This project builds a deep neural net with the popular machine learning library DeepLearning4J. Its neural net tries to predict canteen menus for today based on the day of week and the previous meals.

The training and test data set were extracted with JSOUP from the official students service Leipzig web page as plain, unstructured HTML. The model is so far as simplified, that it only holds the meat type for a given menu. Moreover these types were reduced to the three frequently served ones: chicken (Hühnchen), pork (Schwein) and beef (Rind). You can find my collected and already extracted data set as CSV in data/menu.csv. Alternatively you can download the current meal plan on your own and use my de.htwk.ml.util.Html helper class to parse the data.

Although the model learns sequential data, this project uses a fully-connected multi-layer-perceptron, to map this correlation, and leaves the modern approach with recurrent neural networks out. The picture below shows the network architecture based on the day of week (Montag, Dienstag, ...) and previous two meals as one-hot encoded input vector. Thus nets input layer holds eleven neurons and it has three output neurons with softmax activation, for classification purpose. Further the net consists of two hidden layers with 14 neurons in each, where every neuron has an ReLU activation. The Java class de.htwk.ml.deep.Network implements this model with DeepLearning4J.

To train and test this model, simply run the JUnit test de.htwk.ml.deep.NetworkTest in your IDE or with Maven.

For classification neural nets apply an one vs all vote, where all neurons in their output layers take part. Therefore the weights and biases of output layer neurons form one linear separator in hyper space for each class and compare one neurons decision against all others, like the picture below shows for the 2d space. Thus the softmax function serves a probability for each class based on neurons vote.

All in all after 20,000 iterations of fitting weights and biases my model reaches an accuracy of over 97% on the training set and predicts three of five meals of the test set correctly.