netscope | Neural network visualizer | Machine Learning library

by ethereon JavaScript Version: Current License: No License

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

kandi X-RAY | netscope Summary

netscope is a JavaScript library typically used in Artificial Intelligence, Machine Learning applications. netscope has no bugs, it has no vulnerabilities and it has medium support. You can download it from GitHub.

A web-based tool for visualizing neural network topologies. It currently supports UC Berkeley's Caffe framework.

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Support

netscope has a medium active ecosystem.

It has 851 star(s) with 328 fork(s). There are 31 watchers for this library.

It had no major release in the last 6 months.

There are 26 open issues and 4 have been closed. On average issues are closed in 12 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of netscope is current.

Quality

netscope has 0 bugs and 0 code smells.

Security

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

netscope code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

Top functions reviewed by kandi - BETA

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

Parses the peg .
ECMA - 262 13 . 12
Parse an object
Parses and parses an array
Parse a comment .
Creates a new instance
Builds an unexpected exception
Parses the current pairpair
Parses parsed arguments
Parse key pair .

Get all kandi verified functions for this library.

netscope Key Features

No Key Features are available at this moment for netscope.

netscope Examples and Code Snippets

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Community Discussions

Trending Discussions on netscope

convert resnet implementation from caffe to tensorflow

Counting the number of multiply-add operations (MAC) in Caffe CNN's architecture

QUESTION

convert resnet implementation from caffe to tensorflow

Asked 2017-Nov-12 at 15:08

I want to implement resnet 50 from scratch it is implemented in caffe by author of original paper,but i want tensorflow implementation due to this repository :https://github.com/KaimingHe/deep-residual-networks and therefor this image : http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006 I know every equivalent (in tensorflow),but i dont lknow the meaning of scale in place,after batch normalization,can you explain me the meaning and also "use globale state " parameter in batchnorm ?

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ANSWER

Answered 2017-Nov-12 at 15:07

An "in-place" layer in caffe simply hints caffe to save memory: instead of allocating memory for both input and output of the net, "in-place" layer overrides the input with the output of the layer.
Using global state in "BatchNorm" layer means using the mean/std computed during training and not updating these values any further. This is the "deployment" state of BN layer.

Source https://stackoverflow.com/questions/47250205

QUESTION

Counting the number of multiply-add operations (MAC) in Caffe CNN's architecture

Asked 2017-Jun-13 at 13:22

Lately I've been benchmarking some CNNs regarding time, # of multiply-add operations (MAC), # of parameters and model size. I have seen some similar SO questions (here and here) and in the latter, they suggest using Netscope CNN Analyzer. This tool allows me to calculate most of the things I need just by inputing my Caffe network definition.

However, the number of multiply-add operations of some architectures I've seen in papers and over the internet doesn't match what Netscope is outputting, whereas other architectures match. I'm always comparing either FLOPs or MAC with the MACC column in netscope, but there a ~10x factor that I'm forgetting at some point (check table bellow for more detail).

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ANSWER

Answered 2017-Jun-13 at 13:22

I've found what was causing the discrepancy between Netscope and the information I'd found in papers. Most preset architectures in Nestcope were using a batch size of 10 (this is the case for VGG and GoogLeNet, for example), therefore the x10 factor that multiplied the number of mult-add operations.

Source https://stackoverflow.com/questions/44507612

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