Machine learning

This repository is used to experiment and train machine learning models for predictions. The repository also includes methods for processing InkML files into normalized traces and images.

Creating dataset and running the project

Prerequesites

• Python 3.5
• Pip (To python 3.5)

In order to run the code in this repository, you will need either InkML files or an already generated dataset. There is also a complete folder with both xml and preprocessed data available. If this file is chosen, just extract all the files within the /online_recog/data folder.

First clone the repository and run pip install -r requirements.txt .

From InkML files

If you do not have access to the preprocessed dataset. You must first run the preprocessing on InkML files.

1. Download the InkML folder
2. Create folders such that the folder structure in online_recog is: /online_recog/data/xml/
3. Unzip the InkML files downloaded, and paste them into the xml folder.
4. Open /online_recog/keras_lstm.py in a text editor.
5. Uncomment the line generate_and_save_dataset()
6. Run python keras_lstm.py

From already preprocessed

If you have downloaded the preprocessed data. You will just have to put the data in the correct folder.

1. Download the folder data.zip
2. Create the folder /online_recog/data.
3. Extract the zipped files into the newly created /data folder.

Running the training

After placing the datasets into the correct folder, the models can be ran by doing the following.

1. Uncommentload_dataset_and_run_model() in /online_recog/keras_lstm_py.
2. Choose the model you wish to train by changing run_RNN_model() to the method with the model you wish to train.
3. Run python keras_lstm.py.

Including the real data.

In order to include real data for validation during training. You will either need to create data from symbol-predictor-server, or download the already processed files (the real data is included).

In /online_recog/keras_lstm.py, there is a callback class which can be used to run validation and store logs from the training. This class, as well as a couple other places in keras_lstm.py has commented out lines that can be uncommented if real data is available in /online_recog/data. The regarding lines is shown in the keras_lstm.py file.

Python packages

• Requirements