# Traffic Sign Classifier: Udacity SDC Nanodegree Term 1 Project 2

## Github

## 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