To run scripts from the editor, you’ll want the atom-script package. The language-lua and linter-lua packages are mandatory. Here’re some of the hiccups I’ve had when setting it up for use with Torch : If you’ve got the time and the mathematical foundations, it’s well worth your time to sit down and read it through.Ītom is looking to be my IDE of choice.
#Install aquaterm brew full#
Either way, it doesn’t affect the algorithm’s function - it’s just replacing a random sample with another random sample.įinally, my usual disclaimer: this post is a poor imitation of the full work. This seems unnecessary because $\hat T$ is set to $T + \gamma$ at the start of each step as well. Notesīoth in the arXiv paper and Science’s supplementary material, in case (2) the variable $\hat T$ is set to $T + \gamma$. It also differs from the algorithm proven in the paper (see §3 of the the supplementary materials), and doesn’t make any mention of budget or generalization error. Somewhat confusingly, it mixes code for the classifier in with the code for Thresholdout. If you’re looking for an implementation, code for the §5 experiments was released.
#Install aquaterm brew how to#
Unfortunately, they don’t say how to calculate that budget explicitly. In the implementation (see below), Dwork et al use different random variable distributions to the ones they use in the proof, and claim that the new distributions give a more reasonable budget. If you’ve got a thousand element holdout set and you want a generalization error of less than $\tau = 0.1$, you end up with a budget of $B = 10$ queries.įortunately, in practice it seems you can do better.
With maybe a W tensorflow/core/common_runtime/:1027] 0x19332b0 Compute status: Invalid argument: You must feed a value for placeholder tensor 'embeddings' with dtype double and shape dim \quad B \approx \tau^4 n^2$$ I tensorflow/core/kernels/fifo_:154] Skipping cancelled enqueue attempt , timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batch/fifo_queue, input_producer/Gather)]] If you’re playing around with TensorFlow and trying to populate queues that depend on placeholders, you might run into errors like this: W tensorflow/core/common_runtime/:1027] 0x7f4dc800f810 Compute status: Cancelled: Enqueue operation was cancelled
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