Projections on Fashion-MNIST ============================ .. raw:: html
Usecase projection umap t-sne notebook beta
Runs on the same small CNN, showing the ways a projection reaches the :ref:`studio-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: :doc:`../../projection`. 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: .. code-block:: python 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 :doc:`../pytorch/fashion_mnist_umap`. 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: .. code-block:: python 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: .. code-block:: python 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 :ref:`notebook-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.