rhinoceros | 基于 web.py 的第一版 Medium 克隆

I'm essentially making a counter and it counts the number of times a name appears in a list. I'm trying to use a function so I can easily do it for all the names. It works fine when I don't make the code a function but as soon as I do it no longer returns the value of y.

I am new to the whole coding world. And I am currently creating a learning app for kids, and one of the categories included is taking a quiz. I wanted to shuffle all the questions and I was able to do so but the problem I am facing now is that the questions gets repeated

here is the code i used for Quiz questions activity

I am developing a plug-in for Rhinoceros 6 and making editions to App.Config file of Rhinoceros seems impossible so far. App.Config of the plug-in project has no effect on App.Config of Rhinoceros.

Below error message appears because I couldn't add providers and parameters to App.Config's entityFramework section.

This is for a hangman game, and the logic works great- the word fills in correctly, and the hangman gets more and more hanged with each word. However, the "graphics" are a little difficult for the user, as you can see below in the output:

I have a query regarding the extraction of VGG16/VGG19 features for my experiments.

The pre-trained VGG16 and VGG19 models have been trained on ImageNet dataset having 1000 classes (say c1,c2, ... c1000) and normally we extract the features from first and second fully connected layers designated ('FC1' and 'FC2'); these 4096 dimensional feature vectors are then used for computer vision tasks.

My question is that can we use these networks to extract features of an image that does not belong to any of the above 1000 classes ? In other words, can we use these networks to extract features of an image with label c1001 ? Remember that c1001 does not belong to the Imagenet classes on which these networks were initially trained on.

In the article available on https://www.pyimagesearch.com/2019/05/20/transfer-learning-with-keras-and-deep-learning/, I am quoting the following -

When performing feature extraction, we treat the pre-trained network as an arbitrary feature extractor, allowing the input image to propagate forward, stopping at pre-specified layer, and taking the outputs of that layer as our features

From the above text, there is no restriction to whether the image must necessarily belong to one of the Imagenet classes.

Kindly spare some time to uncover this mystery.

In the research papers, the authors simply state that they have used features extracted from VGG16/VGG19 network pre-trained on Imagenet dataset without giving any further details.

I am giving a case study for reference:

Animal with Attribute dataset (see https://cvml.ist.ac.at/AwA2/) is a very popular dataset with 50 animal classes for image recognition task. The authors have extracted ILSVRC-pretrained ResNet101 features for the above dataset images. This ResNet 101 network has been pre-trained on 1000 imagenet classes (different imagenet classes are available at https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a#file-imagenet1000_clsidx_to_labels-txt).

Also, the AWA classes are put as follows:

I have an array that I want to convert to a dictionary and I declared a function to do so but I get this error each time I compile "Cannot subscript a value of type '[String : [String]]' with an argument of type 'String.SubSequence' (aka 'Substring')"

my code is

I have a .NET Framework solution that I'm trying to set up with a pipeline on Azure DevOps. I'm getting an error when trying to restore packages though:

I am trying to update my hash's values after subtracting the values by three. For example,

I'm still getting my head around list comprehension in python, but I believe it's what I need for this task.