Projections on Fashion-MNIST

Usecase projection umap t-sne notebook beta

Runs on the same small CNN, showing the ways a projection reaches the Projection Board (beta): the built-in live UMAP, and your own algorithm, plugged in from a script or from the Studio notebook. Background and every option: Live 3-D Projection (Parametric UMAP).

Built-in live UMAP, while it trains

Example: weightslab/examples/Usecases/wl-fashion-mnist-umap

A plain training loop with nothing projection-specific in it. Wrapping the model attaches the projection, because it is on by default; the dict only tunes it:

model = wl.watch_or_edit(
    FashionCNN().to(device), flag="model", device=device,
    projection={"every_n_steps": 25, "graph_size": 1024},   # optional
)

Run python main.py, press Play in the Studio, then open Data Exploration → Projection. Until the first fit the board shows an orange No projection found ribbon saying it is waiting; then one point per image appears, coloured by class. The cloud starts as one tangle and pulls apart into clusters as the representation forms — and the classes that stay mixed (Shirt, T-shirt, Pullover, Coat) are the ones the model confuses. Lasso a mixed region to load those images into the grid.

The projection reads the input of the last Linear (fc2): the 128-D representation the classifier decides from, not the logits. Test images are placed by the encoder but never fitted on.

Compare it with a PCA of your own

Example: weightslab/examples/PyTorch/wl-fashion-mnist-umap

Same CNN, driven in stages: train 1000 steps with the live UMAP, reload that checkpoint, discard the 30 % of training samples with the lowest loss (the ones the model already gets right), lay what is left out with a 2-D PCA you write yourself, then compare it with the UMAP in the board’s picker and in numbers (how often a point’s nearest neighbours in the picture share its class). A last stage retrains on what is left: the live UMAP follows the model, the PCA stays the snapshot you took. The full walkthrough is Classification, Fashion-MNIST + UMAP (PyTorch).

Converge, pause, your own t-SNE

Example: weightslab/examples/Usecases/wl-fashion-mnist-custom-projection

  1. The built-in projection is off (projection=False): the projection in this run is yours.

  2. The script evaluates every 250 steps and, once test accuracy stops improving (3 evaluations without +0.25 points), pauses itself:

    if plateau.update(test_accuracy):
    wl.pause_training()      # the next step waits for Play

  3. It has written own-projection.ipynb into the run directory, so the Studio’s notebook opens on it. Its cells collect the converged model’s features, run an exact t-SNE written from scratch in plain torch, and hand the result over:

    wl.save_projection_coords(tsne_coords, batch_ids=ids, prefix="tsne")

  4. Data Exploration → Projection now offers tsne — and pca, from the notebook’s last cell — in the picker.

Running a notebook cell pauses training first and leaves it paused (see Running a cell pauses training), so even a cell run before convergence sees one state of the model. Press Play to resume training when you are done.

Any algorithm plugs in the same way: anything that returns (N, 2) or (N, 3) coordinates for N sample ids, from sklearn, openTSNE, umap-learn or your own code. wl.project_dataset(model, loader, method=TSNE(n_components=3)) collects the features for you instead.