It looks like experimental_run_tf_function was removed from tf.keras.Model.compile in this commit a few days ago: tensorflow/tensorflow@c73c99c#diff-de9b96ac2d81503324cbbbe21732031fR1159

In Horovod, this flag / graph mode is necessary in order for Optimizer.get_gradients() to be called, which aggregates gradi

Are there any references on how to create a good design diagram? What do the different colors mean? What do the dashed lines mean?

Sorry if this is a basic question but I don't even know where to start searching for more information. This is the first page I saw that had the diagrams in the format that I've seen before.

There are some interesting algorithms in simulation from Physics, Chemistry, and Engineering especially regarding Monte Carlo simulation: Heat Bath algorithm, Metro-Police algorithm, Markov Chain Monte Carlo, etc.

Huge and nice collection and also getting very much appreciated from the community.
It would be great if somebody can translate into English then it will be reaching out to global.

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

Small thing, but costed me several hours to find :)
In the documentation example of Siamese mnist .

We see a code for contrastive loss, based on a paper. But the labels in this function are reversed from
the paper. Meaning in the paper Y=0 if X1,X2 are from same domain, Y=1 other

In = syntax,

• double quotes (")
• back slashes (\)
• non-ascii characters

are escaped in json.dumps:

$ http -v httpbin.org/post                                      \
    dquote='\"'                                                 \
    multi-line='line 1\nline 2'                               

Looks like www.python-requests.org and docs.python-requests.org are redirecting to https://2.python-requests.org and failing SSL negotiation, making the site appear down. Google links are all dead, documentation links don't work, etc.

http://2.python-requests.org redirects to https://requests.kennethreitz.org/en/master/, which works.

Expected Result

Website should appear or redirect to

SUMMARY

This is a follow-up to #16903.

Setting the new env var ANSIBLE_DUPLICATE_YAML_DICT_KEY implemented in #56933 to error outputs a cryptic ERROR! Unexpected Exception, this is probably a bug: 'NoneType' object has no attribute 'line' instead of the message defined [here](https://github.com/ansible/ansible/blob/d335d7a62c022d702a29a0ff55cd0c526ec2c5ad/lib/ansible/parsing/yaml

Description

ValueError: Unknown label type: 'unknown' thrown when passing sparse matrix y in RandomForestClassifier.fit.

The reason is that several numpy functions are called on the variable:

• atleast_1d https://github.com/scikit-learn/scikit-learn/blob/f65cdf2d5e5f2b753393190598f9c9f63f34dbe1/sklearn/ensemble/_forest.py#L307
• copy https://github.com/scikit-learn/scikit-learn/bl

https://github.com/aymericdamien/TensorFlow-Examples/blob/9e1bb504f5d0f209d000997ce2ad95bb891798ab/examples/3_NeuralNetworks/bidirectional_rnn.py#L81

I think "outputs [-1]" and "outputs [0]" are equivalent (reversed) in this line of code, but the former (89%) works better than the latter (86%). Why?

Description

Hi, it seems that #3739 is not doing what the documentation describe:

os.environ.get('SCRAPY_CHECK') is returning None in my contract check.

Steps to Reproduce

1. Create a project from scratch
2. Add a random spider
3. Contract code is as follow

    def parse(self, response):

Current implementation does sequential sigmoid_out and mul_. We can get better performance by fusing this operations together.

• face_recognition version: 1.2.3
• Python version: 3.6
• Operating System: OSX

Description

I've been experimenting with using SVMs for working with multiple faces per person, based on the example for SVM here.

Here are the challenges I am facing:

• using clf.predict ([ref](https://gith

Description

The instructor in the above mentioned video has created a new version of the same tutorial, which can be found here

Why

It is always good to keep resources and tutorials up-to-date. The new video talks about namespaces, chroot and cgroups, and speaks about containers at a greater depth.

Is this something you're interest

This is for the website repo and there's an equivalent issue at certbot/website#512 but I want to make sure we don't forget this.

数轴上放置了一些筹码，每个筹码的位置存在数组 chips 当中。

你可以对 任何筹码 执行下面两种操作之一（不限操作次数，0 次也可以）：

将第 i 个筹码向左或者右移动 2 个单位，代价为 0。
将第 i 个筹码向左或者右移动 1 个单位，代价为 1。
最开始的时候，同一位置上也可能放着两个或者更多的筹码。

返回将所有筹码移动到同一位置（任意位置）上所需要的最小代价。

示例 1：

输入：chips = [1,2,3]
输出：1
解释：第二个筹码移动到位置三的代价是 1，第一个筹码移动到位置三的代价是 0，总代价为 1。
示例 2：

输入：chips = [2,2,2,3,3]
输出：2
解释：第四和第五个筹码移动到位置二的代价都是 1，所以最小总代价为 2。
 

提示：

1 <= chips.length <= 1

I think listing anti-patterns with some basic reasoning about "why not" is a good idea.

Example - singleton. Although #256 has "won't fix" label

• it is in PRs section, and people (if searching history at all) are searching issues first.
• it was misspelled, Singelton instead of Singleton, therefore impossible to find

Listing most popular anti-patterns (without actual implementation) shou

项目推荐

• 项目地址：https://github.com/nushell/nushell

• 类别：Rust

• 项目后续更新计划：
  该项目已达到最低可行的产品质量水平。虽然贡献者将它作为日常驱动程序，但它可能对某些命
  令不稳定。未来版本将填补缺失的功能并提高稳定性。它的设计也随着成熟而变化。

  Nu附带了一组内置命令（如下所示）。如果命令未知，命令将弹出并执行它（在 Windows 上使
  用 cmd 或在 Linux 和 MacOS 上使用 bash），正确地通过 stdin，stdout 和 stderr，所以像你的日常 git 工作流程甚至 vim 可以正常工作。

  还有一本关于 Nu 的书，目前正在进行中。

• 项目描述：这是一个 Github 时代下，一个更加现代的 shell。Nushell 将 shell 命

https://github.com/pandas-dev/pandas/blob/62a87bf4a2af02a8d3bc271ad26e5994292b8e6a/pandas/io/pickle.py#L95

read_pickle does not accept google storage URL (in form "gs://bucket-name/path/file.pkl") as input

While this code works fine:
with tf.io.gfile.GFile("gs://bucket-name/path/file.pkl", "rb") as infile: df = pd.read_pickle(infile, compression=None)

Might be reasonable to

I was having a very hard time figuring out

fill = A.stack().mean()
A.add(B, fill_value=fill)

fill = 4.5. However I computed a value of 3.2 because I was taking the mean from the column of A not the DataFrame A.
This coming after the Indexing chapter where "explicit is better than implicit." I was thinking that this should be a little more explicit.