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Plot histograms of parameters to tensorboard #432
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Summary: Pull Request resolved: facebookresearch#432 - Plot the histogram of weights for every parameter in the model at the end of every train phase. - Updated the various scalars plotted to Tensorboard to have their own tags, just like "Speed" to organize things better Adding the activations and gradients is non-trivial since they depend on the input, so skipping that for now. Reviewed By: vreis Differential Revision: D20427992 fbshipit-source-id: d157f73eac3e910733f41cdccd40087431805b25
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Summary: Pull Request resolved: facebookresearch#432 - Plot the histogram of weights for every parameter in the model at the end of every train phase. - Updated the various scalars plotted to Tensorboard to have their own tags, just like "Speed" to organize things better Adding the activations and gradients is non-trivial since they depend on the input, so skipping that for now. Reviewed By: vreis Differential Revision: D20427992 fbshipit-source-id: 148b5e52cb72377c38a6c0a379c669476f9033b2
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Summary: Pull Request resolved: facebookresearch#432 - Plot the histogram of weights for every parameter in the model at the end of every train phase. - Updated the various scalars plotted to Tensorboard to have their own tags, just like "Speed" to organize things better Adding the activations and gradients is non-trivial since they depend on the input, so skipping that for now. Reviewed By: vreis Differential Revision: D20427992 fbshipit-source-id: 49a552cb2099782c4fa219f2a402aa89478bccea
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Summary: Pull Request resolved: #432 - Plot the histogram of weights for every parameter in the model at the end of every train phase. - Updated the various scalars plotted to Tensorboard to have their own tags, just like "Speed" to organize things better Adding the activations and gradients is non-trivial since they depend on the input, so skipping that for now. Reviewed By: vreis Differential Revision: D20427992 fbshipit-source-id: b46b12f3dbf6ac4d49f318b0b2d77548fc2d98a9
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Summary:
Adding the activations and gradients is non-trivial since they depend on the input, so skipping that for now.
Differential Revision: D20427992