FeatureNet | Machining feature recognition | Machine Learning library

by zibozzb Python Version: Current License: GPL-3.0

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

kandi X-RAY | FeatureNet Summary

FeatureNet is a Python library typically used in Manufacturing, Utilities, Machinery, Process, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. FeatureNet has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However FeatureNet build file is not available. You can download it from GitHub.

We developed a novel framework using Deep 3D Convolutional Neural Networks (3D-CNNs) termed FeatureNet to learn machining features from CAD models of mechanical parts. FeatureNet learns the distribution of complex machining feature shapes across a large 3D model data set and discovers distinguishing features that help in recognition process automatically. To train FeatureNet, a large-scale mechanical part datasets of 3D CAD models with labeled machining features is synthetically constructed.

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FeatureNet has a low active ecosystem.

It has 30 star(s) with 15 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. On average issues are closed in 100 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FeatureNet is current.

Quality

FeatureNet has no bugs reported.

Security

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

License

FeatureNet is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

Compute the confusion matrix
Implements the convolutional network
Reads a CIFAR10 dataset

Get all kandi verified functions for this library.

FeatureNet Key Features

No Key Features are available at this moment for FeatureNet.

FeatureNet Examples and Code Snippets

No Code Snippets are available at this moment for FeatureNet.

Community Discussions

Trending Discussions on FeatureNet

Pytorch - TypeError: 'torch.Size' object cannot be interpreted as an integer

Tensorflow Estimator - High evaluation values on training data

QUESTION

Pytorch - TypeError: 'torch.Size' object cannot be interpreted as an integer

Asked 2018-Dec-03 at 14:58

Hi I am training a PyTorch model and occurred this error:

----> 5 for i, data in enumerate(trainloader, 0):

TypeError: 'torch.Size' object cannot be interpreted as an integer

Not sure what this error means.

You can find my code here :

...

ANSWER

Answered 2018-Dec-03 at 11:07

Your problem is the __len__ function. You cannot use the shape as return value.

Here is an example for illustration:

Source https://stackoverflow.com/questions/53588623

QUESTION

Tensorflow Estimator - High evaluation values on training data

Asked 2018-Nov-07 at 14:19

I'm using Tensorflow 1.10 with a custom Estimator. To test my training/evaluation loop, I just feed the same image/label into the network every time, so I expected the network to converge fast, which it does.

I'm also using the same image for evaluation, but get a much bigger loss value than when training. After training 2000 steps the loss is:

INFO:tensorflow:Loss for final step: 0.01181452

but evaluates to:

Eval loss at step 2000: 0.41252694

This seems wrong to me. It looks like the same problem as in this thread. Is there something special to consider, when using the evaluate method of Estimator?

Some more details about my code:

I've defined my model (FeatureNet) like here as an inheritance of tf.keras.Model with init and call method.

My model_fn looks like this:

...

ANSWER

Answered 2018-Nov-07 at 14:19

I've found, that the handling of BatchNormalization can cause such errors, like described here.

The usage of the control_dependencies in the model-fn solved the issue for me (see here).

Source https://stackoverflow.com/questions/53090463

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

Vulnerabilities

No vulnerabilities reported

Install FeatureNet

You can download it from GitHub.
You can use FeatureNet 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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