Comments (4)
Implementing the confusion matrix could be difficult because it looks like cuda-convnet's GPU workers only return the per-batch loss rather than the actual misclassifications.
from deepviz.
I implemented a confusion matrix in e6eb6e8. I'll upload a snapshot of the database to Dropbox in a little bit so you can test it out. I'd still like to add mouseover support for viewing a sample of the images that fall into each misclassification category (for example, mouseover to view 9 cats that were misclassified as dogs).
from deepviz.
f118f4d added support for viewing a sample of misclassified images.
Here's a link to a recent build of the stats_db: https://www.dropbox.com/s/t5zg9dqb0eoe6lv/stats_db.zip
Unizp that file and use that directory as the --model-stats
parameter for runserver.py
if you want to load the data into the app.
from deepviz.
I think there's a bug in the confusion matrix's image display because the images' classes displayed in the histograms don't match up with the classifications reported by the table. I'll investigate this afternoon.
from deepviz.
Related Issues (20)
- Images should be normalized according to the same scale across timesteps HOT 1
- Main content area should be scrollable HOT 2
- Interaction for viewing the filter while viewing the convolution of that filter and an input HOT 1
- Clustering of images based on fully-connected layer outputs HOT 1
- Display prediction probabilities as a histogram
- Layer graph shouldn't display neurons as sinks
- Show Images with Highest Activation for a Filter/Point
- Cluster images by fc10 output - display images closest to cluster center HOT 5
- Add filter weights to detail display on selection/mouseover.
- Error rates in model stats DB don't match up with convnet's final error HOT 5
- Display additional information on the layer graph HOT 1
- Argument size mismatch error while converting RGB image to png
- Error while using magic package
- Implement image preloading for filter views
- Show the filter outputs for individual input images HOT 1
- Visual feedback to show which layer is selected HOT 1
- Show weights for fully-connected layers
- Show pooling and neuron layers when images are selected HOT 2
- Record layer activations aggregated by image categories at all timestamps
- Add ability to select subset of filters, times, etc HOT 3
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from deepviz.