Residual-Networks | Tensorflow implementation of deep residual learning | Machine Learning library

by LantaoYu Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | Residual-Networks Summary

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

Implementation of Deep Residual Learning for Image Recognition.

Support

Quality

Security

License

Reuse

Support

Residual-Networks has a low active ecosystem.

It has 19 star(s) with 12 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 1043 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Residual-Networks is current.

Quality

Residual-Networks has 0 bugs and 0 code smells.

Security

Residual-Networks has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Residual-Networks code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Residual-Networks 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.

Reuse

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

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

Residual-Networks saves you 66 person hours of effort in developing the same functionality from scratch.

It has 171 lines of code, 12 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Initialize training data .
Residual unit norm .
Evaluate the given resnet .
Batch normalization .
Convolutional convolutional layer .
Returns the next batch .
Returns the next batch .
Resets the counter .
Softmax layer .

Get all kandi verified functions for this library.

Residual-Networks Key Features

No Key Features are available at this moment for Residual-Networks.

Residual-Networks Examples and Code Snippets

No Code Snippets are available at this moment for Residual-Networks.

Community Discussions

Trending Discussions on Residual-Networks

How should "BatchNorm" layer be used in caffe?

Conv 1x1 configuration for feature reduction

convert resnet implementation from caffe to tensorflow

Adding Accuracy and Loss Layer to RNN in CAFFE with DIGITS

QUESTION

How should "BatchNorm" layer be used in caffe?

Asked 2020-Jan-19 at 06:41

I am a little confused about how should I use/insert "BatchNorm" layer in my models.
I see several different approaches, for instance:

ResNets: "BatchNorm"+"Scale" (no parameter sharing)

"BatchNorm" layer is followed immediately with "Scale" layer:

...

ANSWER

Answered 2017-Jan-22 at 08:32

If you follow the original paper, the Batch normalization should be followed by Scale and Bias layers (the bias can be included via the Scale, although this makes the Bias parameters inaccessible). use_global_stats should also be changed from training (False) to testing/deployment (True) - which is the default behavior. Note that the first example you give is a prototxt for deployment, so it is correct for it to be set to True.

I'm not sure about the shared parameters.

I made a pull request to improve the documents on the batch normalization, but then closed it because I wanted to modify it. And then, I never got back to it.

Note that I think lr_mult: 0 for "BatchNorm" is no longer required (perhaps not allowed?), although I'm not finding the corresponding PR now.

Source https://stackoverflow.com/questions/41608242

QUESTION

Conv 1x1 configuration for feature reduction

Asked 2018-Dec-25 at 06:35

I am using 1x1 convolution in the deep network to reduce a feature x: Bx2CxHxW to BxCxHxW. I have three options:

x -> Conv (1x1) -> Batchnorm-->ReLU. Code will be output = ReLU(BN(Conv(x))). Reference resnet
x -> BN -> ReLU-> Conv. So the code will be output = Conv(ReLU(BN(x))) . Reference densenet
x-> Conv. The code is output = Conv(x)

Which one is most using for feature reduction? Why?

...

ANSWER

Answered 2018-Dec-25 at 06:35

Since you are going to train your net end-to-end, whatever configuration you are using - the weights will be trained to accommodate them.

BatchNorm?
I guess the first question you need to ask yourself is do you want to use BatchNorm? If your net is deep and you are concerned with covariate shifts then you probably should have a BatchNorm -- and here goes option no. 3

BatchNorm first?
If your x is the output of another conv layer, than there's actually no difference between your first and second alternatives: your net is a cascade of ...-conv-bn-ReLU-conv-BN-ReLU-conv-... so it's only an "artificial" partitioning of the net into triplets of functions conv, bn, relu and up to the very first and last functions you can split things however you wish. Moreover, since Batch norm is a linear operation (scale + bias) it can be "folded" into an adjacent conv layer without changing the net, so you basically left with conv-relu pairs.
So, there's not really a big difference between the first two options you highlighted.

What else to consider?
Do you really need ReLU when changing dimension of features? You can think of the reducing dimensions as a linear mapping - decomposing the weights mapping to x into a lower rank matrix that ultimately maps into c dimensional space instead of 2c space. If you consider a linear mapping, then you might omit the ReLU altogether.
See fast RCNN SVD trick for an example.

Source https://stackoverflow.com/questions/53919836

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 ?

...

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

Adding Accuracy and Loss Layer to RNN in CAFFE with DIGITS

Asked 2017-Apr-26 at 21:36

I have just installed digits with caffe on the backend. I am trying to train RNN with 50 layers on my dataset. To keep things simple, I initially have just three classes in my dataset namely roads, parks and ponds. By default, the above network dones not include accuracy and loss layers so it does not show any accuracy or loss on DIGITS interface during or after the training. To work that around, I just copied the relevant layers from AlexNet and put them at the end of RNN to see what's actually going on. I added following three layers from RNN

...

ANSWER

Answered 2017-Apr-26 at 17:52

You didn't broke the network. You just don't need two softmax layers. Problem probably is it is not converging. As for network initialization parameter I couldn't find Author's training.prototxt. He suggested seeing facebook's torch implementation in this PR which has some changes than the original implementation. One thing you can do is use training network from deepdetect . But that PR's one of the conclusion was that it didn't converge due to caffe's implementation problem with BatchNorm layer. deepdetect's author seems to disagree that it doesn't converge. Either way that seems to be fixed in this PR . So the summary is :

use the latest version of caffe
use deepdetect's net and solver.
First check if it converge's on imagenet or cifar (loss reduces and error decreases)
If it does then train on you own data
If not then we will need more information about your setting

Source https://stackoverflow.com/questions/43622308

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

Vulnerabilities

No vulnerabilities reported

Install Residual-Networks

You can download it from GitHub.
You can use Residual-Networks 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 .

Find more information at: