Examine usefulness of representations for classification, through development and across layers of network. Will be used to expand Anna Birbeck's project relating AoA and layer in network
- Examination of development of CORnet-S representations
- Loads a checkpoint that is the result of training
- Freezes weights in convolutional network
- Adds a logistic regression to each layer
imagenet data, with train and val_in_folders directories GPU machine launch.json contains arguments for running of eval_linear.py with VSCODE checkpoints (e.g., from s3://neurana-imaging/rhodricusack/cornet/)
(1) Ran CORnet and get models from multiple epochs of training (2) Ran eval_linear.sh (or eval_linear_2019-08-31.sh) to
- for each epoch and conv, load model and add logistic regression layer
- train up
- validate
- validate on each aoa category separately (given --aoaval) (3) Ran summarize_performance.py to summarize, make graphs and do stats
linearclass: was fine, but didn't record reglog layers linearclass_v3: main run. Initially, epochs 0,5,10,20,30 analysed. But will add 1,2,3,4,15,25,35 using eval_linear_2019-08-31.sh
Based on code from DeepCluster from Facebook Research https://github.com/facebookresearch/deepcluster and CORnet from Dicarlo lab at MIT https://github.com/dicarlolab/CORnet