Neural-nets | basic scikit-learn compatible neural network library | Machine Learning library

by NicolasHug Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | Neural-nets Summary

Neural-nets is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. Neural-nets has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

A basic scikit-learn compatible NN library for Python 3, built from scratch only using numpy.

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Neural-nets has a low active ecosystem.

It has 6 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Neural-nets has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Neural-nets is current.

Quality

Neural-nets has no bugs reported.

Security

Neural-nets has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Neural-nets does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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Neural-nets releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed Neural-nets and discovered the below as its top functions. This is intended to give you an instant insight into Neural-nets implemented functionality, and help decide if they suit your requirements.

Runs the optimizer
Check the gradients of the function
Backpropagate backward
Perform the forward computation
Returns batch of training data
Fit the model
Implementation of the gradient function
Perform the forward transformation
Sigmoid function
Generate random spins
Plot the classification boundaries of the classification
Predict class for X
Predict new features
Predict for features
R derivative of sigmoid

Get all kandi verified functions for this library.

Neural-nets Key Features

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Neural-nets Examples and Code Snippets

No Code Snippets are available at this moment for Neural-nets.

Community Discussions

Trending Discussions on Neural-nets

Is it possible to train a neural netowrk using a loss function on unseen data (data different from input data)?

How could we get the pixel's color of a mesh when we click on it, using Javascript?

Scaling [-1,1] for Neural Networks: Also for DummyVars?

QUESTION

Is it possible to train a neural netowrk using a loss function on unseen data (data different from input data)?

Asked 2020-Sep-21 at 17:53

Normally, a loss function may be defined as L(y_hat, y) or L(f(X), y), where f is the neural network, X is the input data, y is the target. Is it possible to implement (preferably in PyTorch) a loss function that depends not only on the input data X, but also on X' (X != X)?

For example, let's say I have a neural network f, input data (X,y) and X'. Can I construct a loss function such that

f(X) is as close as possible to y, and also
f(X') > f(X)?

The first part can be easily implemented (PyTorch: nn.MSELoss()), the second part seems to be way harder.

P.S: this question is a reformulation of Multiple regression while avoiding line intersections using neural nets, which was closed. In the original data, input data and photos with a theoretical example are available.

...

ANSWER

Answered 2020-Sep-21 at 17:53

Yes it is possible. For instance, you can add a loss term using ReLU as follows:

Source https://stackoverflow.com/questions/63997404

QUESTION

How could we get the pixel's color of a mesh when we click on it, using Javascript?

Asked 2018-Mar-28 at 16:50

Currently in the page we have a mesh loaded with TreeJS and displayed in a canvas:

How could we get the color of the point where we click on?

I have tried as it has been suggested here: Getting the color value of a pixel on click of a mesh with three.js , to create a canvas with a 2d context on top of the one using webgl context.

The problem is that when we convert the model to PNG, the image is white:

Our img 2d src:

If we click on it:

So then the console logs that the color is: 0 0 0 0

Also I will show the code where we generate the webgl canvas and the 2d canvas:

...

ANSWER

Answered 2018-Mar-27 at 21:13

WebGLRenderer.readRenderTargetPixels

This gives you access to the render target's buffer, much like how you would read the data directly from a 2D canvas's ImageData buffer.

Source https://stackoverflow.com/questions/49495681

QUESTION

Scaling [-1,1] for Neural Networks: Also for DummyVars?

Asked 2018-Jan-13 at 20:02

I have general question regarding the scaling of predictors in a neural network. I'm using the avNNet algorithm in R / Caret for a regression; I have both categorical and numerical predictors.

As far as I have understood, predictors have to be scaled prior to the modeling step:

For lack of better prior information, it is common to standardize each input to the same range or the same standard deviation. [...] In particular, scaling the inputs to [-1,1] will work better than [0,1] (http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html)

If I scale my continuous predictors to the range [-1,1], what about my categorical predictors which are coded as [0 | 1]? Should I replace the zeros by -1?

Kind regards,

Requin

...

ANSWER

Answered 2017-Nov-20 at 16:36

No. The categories are of a different conceptual type (and data type) from the inputs or the weights. The categories are an enumeration (0, 1, 2, ...), and are typically distinct from one another, i.e. category 0 is no more similar to category 1 than it is to category 150.

The weights are on a continuum up floating-point values; this algorithm works best when those values are in the same range for each dimension (input feature) and evenly distributed about 0.

Scale the inputs as described; leave the categories just as you have them, at 0 | 1.

Source https://stackoverflow.com/questions/47395444

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

Vulnerabilities

No vulnerabilities reported

Install Neural-nets

You’d be much happier with scikit-learn default implementation, BUT if you like to live dangerously:.
First install scipy, numpy, matplotlib and scikit-learn (only used for dataset management). Can be done with ``` $ pip install -r requirements.txt ```
Then: ``` $ python setup.py install ```