dendrites | Straight foward - easy to use neural network tool

by sbrki Python Version: Current License: Apache-2.0

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

kandi X-RAY | dendrites Summary

dendrites is a Python library. dendrites has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Straight foward - easy to use neural network tool in python3.

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Support

dendrites has a low active ecosystem.

It has 15 star(s) with 2 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 5 have been closed. On average issues are closed in 46 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of dendrites is current.

Quality

dendrites has 0 bugs and 0 code smells.

Security

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

dendrites code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

dendrites is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

dendrites 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.

dendrites saves you 136 person hours of effort in developing the same functionality from scratch.

It has 342 lines of code, 20 functions and 13 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Train the model
Run backpropagation step
Runs the model
Return the sigmoid of x
Adds two inputs to the network
Save the neural network
Load the neural network
Run the model

Get all kandi verified functions for this library.

dendrites Key Features

No Key Features are available at this moment for dendrites.

dendrites Examples and Code Snippets

No Code Snippets are available at this moment for dendrites.

Community Discussions

Trending Discussions on dendrites

How to update all the values in a BTreeSet?

Count protuberances in dendrite with openCV (python)

How can I retrieve from every using OWL API

QUESTION

How to update all the values in a BTreeSet?

Asked 2019-Feb-14 at 20:15

I have collection which is a field in a struct in some module. I want to update all the values in the collection from another module.

I wrote some code to mimic what I want to achieve. It's shortened a bit, but I think it has all needed parts. There is no struct holding the collection in this code, but imagine this is a getter which returns the collection. I added in comments how I think it should look.

...

ANSWER

Answered 2019-Feb-14 at 19:53

BTreeSet doesn't implement impl<'a, T> IntoIterator for &'a mut BTreeSet (that would break the tree).

You can only do this with types that implement IntoIterator with mut like impl<'a, T> IntoIterator for &'a mut Vec, example.

Source https://stackoverflow.com/questions/54697274

QUESTION

Count protuberances in dendrite with openCV (python)

Asked 2018-Jan-31 at 06:38

I'm trying to count dendritic spines (the tiny protuberances) in mouse dendrites obtained by fluorescent microscopy, using Python and OpenCV.

Here is the original image, from which I'm starting:

Raw picture:

After some preprocessing (code below) I've obtained these contours:

Raw picture with contours (White):

What I need to do is to recognize all protuberances, obtaining something like this:

Raw picture with contours in White and expected counts in red:

What I intended to do, after preprocessing the image (binarizing, thresholding and reducing its noise), was drawing the contours and try to find convex defects in them. The problem arose as some of the "spines" (the technical name of those protuberances) are not recognized as they en up bulged together in the same convexity defect, underestimating the result. Is there any way to be more "precise" when marking convexity defects?

Raw image with contour marked in White. Red dots mark spines that were identified with my code. Green dots mark spines I still can't recognize:

My Python code:

...

ANSWER

Answered 2018-Jan-31 at 06:38

I would approximate the contour to a polygon as Silencer suggests (don't use the convex hull). Maybe you should simplify the contour just a little bit to keep most of the detail of the shape.

This way, you will have many vertices that you have to filter: looking at the angle of each vertex you can tell if it is concave or convex. Each spine is one or more convex vertices between concave vertices (if you have several consecutive convex vertices, you keep only the sharper one).

EDIT: in order to compute the angle you can do the following: let's say that a, b and c are three consecutive vertices

Source https://stackoverflow.com/questions/48411742

QUESTION

How can I retrieve from every using OWL API

Asked 2017-Apr-23 at 10:18

I am new to OWL API hence I am facing some issues for retrieving data.

Suppose I have the following data:

...

ANSWER

Answered 2017-Apr-23 at 10:18

This should help:

Source https://stackoverflow.com/questions/43306382

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

Vulnerabilities

No vulnerabilities reported

Install dendrites

For more info, visit the documentation page.

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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