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Transfer learning with pytorch engine on fresh fruit dataset #2070
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api/src/main/java/ai/djl/training/loss/SoftmaxCrossEntropyLoss.java
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api/src/main/java/ai/djl/training/loss/SoftmaxCrossEntropyLoss.java
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api/src/main/java/ai/djl/training/tracker/FixedPerVarTracker.java
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examples/src/test/java/ai/djl/examples/training/TransferFreshFruitTest.java
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Codecov ReportBase: 72.08% // Head: 70.33% // Decreases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## master #2070 +/- ##
============================================
- Coverage 72.08% 70.33% -1.76%
- Complexity 5126 6147 +1021
============================================
Files 473 615 +142
Lines 21970 27301 +5331
Branches 2351 2958 +607
============================================
+ Hits 15838 19201 +3363
- Misses 4925 6662 +1737
- Partials 1207 1438 +231
Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here. ☔ View full report at Codecov. |
examples/src/main/java/ai/djl/examples/training/transferlearning/TransferFreshFruit.java
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examples/src/main/java/ai/djl/examples/training/transferlearning/TransferFreshFruit.java
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This helps divide the simple trackers from the ones that can be per-parameter. Maybe we can also generalize the parameter tracker to handle other cases as a multi-tracker, but for now it should better represent the situation that is happening with the fixedPerValueTracker.
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I updated this with a new ParameterTracker abstraction which I think should make the situation more maintainable and easier to get "correct" in the future rather than forgetting to update the parameter id. With this change, I think it looks good to merge
…alibrary#2070) * ATLearning on PyTorch, frozen pretrained model Co-authored-by: Frank Liu <frankfliu2000@gmail.com> Co-authored-by: Zach Kimberg <kimbergz@amazon.com>
Description
In this PR, the following features are enabled:
Criteria.builder().optOption(String key, String value)
, when loading the traced pretrained model.