nn | Fast and simple Neural Network for node.js | Runtime Evironment library

 by   ttezel JavaScript Version: Current License: No License

kandi X-RAY | nn Summary

kandi X-RAY | nn Summary

nn is a JavaScript library typically used in Server, Runtime Evironment, Nodejs applications. nn has no bugs, it has no vulnerabilities and it has low support. You can install using 'npm i simple-nn' or download it from GitHub, npm.

Fast and simple Neural Network for node.js
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              nn has a low active ecosystem.
              It has 97 star(s) with 19 fork(s). There are 6 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 4 open issues and 0 have been closed. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of nn is current.

            kandi-Quality Quality

              nn has 0 bugs and 0 code smells.

            kandi-Security Security

              nn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              nn code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              nn does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              nn releases are not available. You will need to build from source code and install.
              Deployable package is available in npm.
              Installation instructions are not available. Examples and code snippets are available.

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            nn Key Features

            No Key Features are available at this moment for nn.

            nn Examples and Code Snippets

            No Code Snippets are available at this moment for nn.

            Community Discussions

            QUESTION

            Problem with FULLY_CONNECTED op in TF Lite
            Asked 2021-Jun-15 at 13:22

            I'd like to run a simple neural network model which uses Keras on a Rasperry microcontroller. I get a problem when I use a layer. The code is defined like this:

            ...

            ANSWER

            Answered 2021-May-25 at 01:08

            I had the same problem, man. I want to transplant tflite to the development board of CEVA. There is no problem in compiling. In the process of running, there is also an error in AddBuiltin(full_connect). At present, the only possible situation I guess is that some devices can not support tflite.

            Source https://stackoverflow.com/questions/67677228

            QUESTION

            How to calculate the f1-score?
            Asked 2021-Jun-14 at 07:07

            I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.

            My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall)) but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code? (Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)

            ...

            ANSWER

            Answered 2021-Jun-13 at 15:17

            You can use sklearn to calculate f1_score

            Source https://stackoverflow.com/questions/67959327

            QUESTION

            Extracting verbs except for the POStag from text with POS tag in R
            Asked 2021-Jun-13 at 20:09

            I am new in R. I tried to gather the verbs ("/VB","/VBD","/VBG","/VBN","/VBP","/VBZ") using "openNLP" package (Note that 'udpipe' does not work in my environment). I have a sentence mixed with the tag as below.

            "Doing/VBG work/NN as/IN always/RB ./. playing/VBG soccer/NN is/VBZ good/JJ ./. I/PRP do/VBP that/IN"

            How can I achieve the verbs without POS tags? The answer I am trying to get in this example is

            "doing", "playing", "is", "do"

            ...

            ANSWER

            Answered 2021-Jun-13 at 20:09
            your requested example:

            Source https://stackoverflow.com/questions/67961637

            QUESTION

            Why does THREE.BoundingBoxHelper not display?
            Asked 2021-Jun-13 at 13:55

            I have previously managed to display the box, but here, where I have stripped everything down in order to experiment with expanding boxes by positioning collada models, the box won't show.

            ...

            ANSWER

            Answered 2021-Jun-13 at 13:55

            First of all, BoundingBoxHelper is now BoxHelper. It seems you are using a very old release of three.js.

            Besides, your collada() function is not synchronous. After executing collada("14",pobj); and collada("7",pobj); the assets are not yet loaded which means you are computing the bounding box for an empty group object.

            I suggest you wait until the Collada models have been loaded via THREE.LoadingManager and then add the helper to your scene.

            Alternatively, ensure to call helper.update() in your animation loop. This approach is wasteful however if the object is static and does not change. So it's probably better to use the onLoad() callback of a loading manager.

            Source https://stackoverflow.com/questions/67957274

            QUESTION

            How to perform bicubic upsampling of image using pytorch?
            Asked 2021-Jun-13 at 12:16

            I have png image. I want to upsample it using bicubic interpolation. I found this function in pytorch:

            ...

            ANSWER

            Answered 2021-Jun-13 at 12:16

            QUESTION

            Force BERT transformer to use CUDA
            Asked 2021-Jun-13 at 09:57

            I want to force the Huggingface transformer (BERT) to make use of CUDA. nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0 or .to(cuda:0).

            The code below is basically a customized part from german sentiment BERT working example

            ...

            ANSWER

            Answered 2021-Jun-12 at 16:19

            You can make the entire class inherit torch.nn.Module like so:

            Source https://stackoverflow.com/questions/67948945

            QUESTION

            Is it possible to combine 2 neural networks?
            Asked 2021-Jun-13 at 00:55

            I have a NET like (exemple from here)

            ...

            ANSWER

            Answered 2021-Jun-07 at 14:26

            The most naive way to do it would be to instantiate both models, sum the two predictions and compute the loss with it. This will backpropagate through both models:

            Source https://stackoverflow.com/questions/67872719

            QUESTION

            UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach()
            Asked 2021-Jun-12 at 23:00

            I'm new on PyTorch and I'm trying to code with it so I have a function called OH which tack a number and return a vector like this

            ...

            ANSWER

            Answered 2021-Apr-30 at 23:19

            the problem is that you are receiving a tensor on the act function on the Network and then save it as a tensor just remove the tensor in the action like this

            Source https://stackoverflow.com/questions/67341208

            QUESTION

            How and where can i freeze classifier layer?
            Asked 2021-Jun-12 at 20:29

            If I need to freeze the output layer of this model which is doing the classification as I don't need it.

            ...

            ANSWER

            Answered 2021-Jun-11 at 15:33

            You are confusing a few things here (I think)

            Freezing layers

            You freeze the layer if you don't want them to be trained (and don't want them to be part of the graph also).

            Usually we freeze part of the network creating features, in your case it would be everything up to self.head.

            After that, we usually only train bottleneck (self.head in this case) to fine-tune it for the task at hand.

            In case of your model it would be:

            Source https://stackoverflow.com/questions/67939448

            QUESTION

            Pytorch: Is it able to make a convolution module without bias have bias again?
            Asked 2021-Jun-12 at 12:48

            After instantiating a 2D convolution with conv = nn.Conv2d(8, 8, 3, bias=False), whose member bias should be None, is it able to give conv a legal bias again (whether with random initialization or determined values)?

            I observed that bias in other default convolution modules is of the type Parameter, so I suspect there are extra procedures beyond simply conv.bias = torch.tensor(...) to make the new bias legal for conv.

            ...

            ANSWER

            Answered 2021-Jun-12 at 12:48

            Yes, it is possible to set the bias of the conv layer after instantiating. You can use the nn.Parameter class to create bias parameter and assign to conv object's bias attribute.

            To show this I have created a simple Conv2d layer and assigned zero to the weights and ones to bias.

            Source https://stackoverflow.com/questions/67948404

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install nn

            You can install using 'npm i simple-nn' or download it from GitHub, npm.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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