Hi Guobao, that sounds very good. In general, the "model" refers to the collection of all weights and bias matrices of a given architecture. Similar to a classic regression model, we can view the weights as the "slope", i.e., multiplicative terms, while the biases are the "intercept", i.e., additive terms that shift the layer output. Both are subject to training and thus part of the model.
This implies that the number of matrices in the model depends on the architecture. Hence, we have two choices here: (a) allow for a variable number of inputs and outputs, or (b) create a struct-like data type that allows passing the collection of matrices via a single handle. We've discussed the second option in other contexts as well because this would also be useful for reducing the number of parameters passed through function calls. I'm happy to help out integrating these struct-like data types if needed. Great to see that you're in the process of updating the related JIRAs. Let us know whenever you think you're ready with an initial draft - then I'd make a detailed pass over it. Furthermore, I would recommend to experiment with running these existing mnist lenet examples (which is one of our baselines moving forward): * Download the "infinite MNIST" data generator (http://leon.bottou.org/projects/infimnist), and generate a moderately sized dataset (e.g., 256K instances). * Convert the input into SystemML's binary block format. The generator produces the data in libsvm format and we provide a data converter (see RDDConverterUtils.libsvmToBinaryBlock) to convert this into our internal binary representation. * Run the basic mnist lenet example for a few epochs. * Install the native BLAS libraries mkl or openblas and try using it for the above example to ensure its setup and configured correctly. Regards, Matthias On Sun, May 6, 2018 at 3:24 AM, Guobao Li <[email protected]> wrote: > Hi Matthias, > > I'm currently reading the dml script MNIST LeNet example and got some > questions. I hope that you could help me out of them. > > 1) Is it possible to define a matrix containing the variables? Because I'm > wondering how to represent the model as a parameter for the "paramserv" > function. > 2) What is the role of bias? Why we need it? > > Additionally, I have added some updates in JIRA for SYSTEMML-2083 and hope > to get some feedback. Thanks! > > Regards, > Guobao >
