Try to align the CNN and RNN

[PythEsc]

We should make sure that the CNN and RNN actually are using the same structure of input and output data. Moreover, they should use the same internal methods (e.g. for calculating the accuracy)

[Naxter]

Would be the best if Bruno would also push his RNN code, that he AND me can compare the code

[brunolubascher]

I aligned the two. I pushed the RNN and the changes I did for both.

[PythEsc]

@brunolubascher @Naxter Is it currently possible to train the network and use the training progress later for the prediction of a single post?

1. Start the training
2. Save the training progress after some time
3. Start API
4. When a new request comes in use the already trained network to predict a single post

So basically the networks should support a way to simply call a function

def predict(post: str) -> dict

which gets a single post and returns the predicted reactions based on the former trained model

[Naxter]

For the CNN this works with the saved checkpoints that are written while training. The function can be easily written. Maybe we provide a base class so it is easier for the API to call the function for both networks. Also, the modularity is better and easier extendable.

[PythEsc]

This is a really good idea. I've created a base class called "NeuralNetwork". Moreover, I've moved both directories ("convolutional_neural_network" and "recurrent_neural_network") to a base directory ("neural_network"). In this base directory we'll add all files that are used by both networks. Everything specific for one or the other network will be added to its sub-directory

[PythEsc]

Ok I have started to adjust the RNN to the base class. I think it would be nice to have the possibility to save and restore the models so that one can continue training at a later point. Moreover, this is necessary for the predict-method anyways. So basically this is still open:

• Implement the train and predict method for the CNN
• Implement the predict method for the RNN
• Improve the way how the model is saved and restored so that one can continue training at a later point or can load the model when the predict method is called

[Naxter]

CNN prediciton method is implemented

[Naxter]

Is aligned.