Thanks for the tips and I'm working on it. So do we have a weekly call at
9:30 08/05 (PST)?
---------- Forwarded message ----------
From: Matthias Boehm <[EMAIL PROTECTED]>
Date: 2018-05-06 22:08 GMT+02:00
Subject: Re: Questions about MNIST LeNet example
To: [EMAIL PROTECTED]
Cc: Guobao Li <[EMAIL PROTECTED]>
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
* Download the "infinite MNIST" data generator
), 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.
On Sun, May 6, 2018 at 3:24 AM, Guobao Li <[EMAIL PROTECTED]> wrote: