1.Software Environment

The required software are listed below:

...

5.Convolutional Neural Network (pending)

I have considered two ways to use CNN to improve the performance, but did not finish either of them.

Transfer training. In the last several programs in folder 'cnn', I have implemented the VGG16 with tensorflow in a way that it can work on a single GPU. The next work for this approach should be
- rewrite it in a way that is capable of running on multiple GPUs
- or train different copy of the models on different GPUs and average over different copy of models after training.
- rewrite the loss function. About this, you should consult with David. The general idea is to include classification and regression in a single loss function. One way to realize this is to minimize the expectation of loss funcitons, i.e. we set two loss function respectively for the case of one beam spot and zero beam spot. We first predict the possibility distribution of whether there is a beam. Then minimize the expectation of the loss function.
- Remember we should start with the pre-trained parameters rather than randomly initialized parameters. But I have written that part of code.
New convolutional neural network.
- Daniel has some idea of constructing a new convolutional neural network. But honestly I still don't understand what he wants to do. Not even the rough idea. So if you would like to create a new convolutional neural network, you should talk with him first.