To use them we would have to adapt the encoders and decoders, though. Other datasets, such as “CIFAR-100” can easily be used as well since they can be loaded with ResourceData. mnistEncoder = NetEncoder[, "Input" -> mnistEncoder, They take care of the internal representation of the data and ensure that input and output works flawlessly. The output shall be a single digit number ranging from 0 to 9.Īgain there are functions for this: NetEncoder and NetDecoder.The input will be grayscale images of size 28×28.Once we have the data, we must set up a way to encode the data (our input will be lots of small images) and to specify the output that we expect. Which is strange, because you can easily train on the latter by entering its name (see below). Also, it seems that “MNIST” is available, but “FashionMNIST” is not. I must admit that I am not quite sure how the Resource* functions relate to the ExampleData command that seems to fulfil a similar purpose and has been around for a much longer time. There are countless ResourceObjects of all kinds that you can query in Mathematica. The function ResourceData gives us the content of a ResourceObject. The simplest way to get labelled data into our notebooks is via the commands: trainingData = ResourceData The goal is to set up a LeNet neural network and train it on the MNIST data set of handwritten digits. There’s a tutorial in the Mathematica documentation that we will loosely follow. You can train on the fashion MNIST data set by entering its name, but you can’t explicitly retrieve the dataset. The built-in functionality to retrieve data is not 100% consistent.I had to go through several manual pages and tutorials to get all the things together to get the code in this article running. I didn’t encounter any bugs or other issues, however the documentation is not always as clear as I am used from a software like Mathematica (Mathematica and Matlab have both some of the best documentation I have ever seen).I assume this means that they might change in future releases. The documentation lists many of the functions as experimental.There are a few things that I noted, though: I can’t say how useful it is for real world problems because I’m just playing with it on my kindergarten projects, but I enjoyed working with it so far. Wolfram Mathematica provides a comfortable machine learning framework to play around with.
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