial, we're going to cover how to actually create a Recurrent Neural Network model with an LSTM cell.