Le 26/01/2021 à 20:03, Jim Clarke a écrit :
> I have started work on Layers, and have some Model plumbing done to
> support that,
> but I haven’t created a PR for it yet. Layers are at least dependent on
> finishing metrics,
> and somewhat on regularizers and constraints.
> So far, most of the work has been focused on setting up components that
> the Model will end up using,
> like losses, metrics, activations, initializers, etc. We are close to
> finishing Metrics Phase 1, with Metrics Phase 2
> queued up right after that. After metrics are done, I plan on
> submitting phase 1 of layers (Input, Dense, Flatten, Dropout).
> This will focus on the basic plumbing for layers, with many more layers
> planned for subsequent phases.
> I have the phase 1 layers mostly done, but am waiting for
> any changes coming out the the Metrics PRs before
> creating a PR for layers. Also, I just submitted PRs for constraints and
> regularizers this morning.
Thanks for this summary of the current development state. It seems I'm
coming a bit early for implementing a full model.
> I have been basing my work on the Keras Sequential Model, with Model
> being the abstract base class,
> and I am happy to share what I have done thus far.
> How does your model work fit in with the Keras view of Model?
I'm no familiar with Keras, sorry. The models I'm interested in were
implemented by the original authors with PyTorch, and my own current
version is with DL4J.
The single remark I can think of this point is that it would be nice if
the Model class was designed to fit the common idea in the literature of
a backbone (abstract) network that could be extended/subclassed for
specific tasks (classification, segmentation, detection...) by adding
some output layers, choosing a loss function, etc...
Or maybe should we distinguish a graph of layers (like a network
backbone) and a model (having a particular task).
But I guess you already have a mind set about all this.
> If you are interested in creating layers like Conv2D and
> Conv2DTranspose, etc., we could use all the help
> we can get. Over all, there are about 80 layer classes defined in Keras.
Sure. I'd wait and have a look at your work on basic layers (like dense
layers) to see how I can derive other kind of layers.