Triplet-Loss | Use Triplet Loss to finetune and train a CNN pre | Machine Learning library

by shivsondhi Python Version: Current License: No License

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kandi X-RAY | Triplet-Loss Summary

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

Use Triplet Loss to finetune and train a CNN pre-trained on the Imagenet dataset

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Triplet-Loss has a low active ecosystem.

It has 24 star(s) with 13 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 0 have been closed. On average issues are closed in 510 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Triplet-Loss is current.

Quality

Triplet-Loss has 0 bugs and 1 code smells.

Security

Triplet-Loss has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Triplet-Loss code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

Triplet-Loss 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.

Triplet-Loss saves you 42 person hours of effort in developing the same functionality from scratch.

It has 111 lines of code, 5 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Creates a FreesNet50 model .
Generate a data generator .
Computes the loss function .
Calculate a triplet loss function .

Get all kandi verified functions for this library.

Triplet-Loss Key Features

No Key Features are available at this moment for Triplet-Loss.

Triplet-Loss Examples and Code Snippets

No Code Snippets are available at this moment for Triplet-Loss.

Community Discussions

Trending Discussions on Triplet-Loss

Implement mask for anchor-negative in list of triplets

How to apply Triplet Loss for a ResNet50 based Siamese Network in Keras or Tf 2

How does the Tensorflow's TripletSemiHardLoss and TripletHardLoss and how to use with Siamese Network?

Keras style lossless triplet loss

QUESTION

Implement mask for anchor-negative in list of triplets

Asked 2022-Feb-01 at 13:15

I have a generator in producing my TensorFlow data, as triplets (anchor, positive, negative), in batches. Each batch is a list of such triplets, making up the list labels. Using code from Moindrot's blog on triplet loss we get a mask for positives and negatives: With

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ANSWER

Answered 2022-Feb-01 at 13:15

If you really only want anchor-negative True in the negative mask, you can accomplish this with a mix of tf.tile, tf.linalg.band_part and tf.transpose:

Source https://stackoverflow.com/questions/70895684

QUESTION

How to apply Triplet Loss for a ResNet50 based Siamese Network in Keras or Tf 2

Asked 2021-Jan-06 at 07:52

I have a ResNet based siamese network which uses the idea that you try to minimize the l-2 distance between 2 images and then apply a sigmoid so that it gives you {0:'same',1:'different'} output and based on how far the prediction is, you just flow the gradients back to network but there is a problem that updation of gradients is too little as we're changing the distance between {0,1} so I thought of using the same architecture but based on Triplet Loss.

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ANSWER

Answered 2021-Jan-06 at 07:52

Following this answer of mine, and with role of TripletSemiHardLoss in mind, we could do following:

Source https://stackoverflow.com/questions/65508868

QUESTION

How does the Tensorflow's TripletSemiHardLoss and TripletHardLoss and how to use with Siamese Network?

Asked 2021-Jan-06 at 07:25

As much as I know that Triplet Loss is a Loss Function which decrease the distance between anchor and positive but decrease between anchor and negative. Also, there is a margin added to it.

So for EXAMPLE LEt us Suppose: a Siamese Network, which gives embeddings:

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ANSWER

Answered 2021-Jan-06 at 07:25

What is TripletHardLoss?

This loss follow the ordinary TripletLoss form, but using the maximum positive distance and minimum negative distance plus the margin constant within the batch when computing the loss, as we can see in the formula:

Look into source code of tfa.losses.TripletHardLoss we can see above formula been implement exactly:

Source https://stackoverflow.com/questions/65579247

QUESTION

Keras style lossless triplet loss

Asked 2020-Jan-06 at 17:25

I am trying to replicate the lossless tripler loss, but using the "K." syntax, like in my triplet loss below:

My code

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ANSWER

Answered 2020-Jan-06 at 17:25

Here's a way you can do:

Source https://stackoverflow.com/questions/59615096

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

Vulnerabilities

No vulnerabilities reported

Install Triplet-Loss

You can download it from GitHub.
You can use Triplet-Loss 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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