Partially Trained Neural Network Updates

Week 3 progress update         Most of the progress I made this week was just research, I focused on getting familiar with the TensorFlow framework. I made several neural networks that work in slightly different ways and using different data sets. I have not completely decided on a data set that I want to use but I am probably going to use Image Net as it provides a relatively large sample for each class as well as plenty of classes. During my research I also designed a small script for processing "pickled" data, which has been stored for use in python as well as a script for processing some particular data sets such as Mnist, both of these are shown below. I also did a bit of research on making a CNN in TensorFlow which I will start actually building over the course of the next week or two.

Week 2 Progress Update.         I spent the majority of this week learning how to use the TensorFlow Framework. In order to do this I built a very simple neural network with it. All this neural network does is sort 28 x 28 pictures of street signs into 62 different categories, below is the code, the sections that are commented out (surrounded by ''' or proceeded by #) are either notes or remnants of the tutorial I was following. Below the code is an image which randomly samples some of the signs in the testing dataset once the network has been trained. It then attempts to predict what category it belongs to, if the predicted category matches the actual category it shows green text, if not, it shows red text. # imports import tensorflow as tf import os import skimage import numpy as np import matplotlib.pyplot as plt import random as r from skimage import transform ''' #init constants x1 = tf.constant([1,2,3,4]) x2 = tf.constant([5,6,7,8]) #multi...

Week 1 progress           This week I started building the algorithms for operating my convnet. I first installed TensorFlow, the framework I am using to build the models, which took far longer than it should have because of compatibility issues with the python installation on my machine. After I got this installed I did some minor research into data sets and chose a beginner data set for creating a neural network. This data set consists of different classifications of Belgian street signs. I then created a small algorithm to load the data as shown below. The complications in installing TensorFlow set me back and this was as far as I got during the week.