Thanks David, I’ve been going through the code of the low level components and I can't find any piece of code that explains that behavior. I’d agree that it must be in the OPF but I’m seeing evidence of it even in my non-OPF implementation.
At this point, I’m really wanting the feedthrough to be the issue because it’s the one thing I haven’t attempted to tweak to get better results. Thanks for your input! best, Nick > On Feb 3, 2015, at 5:20 PM, cogmission1 . <[email protected]> wrote: > > Nicholas, > > I can't really give a definitive answer to your question of whether, "... > [HTM] simply passes through the last input it's seen and uses that as a > prediction..." - But I *can* say definitely that this isn't a property of any > of the lower-level components or algorithms (i.e. encoder, spatial pooler, > temporal memory, cla classifier). > > This means that if this exists, it must be a property of the "containing" > infrastructure such as a Region/RegionImpl or some property of an OPF > container (if you're using that). > > Just thought that clarification might be helpful if you attempt to track this > down yourself... > > David > > On Tue, Feb 3, 2015 at 8:54 AM, Nicholas Mitri <[email protected] > <mailto:[email protected]>> wrote: > Hey all, > > As far as I’ve gathered, when HTM can’t make a proper prediction, it simply > passes through the last input it’s seen and uses that as a prediction (which > is why plots comparing predictions with actual values usually start out with > a lag). > > The problem with this approach is that when using multiple HTM regions each > trained on their own data in a classification setup, a region that is totally > confused by the sequence it’s seeing (since it never learned it) will end up > outputting predictions that are delayed inputs and the final prediction > sequence will have a similarity to the original sequence that you wouldn’t > expect an untrained region to have. > > More concretely, in my application, I’m passing sequences of 2D coordinates > that trace a number. Even though I only train a single region to produce low > anomaly for that number, ALL other regions output a predicted sequence that > looks similar i.e. region assigned to recognize ‘2’ outputs a trace that > looks like a ‘1’ when a ‘1’ sequence is shown to it even though it’s never > seen a ‘1’!! So do all other regions. > > I think this is what’s causing my classification results (based on anomaly) > to be so sub par. Is that a right assessment of the consequences of using > this feed-through approach? If so, where exactly is the code can I make a > change to prevent HTM from doing it? > > Thank you, > Nicholas > > > > -- > We find it hard to hear what another is saying because of how loudly "who one > is", speaks...
