SelfDrivingCar

My First real project using deep learning using Tensorflow and keras with a convolutional neural networks

Model Architecture and Training method

I train the model using a convolutional neural network predicting one linear output. first of all to reduce the overfitting, I used data augmentation by flipping each image of the dataset and by using the left/right camera. The model used an Adam optimizer For the training data, I used the central, left and right camera, each one, randomly flip.

Model Architecture

The architecture is the following:

• One convolution of 8 filters (9*9) [Elu activation]
• One convolution of 16 filters (5*5) [Elu activation]
• One convolution of 32 filters (4*4) [Elu activation]
• Max pooling pool_size=[2,2]
• Flatten layer
• Dropout: 0.5
• Fully conected: 1024 [Elu]
• Dropout: 0.3
• Fully conected: 1 (Linear output)