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Deep Learning Analysis Toolkits

Project description

DL Toolkits

:man_technologist:  Analytical tools useful for deep learning experiments  :woman_technologist:

 

Whenever I analyzed the results of the DL experiment, I had to re-implement the analysis function every time. So, I implement frequently used functions in this repository. New features continue to be implemented, and simple examples of function usages can be found in the examples directory.

Installation

You can install the package with pip command. Python>=3 are supported.

pip install dl-toolkits

You can check the version of the package using the following commands.

import toolkits
print(toolkits.__version__)

Modules

Visualization

Clustering quality

  • cluster.sse: Sum of squared error(SSE) [^1]
  • cluster.batch_sse: Sum of squared error(SSE) for batch input
  • cluster.nsse: SSE normalized by the squared distance to the nearest interfering centroid(nSSE) [^1]
  • cluster.batch_nsse: SSE normalized by the squared distance to the nearest interfering centroid(nSSE) for batch input
  • cluster.nearc: Top N nearest interfering centroid

Pretty print

Log parser

PyTorch helper function

References

[^1]: Yoon, Sung Whan, et al. "Xtarnet: Learning to extract task-adaptive representation for incremental few-shot learning." International Conference on Machine Learning. PMLR, 2020. [^2]: Mazumder, Pratik, Pravendra Singh, and Piyush Rai. "Few-Shot Lifelong Learning." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 3. 2021.

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