Even though our dataset only classifies images into 10 classes, the examples could be

The latest stats_db dump should have the data for this: <a href="https://www.dropbox.c

Clustering of images based on fully-connected layer outputs about deepviz HOT 1 OPEN

bruckner commented on September 26, 2024

Clustering of images based on fully-connected layer outputs

from deepviz.

Comments (1)

JoshRosen commented on September 26, 2024

The latest stats_db dump should have the data for this: https://www.dropbox.com/s/t5zg9dqb0eoe6lv/stats_db.zip

This should have the required data to begin exploring different clustering techniques. Here was my first attempt at using PCA to show the model at timestep 40 (colors according to true image class), which should be a decent example of how to use the data:

>>> from deepviz_webui.model_stats_db import ModelStatsDB
>>> db = ModelStatsDB("stats_db")
>>> probs = db.get_stats(40).probs_by_image
>>> probs
array([[  1.29584700e-03,   3.57754361e-05,   5.01396321e-02, ...,
          7.13144150e-03,   4.18440730e-04,   3.63701925e-04],
       [  1.50107443e-02,   9.17336904e-03,   3.14076282e-02, ...,
          4.42980155e-02,   6.94869012e-02,   1.35898665e-01],
       [  1.14151709e-01,   3.09785362e-03,   8.43245164e-03, ...,
          9.15541202e-02,   7.15444088e-02,   2.27550700e-01],
       ...,
       [  3.99635881e-01,   6.38790429e-03,   6.37241304e-02, ...,
          6.20891619e-03,   3.05531979e-01,   5.47074946e-04],
       [  9.86086950e-02,   9.97514580e-04,   5.42566031e-02, ...,
          6.17355225e-04,   2.41912946e-01,   9.71772615e-03],
       [  1.26740802e-02,   5.66647970e-04,   6.76987618e-02, ...,
          2.52383947e-02,   1.17670270e-02,   6.42727688e-03]], dtype=float32)
>>> from deepviz_webui.imagecorpus import CIFAR10ImageCorpus
>>> corpus = CIFAR10ImageCorpus("../cifar-10-py-colmajor")
>>> from sklearn.decomposition import PCA
>>> pca = PCA(2)
>>> r = pca.fit_transform(probs)
>>> r
array([[-0.22592825, -0.03838477],
       [ 0.1176122 , -0.12934557],
       [ 0.15507133,  0.13634114],
       ...,
       [ 0.39083847,  0.14840141],
       [ 0.07537034,  0.03404486],
       [-0.135039  , -0.00479577]], dtype=float32)
>>> import pylab as pl
>>> pl.scatter(r[:, 0], r[:, 1], c=corpus._image_labels)
<matplotlib.collections.PathCollection object at 0x110cea790>
>>> pl.show()