fastai_helpers -- Fastai utility functions

Key Files

stats.py

StatsRepo()
- .add(), .show_results(), .restore(), .save()

train.py

_get_learner()
- higher level function to create learner, inclusing cupping of pre-trained model
- model_cutter(): used for custom layer clipping of pre-trained model
lr_find()
- lrfinder + plots
- Learner.Recorder.plot2(): returns smoothed plot with suggestions
_train()
- supports both one-cycle and flat annealing
Learner.svalidate()
- silently validate -- compute validation results without updating Jupyter cell output
get_best_stats()
- Returns best result from recorder history
get_val_stats()
- Returns final result from recorder history
Thresholded metrics for multi-label classification:
- error_02(), accuracy_02(), error_05(), accuracy_05()
mishify()
- modifies resnet and xresnet models to use MISH activation function in place of RELU.
- note: Includes version of MISH with application (mish source: https://github.com/lessw2020/mish)

interpret.py

analyze_interp()
- Summary results for nterpreter including plots
combine_predictions()
- Generates composite prediction based on multiple interpreters
plot_confusion_matrix()
- plots confusion matrix with clean-up of distplay boundaries
plot_confusion_matrix_thresh()
- plots confusion matrix after thresholding values
- threshold_confusion_matrix(): computes confusion matric using threshold
interpretation_summary()
- Outputs various stats (summary, cm, threshold analysis)
get_accuracy()
- Computes accuract stat for interpreter
analyze_confidence()
- Plots confidence histograms
accuracy_vs_threshold()
- Plots accuract vs threshold value
show_incremental_accuracy()
- TBS
analyze_low_confidence()
- TBS

other_helpers.py

verify_gpu()
- checks GPU status
reset_seeds()
- initialized all random seeds to known value

Files requiring reorganization

helpers.py

_get_interp()
- ISSUE: Code may be too implementation specific