nn-c | Natural Neighbours interpolation | Natural Language Processing library

I have a column that contains a complicated string format. I would like to keep the first word only, and/or keep the first word in addition to certain other words.

I wish to keep certain key words in the string, such as 'RED', 'DB', 'APP', 'Infra', etc.

DATA

I have such Makefile with a content for creating a script:

I have installed ISTIO with the below configuration

Using Python 3.6 with Pytorch 1.3.1. I have noticed that some saved nn.Modules cannot be loaded when the whole module is being imported into another module. To give an example, here is the template of a minimum working example.

I've been reading the Hands-On Machine Learning textbook (2nd edition) by Aurélien Géron (textbook publisher webpage here). I've gotten into the content that applies CNNs to images. In the section titled Tensorflow Implementation of Chapter 14, they manually create filters that get passed to tf.nn.conv2d and applied to an image to produce a set of feature maps. After these manual filter examples, the book says:

in a real CNN you would normally define filters as trainable variables ... Instead of manually creating the variables, use the keras.layers.Conv2D layer.

The above quote implies to me that given identical inputs (and equivalent initializations), we should be able to derive identical outputs from tf.nn.conv2d and keras.layers.Conv2D. To validate this idea, I looked up whether the two functions were equivalent. According to this previously answered SO post, for convolution, the two functions are the same.

I set out to perform a simple test of their equivalence. I created a convolutional layer consisting of one feature map using a 7x7 filter (a.k.a: convolutional kernel) of all zeros that was implemented separately for tf.nn.conv2d and keras.layers.Conv2D. As expected, after summing all the pixel values in the difference of both images, this filter did cause the output images to have a value of zero for each pixel value. This difference of zero implies that the output images are identical.

I then decided to create the same 7x7 filter, but with all ones this time. Ideally, both functions should produce the same output, therefore the difference in the two output images should be zero. Unfortunately, when I check the difference in the output images (and sum the differences at each pixel), I get a nonzero sum value. Upon plotting the images and their difference, it is evident that they are not the same image (though they do look very similar at a glance).

After reading through the documentation for both functions, I believe that I am giving them equivalent inputs. What could I be doing/assuming incorrectly that is preventing both functions from producing identical outputs?

I have attached my code and versionining information below for reference. The code uses the scikit-learn china.jpg sample image as input and matplotlib.pyplot.imshow to help in visualizing the output images and their difference.

TF Version: 2.2.0-dev20200229

Keras Version: 2.3.1

Scikit-Learn Version: 0.22.1

Matplotlib Version: 3.1.3

Numpy Version: 1.18.1

I've just built a deep learning rig (AMD 12 core threadripper; GeForce RTX 2080 ti; 64Gb RAM). I originally wanted to install CUDnn and CUDA on Ubuntu 19.0, but the installation was too painful and after reading around a bit, I decided to switch to Windows 10...

After doing several installs of tensorflow-gpu, in and outside condas, I ran into further issues which I assumed was down to the CUDnn-CUDA-tensorflow compatibility, so uninstalled various versions of CUDA and tf. My output from nvcc --version:

I'm trying to simulate a network that continuously (at least 24h) exchange data, therefore I connected two Laptops (OS Windows) through Ethernet cable and give each Ethernet port a static IP address (192.168.0.1 and 192.168.0.2). The cable are connected with a switch and on the switch there is a port mirroring the data on Raspi because with Wireshark I would like to analyse the traffic and do some tests.

I found the code to simulate server and client here and I'm using multiconn-client.py and multiconn-server.py. If I test the code on localhost it does work fine but if I change the host with the static IP address of the machine simulating the server than I get WinError 10060. I read many other similar topics, but since my development environment is peculiar no solution was working for me.

Maybe it is worth to mention that the laptops are also connected to wifi, at the beginning I thought this was the problem, but when I turned the wifi off I had the same issue.

EDIT:

This is the Traceback for client.

I have a C++/WinRT/UWP project. I need the HWND and HINSTANCE to be able to correctly initialize Direct Input, otherwise DirectInput manage only to enumerate the keyboard and mouse but not the joysticks.. don't ask me why, I even tried to get the TopMostWindow from the HInstance got with GetModule, it just return NULL when running from my C++/WinRT/UWP app, but works when running from a console app.

documentation https://docs.microsoft.com/en-us/windows/win32/api/corewindow/nn-corewindow-icorewindowinterop I have no idea how to cast my CoreWindow to ICoreWindowInterop. CComPtr is not availble and not usable in C++/WinRT, conflicts with IUnkwnown.

In the method App::OnLaunched(LaunchActivatedEventArgs const& e) I get the current window like this

CoreWindow w = Window::Current().CoreWindow().GetForCurrentThread();

Then I don't know how to get the ICoreWindowInterop from it. CoreWindow is a ICoreWindow , but I don't see any explanation in the Microsoft documentation :/

I tried casting and reinterpret casting without success ( compilation error ). I'm no COM/Windows expert, so I'm quite lost now.

Thanks for the help Cheers, Seb

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