PhysNet | Code for training PhysNet models | Machine Learning library

by MMunibas Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | PhysNet Summary

PhysNet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. PhysNet has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However PhysNet build file is not available. You can download it from GitHub.

Tensorflow implementation of PhysNet (see for details.

Support

Quality

Security

License

Reuse

Support

PhysNet has a low active ecosystem.

It has 69 star(s) with 25 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. On average issues are closed in 235 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of PhysNet is current.

Quality

PhysNet has 0 bugs and 0 code smells.

Security

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

PhysNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PhysNet is licensed under the MIT License. This license is Permissive.

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

Reuse

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

PhysNet has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Calculate the energy of the atomic properties
R Computes the energy of a given atomic properties
Compute the energy of atomic properties
Compute atomic properties
Calculate atomic properties
Calculate the interatomic distances between the given indices
Applies the given gradients to the gradients
Calculate the shared shared variance
Estimate the energy and forces
Calculates the energy and forces
Calculate energy and forces from a scaled atomic properties
Stdev of force magnitude
Calculate charges for atoms
Calculate the mean energy of each atom in the training set
Calculate the DPerA mean of the DperA
Stdev of partial charge
Stdev of energy peratom
Returns the mean force magnitude per atom
Softplus function
Build a train op
Run the loop
Calculate the weighted averages
Start the run thread
Returns the next batch
Calculate mean absolute error loss
Create a tf Summary object from a dictionary

Get all kandi verified functions for this library.

PhysNet Key Features

No Key Features are available at this moment for PhysNet.

PhysNet Examples and Code Snippets

No Code Snippets are available at this moment for PhysNet.

Community Discussions

Trending Discussions on PhysNet

BPF: How to set the jump value as the value stored in the accumulator?

OSError: [Errno 22] Invalid argument when using torch.load

QUESTION

BPF: How to set the jump value as the value stored in the accumulator?

Asked 2021-Jul-26 at 17:42

I am working with seccomp BPF and need to set the jump values (jt/jf/k) of a jump statement (conditional jump/jump always) as the value stored in the accumulator. Is this possible? I have a hunch that it is not, because the BPF verifier cannot check the jump values before loading the filter. If not, are there any workarounds?

...

ANSWER

Answered 2021-Jul-26 at 17:41

No, BPF doesn't support indirect branch instructions. Neither cBPF as used in seccomp-bpf nor eBPF does.

In the case of cBPF, you can check this in the documentation. You will see that instructions are defined as:

Source https://stackoverflow.com/questions/68502404

QUESTION

OSError: [Errno 22] Invalid argument when using torch.load

Asked 2020-Nov-10 at 13:01

I am trying to load my dataset and it was working before but all of the sudden this error started pooping up.

When I try to load it like this:

...

ANSWER

Answered 2020-Nov-10 at 13:01

This was a known issue (Issue#26998 and Issue#) caused by PR#20900. The problem happens because you're trying to load a file larger than 2GB, and it is specific to Windows in which "sizeof(long)=4 for both 32-bit and 64-system systems". This issue was fixed by PR#27069 and is only available in PyTorch 1.3+. Therefore, to fix this issue, please upgrade your PyTorch version.

Source https://stackoverflow.com/questions/64761518

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

Vulnerabilities

No vulnerabilities reported

Install PhysNet

You can download it from GitHub.
You can use PhysNet like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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