1. Logan Lebanoff Mentor: Haroon Idrees
Jun 1 - 5

2. Research paper
ImageNet Classification with Deep Convolutional Neural Networks
Motivations/results of certain approaches
Terms
ReLU vs sigmoid, GPUs, overlapping pooling, input image size, data augmentation, dropout
MatConvNet Manual
Necessary to understand how to make changes in the code

3. ImageNet Downloaded training/validation/test image dataset
Understanding the code
Modifying

4. ImageNet Load a pretrained network
Retrain the pretrained network to just change the last layer’s weights

5. pretrained = load('E:\Logan\matconvnet-1. 0-beta12\matconvnet-1
pretrained = load('E:\Logan\matconvnet-1.0-beta12\matconvnet-1.0-beta12\examples\data\imagenet12-dropout\pretrained.mat');
pretrained.layers{end} = struct('type', 'softmaxloss', 'name', 'loss') ;
[net,info] = cnn_train(pretrained, imdb, fn, opts.train, 'conserveMemory', true) ; % changed net to pretrained

6. ImageNet
Change last layer to output a different size vector instead of the default 1000
Find where back propagation happens in the code
Change back propagation depth from +inf to 1
Still quite slow
Found where the number of epochs was set, and changed it from 20 to 1, for testing

7. % Block 7
%net = add_block(net, opts, 7, 1, 1, 4096, 4096, 1, 0) ; Changed removed
net.layers{end+1} = struct('type', 'dropout', 'name', 'dropout7', 'rate', 0.5) ;
% Block 8
net = add_block(net, opts, 8, 1, 1, 4096, 5, 1, 0) ; % I changed this to 5 from 1000
net.layers(end) = [] ;

8. ImageNet Error while running test image on my retrained network
Assertion failed in nnsoftmaxloss
if nargin <= 2
t = Xmax + log(sum(ex,3)) - reshape(X(c_), [sz(1:2) 1 sz(4)]) ;
Y = sum(t(:)) / n ;

9. MNIST Error while running on test image on MNIST
Image depth does not match
Changed to grayscale
Also had to resize the image to 224x224x3 for ImageNet

10. Next week Retrain network with crowd training images
Using pretrained network
Still only training last layer
Classification problem
Find the count by classifying into groups
, , , etc.