Learn three approaches to training a deep learning neural network:
1. training from scratch
2. transfer learning
3. semantic segmentation
This submission, along with the corresponding ebook, offers a hands-on approach to deep learning.
Great one to start with on it..
Thanks a lot!
Got it, I downloaded individual files before. Thanks a lot!
@Adam - the MNISTModel is a .mat file included in the download of the files. It should be under Demo1_MNIST/MNISTModel.mat
Hey, where can I find MNISTModel file (line 22)?
@Venkat - you can remove those lines of code, and the function should run properly without them. This was an old artifact that can be removed. Let me know if you have any trouble.
@Fajar: You are going to run into errors using this code in 2014a. The error is most likely because the function webread() was not introduced until 2014b. You can download the files manually using the link rather than using webread, but you will run into other challenges with the version 2014a, since our deep learning support came out in R2017a
Thank you for sharing the code for these examples. In the function prepareData.m, a couple of lines have been commented out. These are:
% img = readMNISTImage(imgDataTrain, 3);
% figure, imshow(img);
I wasn't able to locate the function readMNISTImage readily. Am I missing anything obvious? Very grateful if you could pint me in the right direction.
i have this error.
Preparing MNIST data...
Error using fread
Invalid file identifier. Use fopen to generate a valid file identifier.
Error in prepareData (line 42)
magicNum = fread(fid, 1, 'uint32');
Error in MNIST_Classification_Demo (line 11)
[imgDataTrain, labelsTrain, imgDataTest, labelsTest] = prepareData;
i use R2014a
Updated Image and Copyright. No changes to code.