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System information

• TensorFlow version: 1.13.1
• Doc Link: https://www.tensorflow.org/api_docs/python/tf/cus

As discussed in #3926, the following files do not have .py extensions because TensorFlow does not yet support the current Python.

• dynamic_programming/k_means_clustering_tensorflow.py_tf
• machine_learning/lstm/lstm_prediction.py_tf
• neural_network/gan.py_tf
• neural_network/input_data.py_tf

TODO: Restore the .py file extensions

(e.g. for links and images), because some of these examples are now being rendered in the docs.

Added by @fchollet in requests for contributions.

🐛 Bug

I stumbled upon excessive CPU usage for my training code running on GPU. After some investigations I found the culprit.
It basically was

x = torch.eye(256).to('cuda') 

To Reproduce

This is quick and loads single CPU core.

%%timeit
    torch.eye(181)
6.43 µs ± 218 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

This is 3 times slowe

It appears that the docs for Logistic Regression differ based on solvers and penalties. The "penalty" parameter states that "The ‘newton-cg’, ‘sag’ and ‘lbfgs’ solvers support only l2 penalties," while the "solver" parameter states that "‘newton-cg’, ‘lbfgs’, ‘sag’ and ‘saga’ handle L2 or no penalty" (attaching some screenshots). This was actually a little unclear to me, as I wasn't sure if the n

A minor cleanup for the future.

At the moment we use this in http11.py:

with suppress(AttributeError):
    txresponse._transport._producer.loseConnection()

[Once we drop support for Twisted < 18.4.0](twisted/twisted@3855923

Is your feature request related to a problem? Please describe.
Currently when users searches metric in Chart Explore Metric dropdown, the search first displays the "ending" string of the metric, even if there is an exact match. For example if you search for san_francisco and your metrics include population_in_san_francisco san_francisco and san_francisco_weather your first result woul

Hello everyone, I will explain the current status of manim in this issue.

Now there are three main manim versions, and their differences:

1. The master branch of 3b1b/manim: Rendering on GPU using OpenGL and moderngl. Support interaction and have higher efficiency.
2. ManimCommunity/manim: (@ManimCommunity

This is not exactly right, but we have an inconsitency in the argument specifications of using 'array_like' vs 'array-like', in argument types we should settle on one (prob 'array-like')

(pandas-dev) ~/pandas$ grep -r array-like --include '*.py' pandas|wc
     317    2259   24072
(pandas-dev) ~/pandas$ grep -r array_like --include '*.py' pandas|wc
     104     498    7414

Simila

First check

• I added a very descriptive title to this issue.
• I used the GitHub search to find a similar issue and didn't find it.
• I searched the FastAPI documentation, with the integrated search.
• I already searched in Google "How to X in FastAPI" and didn't find any information.
• I already read and followed all the tutorial in the docs and didn't find an answ