self-steering

Using Deep Learning to Predict Steering Angles. Udacity Challenge #2

Installation

Install all dependencies with:

$ pip install -r requirements.txt

This project is built on top of TensorKit which helps organizing experiments.

pip install https://github.com/nghiattran/tensorkit/archive/master.zip

Also, make sure you have Tensorflow above 1.0.

Usage

Check Hypes to see all options or to create your own ones.

1. TensorKit

You can train your own model by using.

$ tk-train path-to-your-hype

There are times that training process is interrupted due to unexpected reasons. In this case, you can resume training by using tk-continue.

$ tk-continue path-to-logdir

To evaluate trained model on valuation set, you can use tk-evaluate.

$ tk-evaluate path-to-logdir

2. Submission

To test your model, use submission.py to generate csv file. Format of this csv file would be similar to CH2_final_evaluation.csv.

$ python submission.py path-to-logdir path-image-folder

See submission.py for more detail.

3. Visualization

Use visualize.py to create a demo video from csv files generated above.

$ python visualize.py path-to-input-csv path-groundtruth-csv path-to-image-folder

See visualize.py for more detail.

Hypes

Options:

• model:
  • dataset_file: path to python file that handles input.
  • architecture_file: path to python file that constructs main graph.
  • objective_file: path to python file that handles losses.
  • optimizer_file: path to python file that handles updating variables.
  • evaluator_file: path to python file that handles validation.
• data:
  • train_file: path to training data file.
  • val_file: path to validation data file.
• logging:
  • display_iter: display frequency.
  • eval_iter: validation frequency.
  • save_iter: saving model frequency.
• solver:
  • opt: type of optimizer. Supported Adam, RMS, and SGD.
  • rnd_seed: seed number for random.
  • epsilon: value for epsilon (a small number that is used to avoid zero division).
  • learning_rate
  • max_steps
  • batch_size
• clip_norm: upper bound for gradients to avoid gradient exploding.
• image_height
• image_width
• reg_strength: regularization strength.
• color_space: image preprocessing color space. Supported rgb and yuv. Default rgb.
• crop: crop size. This value count from bottom to top.

Usage

To fine-tune, you only need to change learning_rate, opt, and max_steps in solver and other hyperparameters like reg_strength, color_space, crop,...

You can also create your own neural network architect by using layout in model/nvidia/architect.py and keep other parameter as is.

Models

• nvidia: inspired by SullyChen's implementation of Nvidia's paper End to End Learning for Self-Driving Cars but our implementation runs way faster.

• rambo: inspired by rambo team's submission.

NOTE: All models in this project are similar but not identical to what they are based on.