/ Udacity, SDC

Traffic Sign Classifier: Udacity SDC Nanodegree Term 1 Project 2

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Introduction

This project was an introduction to CNNs in tensorflow. Udacity gives the student some suggestions on what a convolutional neural network looks like and then the student goes and implements that on their own

My solution

Here's what my final CNN looked like. It was trained on the dataset provied my Udacity

Input: 32x32x3 (a 32x32 pixel color image) Layer1: 2D convolution with an output of 28x28x6, and a pooling layer of kernel size 2x2 with a stride of 2 for an output of 14x14x6
Layer2: 2D convolution with output size 10x10x16, and a pooling layer with kernel size 2x2 for an output of 5x5x16
Fully Connected 0: Flatten layer 2
Fully connected 1: Matrix multiply with input 400 and output 120, then a relu activation
Fully connected 2: Matrix multiply with input 120 and output 84, then a relu activation
Output layer: Matrix Multiply with input 84 and output 43 (for 43 possible labels)

This was trained on the dataset provided by Udacity (https://d17h27t6h515a5.cloudfront.net/topher/2017/February/5898cd6f_traffic-signs-data/traffic-signs-data.zip) for an accuracy of 94.2% of the validation set