This repository contains the notebook that sees the implementation and optimization of a convolutional network. The underlying task is to prediction two quantities; steering angle and speed of a SunFounder PiCar.
Data collection
The data collection was performed in segments, where each particular scenario of the final test enirvonment was targeted. The remote controlled car was manuvoured via external controls around the following tracks:
Where the following situations were considered:
- Clear ring
- Clear T-junction
- Ring track with obsteciules
- T-junction with traffic lights
- T-junction without stationarity
In total, there are around 3000 training examples. The set is split using 70:20:10 ratio between the training, validation and evaluation sets. To investigate the raw datasets, please find them via this link.
Data preparation
The raw files are separated via their original collection. There are around 13 folders. The data_exploration.ipynb notebook contains the sortation and exploration of the folders content, preparing it for the training process and visualizing descriptive statistics.
Modelling
The CNN_model.ipynb contains the fitting and optmization process of the CNN model. Tensorflows' hyperparameter API alongside the TensorBoard API has been used for the model optimization process.