Deep Learning framework extension allowing more efficient backpropogation of gradient in a situation with branched computational graph structure
This is the Course Project for the DeepLearning University Course.
Library can be installed from the PyPI via
pip install fast_deep_rnn
core module contains the original core of the framework with Tensor
class implementation and the set of differentiable operations, organized
in Modules.
core_v2 module contains the alternative proposed implementation,
resulting in much faster gradient computing in RNN-s.
Notebook nbs/02_minimal_training.ipynb contains the simplest example
of model, having exponential growth in original gradient computing, and
benchmarking function to measure this growth. git tags
baseline_benchmark_results and solution_benchmark_results contain
corresponding benchmark results inside the notebook.
Notebook nbs/01_lstm_training.ipynb contains training of LSTM on
number sorting task, which became possible only after the implemented
optimization.
FAQs
Deep Learning framework extension allowing more efficient backpropogation of gradient in a situation with branched computational graph structure
We found that fast-deep-rnn demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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