This chapter goes through some techniques for improving your recurrent neural network model. Often, the initial results from your model can be disappointing, so you need to find ways of improving them. This can be done with various methods and tools, but we will focus on two main areas:
- Improving the RNN model performance with data and tuning
- Optimizing the TensorFlow library for better results
First, we will see how more data, as well as tuning the hyperparameters, can yield significantly better results. Then our focus will shift to getting the most out of the built-in TensorFlow functionality. Both approaches are applicable to any task that involves the neural network model, so the next time you want to do image recognition with convolutional networks or fix a rescaled image with GAN, you can apply the same techniques for perfecting your model...