nn | A lightweight neural network library using resilient | Machine Learning library

by mblum C++ Version: Current License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | nn Summary

nn is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. nn has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

nn is a lightweight neural network library using resilient propagation for adapting the weights.

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nn has a low active ecosystem.

It has 11 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

nn has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of nn is current.

Quality

nn has no bugs reported.

Security

nn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

nn is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

nn releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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nn Key Features

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nn Examples and Code Snippets

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Community Discussions

Trending Discussions on nn

Problem with FULLY_CONNECTED op in TF Lite

How to calculate the f1-score?

Extracting verbs except for the POStag from text with POS tag in R

Why does THREE.BoundingBoxHelper not display?

How to perform bicubic upsampling of image using pytorch?

Force BERT transformer to use CUDA

Is it possible to combine 2 neural networks?

UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach()

How and where can i freeze classifier layer?

Pytorch: Is it able to make a convolution module without bias have bias again?

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:

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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)

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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"

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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.

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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:

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ANSWER

Answered 2021-Jun-13 at 12:16

You can do this

Source https://stackoverflow.com/questions/67957965

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)

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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

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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.

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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

nn was tested on Ubuntu, Arch Linux and MacOS.
install [CMake](http://http://www.cmake.org/), [Eigen3](http://eigen.tuxfamily.org/) and [subversion](http://subversion.tigris.org/). On Ubuntu this is done as follows: sudo apt-get install cmake subversion libeigen3-dev
clone the nn repository or download it [here](https://bitbucket.org/mblum/nn/get/master.tar.gz)
change to the nn directory and create a build folder cd path/to/nn mkdir build
run cmake from within the build folder and compile the library using make cd build cmake .. make
run the example code ./tutorial
to compile unit tests for nn, run cmake with the option -DWITH_GTEST=ON cmake .. -DWITH_GTEST=ON make make test

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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