URL(s) with the issue:

Better performance with tf.data: https://www.tensorflow.org/guide/data_performance

Description of issue (what needs changing):

Currently, this guide seems to be the main documentation source for tf.data usage.
However, the differents steps shown do not seems to be optimal. For example:

• TFRecordDataset usage do not match the actual API (see #33048)
• Usa

The following do not yet have any pytests:

• boolean_algebra
• digital_image_processing
• divide_and_conquer
• hashes
• linear_algebra #1124
• networking_flow #1126
• traversals
• file_transfer_protocol # This might be difficult
• machine_learning
• neural_network

For ease of review, please only modify one directory per pull request.

It says in the documentation (the last section - "Working with Virtual Environments"):

For Python 3 add the following lines to the top of your .wsgi file:

activate_this = '/path/to/env/bin/activate_this.py'
with open(activate_this) as file_:
    exec(file_.read(), dict(__file__=activate_this))

However `activate_this.p

Proposing to bundle Caltech-101 dataset in the next release of Keras (or any future release) as a standard dataset for image-related application tasks.

It is small (compared to the whole ImageNet) but more difficult and challenging to train and experiment on than MNIST or CIFAR-10.

It was the precursor to Dr. Fei Fei Li's ImageNet p

Brew is listed as the recommended way of installing httpie on macOS. However, it doesn't work with auth plugins.

For example, if you pip3 install requests-hawk and then run http --help hawk will not show as an auth type. If you pip3 install httpie-oauth it will install httpie via pip as a dependency and overwrite the brew installed link in /usr/local/bin/http and now all the plugins wil

tldr; in multi-process environment (Celery) sessions might lead to request/responses being mixed up.

It is unsafe to use Session in a multi-process environment - if the fork happens after Session initialisation the underlying connection pool will be shared across both processes, leading to potentially dangerous and hard to debug issues.

I'm not sure what should happen - whather a code change

SUMMARY

I was working on an example of what not to do—namely, using the yum module instead of package for something meant to be platform agnostic... but then I realized my example (using the yum module on Debian) actually worked.

When I ran the yum task with -vvvv I found:

Running apt as the backend for the yum action plugin
Using module file /usr/local/lib/pytho

I think it will be readonable to add an option to use the original dataset when training final_estimator. This seems reasonable and has proved to be useful in some Kaggle competitions.

Reference: implementation from mlxtend
http://rasbt.github.io/mlxtend/api_subpackages/mlxtend.classifier/#stackingcvclassifier

use_features_in_secondary : bool (default: False)
If True, the meta-classifier w

I want to save images on a remote server via (FTP?). Is this possible with scrapy?
(I know about S3 or Google but i mean in a custom FTP server).

🚀 Feature

Add numerically stable cumulative logsumexp function. Also we have associated PR on cummax that is needed for numerically stable implementation (pytorch/pytorch#20240).

Motivation

This is useful when computing sum of probabilities and have different applications.

Pitch

Torch has cumsum and cumprod so I suggest logcumsumexp to be added.

Once certbot/certbot#7268 lands, we should warn about Certbot and its components deprecating Python 3.4 support. To do this we should:

1. Have acme use Python's warning system to register deprecation warnings.
2. Log warnings using the logging module in Certbot.

You can see how we did this for previous versions of Python in our git history.

There are issues in some docstrings where the See Also section is not complete. All of the following errors exist because there is no description for a referenced function or method. If these can get fixed then CI will be able to check all of the See Also section errors! The command to check for the errors is ./scripts/validate_docstrings.py --errors=SA04

pandas.melt: Missing description

给定一个原区间[x, y] (y>=x) 和 N个无序的目标区间[x1, y1],[x2, y2],[x3, y3] ... [xn, yn] 判断原区间是否在目标区间内（即\bigcup_{i=1}^{n}[x_i, y_i]是否成立）

例如给定原区间[1,6]和无序的目标区间[2, 3] , [1, 2], [3, 9]. 即可认为区间[1, 6]在[2, 3] , [1, 2], [3, 9]内， 如图所示：