From 26f3d8368f3330d9c1eb719001a65499e45e5e63 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 10 Aug 2021 13:17:06 -0700 Subject: [PATCH] Add the getting started page to the external Tensorflow Responsible AI Guide. PiperOrigin-RevId: 389961144 --- g3doc/guide/get_started.md | 90 ++++++++++++++++++++- g3doc/guide/images/getting-started-img.png | Bin 0 -> 112061 bytes 2 files changed, 89 insertions(+), 1 deletion(-) create mode 100644 g3doc/guide/images/getting-started-img.png diff --git a/g3doc/guide/get_started.md b/g3doc/guide/get_started.md index 903cdd6..143a513 100644 --- a/g3doc/guide/get_started.md +++ b/g3doc/guide/get_started.md @@ -1,3 +1,91 @@ # Get Started -## Tips +Using TF Privacy + +This document assumes you are already familiar with differential privacy, and +have determined that you would like to implement TF Privacy to achieve +differential privacy guarantees in your model(s). If you’re not familiar with +differential privacy, please review +[the overview page](https://tensorflow.org/responsible_ai/privacy/guide). After +installing TF Privacy get started by following these steps: + +## 1. Choose a differentially private version of an existing Optimizer + +If you’re currently using a TensorFlow +[optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers), you +will most likely want to select an Optimizer with the name `DPKeras*Optimizer`, +such as [`DPKerasAdamOptimizer`] in [`TF Privacy`]. + +Optionally, you may try vectorized optimizers like +[`tf_privacy.VectorizedDPKerasAdamOptimizer`]. for a possible speed improvement +(in terms of global steps per second). The use of vectorized optimizers has been +found to provide inconsistent speedups in experiments, but is not yet well +understood. As before, you will most likely want to use an optimizer analogous +to the one you're using now. These vectorized optimizers use Tensorflow's +`vectorized_map` operator, which may not work with some other Tensorflow +operators. If this is the case for you, please +[open an issue on the TF Privacy GitHub repository](https://github.com/tensorflow/privacy/issues). + +## 2. Compute loss for your input minibatch + +When computing the loss for your input minibatch, make sure it is a vector with +one entry per example, instead of aggregating it into a scalar. This is +necessary since DP-SGD must be able to compute the loss for individual +microbatches. + +## 3. Train your model + +Train your model using the DP Optimizer (step 1) and vectorized loss (step 2). +There are two options for doing this: + +- Pass the optimizer and loss as arguments to `Model.compile` before calling + `Model.fit`. +- When writing a custom training loop, use `Optimizer.minimize()` on the + vectorized loss. + +Once this is done, it’s recommended that you tune your hyperparameters. For a +complete walkthrough see the +[classification privacy tutorial](../tutorials/classification_privacy.ipynb) + +## 4. Tune the DP-SGD hyperparameters + +All `tf_privacy` optimizers take three additional hyperparameters: + +* `l2_norm_clip` or $C$ - Clipping norm (the maximum Euclidean (L2) norm of + each individual gradient computed per minibatch). +* `noise_multiplier` or $σ$ - Ratio of the standard deviation to the clipping + norm. +* `num_microbatches` or $B$ - Number of microbatches into which each minibatch + is split. + +Generally, the lower the effective standard deviation $σC / B$, the better the +performance of the trained model on its evaluation metrics. + +The three new DP-SGD hyperparameters have the following effects and tradeoffs: + +1. The number of microbatches $B$: Generally, increasing this will improve + utility because it lowers the standard deviation of the noise. However, it + will slow down training in terms of time. +2. The clipping norm $C$: Since the standard deviation of the noise scales with + C, it is probably best to set $C$ to be some quantile (e.g. median, 75th + percentile, 90th percentile) of the gradient norms. Having too large a value + of $C$ adds unnecessarily large amounts of noise. +3. The noise multiplier $σ$: Of the three hyperparameters, the amount of + privacy depends only on the noise multiplier. The larger the noise + multiplier, the more privacy is obtained; however, this also comes with a + loss of utility. + +These tradeoffs between utility, privacy, and speed in terms of steps/second are +summarized here: + +![tradeoffs](./images/getting-started-img.png) + +Follow these suggestions to find the optimal hyperparameters: + +* Set $C$ to a quantile as recommended above. A value of 1.00 often works + well. +* Set $B$ = 1, for maximum training speed. +* Experiment to find the largest value of σ that still gives acceptable + utility. Generally, values of 0.01 or lower have been observed to work well. +* Once a suitable value of $σ$ is found, scale both $B$ and $σ$ by a constant + to achieve a reasonable level of privacy. diff --git a/g3doc/guide/images/getting-started-img.png b/g3doc/guide/images/getting-started-img.png new file mode 100644 index 0000000000000000000000000000000000000000..716bdd7886a22ff02c4dca3a4941b54ef8510e9c GIT binary patch literal 112061 zcmcG#bzED|(=eK#E!t8F6fYEacSwsnh2q7FOK=MkpvASg(*mWqyE_yTticITTtji^ z^8G#f-h1EoKL6bF`D}L2?8uopXLo02HxU|Y^7uI9H~;_uUr|9u69B-N2LOQHPp}># z!PZGn002A-TWM(xMQLeT4Og(0t%D^1pb(L)i>;@nOCjm?>1!3*^Z4iIM3yf=a!=TT zp(<}bydzQ|<6*>jDWq9qxEqmBJDLn;wW64eam>dN-h`5RMri zy4g8ZY7cl1OI`ZVaNm4yGOWR@`2+yseGMmJ#rX295e=G(^XZrl5WX4@7Ry)o(PJJS zv(60)-}>oXfmNxo>VS`|NKsTIO zQUw|%eV2vz_h(O!Eo&RVoQat$#GABle}Bwj^Bxv(Uga+-~U%zHVEy#=>rX-ai(YwldT5(OYQLgQbcHIc>y#^^j>9uPmqUU z;&7JHn7-TRTn`%rIS{xM)X{S4jEun(WR7V=TQRd)SpG2BdyqyEKUXx!q}Z4uS_^ZI 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