I am using Huber loss implementation in tf.keras in tensorflow 1.14.0 as follows:

huber_keras_loss = tf.keras.losses.Huber(
        delta=delta,
        reduction=tf.keras.losses.Reduction.SUM,
        name='huber_loss'
    )

I am getting the error AttributeError: module 'tensorflow.python.keras.api._v1.keras.losses' has no attribute 'Reduction'

I have tried using tf.losses.

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

There's a small mistake in the description of the embedding layer. It says

'Turns positive integers (indexes) into dense vectors of fixed size.'

but it should read

'Turns non-negative integers (indexes) into dense vectors of fixed size.'

as it expects indexes ranging from 0 to input_dim - 1.

See https://keras.io/layers/embeddings/

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

In recent ansible release, the default undefined class in jinja evaluation context is now `AnsibleU

In sklearn.preprocessing._encoders._BaseEncoder, columns with pd.Categorical dtype are converted to arrays.

https://github.com/scikit-learn/scikit-learn/blob/03ea20db0f9585fa0d44f4d3cae4b4c4a7c7f235/sklearn/preprocessing/_encoders.py#L60

If the categories ordering is explicitly specified by the user to the constructor of OneHotEncoder or OrdinalEncoder, then this is fine... but if 'a

When triggered, the DOWNLOAD_MAXSIZE setting raises an ERROR level logging event. I don't think it should be at this level. Error is "something went wrong"; I've explicitly told Scrapy I don't want files of this size, so it's not an error to not download them. WARN or even just INFO makes more sense.

I'm logging Error levels and above to stderr so I can keep track of them easily and it's bein

Context

We would like to add the following two APIs to the C++ frontend:

• torch::nn::TripletMarginLoss , which is the equivalent of Python API torch.nn.TripletMarginLoss.
• torch::nn::functional::triplet_margin_loss, which is the equivalent of Python API torch.nn.functional.triplet_margin_loss.

Steps

• Add torch::nn::TripletMarginLossOptions to `torch/csrc/api/i

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.

mypy error: pandas\core\arrays\categorical.py:1125:13: error: Incompatible types in assignment (expression has type "List[Any]", variable has type "Set[Any]") [assignment]

Code Sample, a copy-pastable example if possible

Python 3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" f

给你两个数组，arr1 和 arr2，

arr2 中的元素各不相同
arr2 中的每个元素都出现在 arr1 中
对 arr1 中的元素进行排序，使 arr1 中项的相对顺序和 arr2 中的相对顺序相同。未在 arr2 中出现过的元素需要按照升序放在 arr1 的末尾。

示例：

输入：arr1 = [2,3,1,3,2,4,6,7,9,2,19], arr2 = [2,1,4,3,9,6]
输出：[2,2,2,1,4,3,3,9,6,7,19]
 

提示：

arr1.length, arr2.length <= 1000
0 <= arr1[i], arr2[i] <= 1000
arr2 中的元素 arr2[i] 各不相同
arr2 中的每个元素 arr2[i] 都出现在 arr1 中

来源：力扣（LeetCode）
链接：