whitebox
[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes (by squaredev-io)
tf-keras-vis
Neural network visualization toolkit for tf.keras (by keisen)
whitebox | tf-keras-vis | |
---|---|---|
12 | 1 | |
181 | 306 | |
0.6% | - | |
2.8 | 6.9 | |
10 months ago | about 2 months ago | |
Python | Python | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
whitebox
Posts with mentions or reviews of whitebox.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-06.
- [P] We made an open source ML / data monitoring platform
-
A machine learning monitoring platform
Check it out on GitHub: https://github.com/squaredev-io/whitebox And, read the docs here: https://squaredev-io.github.io/whitebox/
- A new ML Monitoring platform. Stars help a lot 🤟
- I'm doing some work on the SDK of Whitebox, a new ML monitoring platform. First time building an SQD for mass consumption in python so feedback is essential. Here is the PR link.
- Hello, I am creating an open source ML monitoring platform. It's still early work and feedback is much appreciated. There is also a planned MLFlow integration. What do you think?
- We just open-sourced Whitebox for machine learning models monitoring!!!
- We just open-sourced Whitebox for machine learning models monitoring....
- We just open-sourced Whitebox for AI models monitoring....
tf-keras-vis
Posts with mentions or reviews of tf-keras-vis.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Help implementing an attention module in a DCGAN (question in comments)
Hello! I'm trying to implement the Deep convolutional GAN of this paper: Weather GAN: Multi-Domain Weather Translation Using Generative Adversarial Networks by Xuelong Li, Kai Kou, Bin Zhao (arxiv) (architecture in image). The training data the authors used consists of images and correspondingsegmentation masks with labels 0-5 for each pixel (0 being no weather-relatedpixel). I have crudely made the segmentation module G(seg), Initialgenerator module G(init) and the Discriminator D, but I don'tunderstand how to do the attention module G(att). In the paper theymentioned they used pretained weights of VGG19, but very little else is saidabout G(att).I found this https://github.com/keisen/tf-keras-vislibrary, which might help me, as I guess I would want G(att) to extractsomething like the saliency or activation maps multiplied with the image.However, I don't know what kind of layers I should use, or what practicalitiesto use apart from the input and output. Should I transfer-learn the network withthis data, and if so, with the segmentation labels (i.e. if any label 1-5 ispresent in the attention pixel returned)? Or can I use the pre-trainedimagenet?Also, does anyone know if the layer colors in thearchitecture image mean anything, or if they are just selected randomly forvisualization? Especially I'm concerned why G(att)'s last encoderlayer (3rd layer) is colored differently from the first two. I was firstthinking that maybe it means that G(att) is a module inside G(seg),but apparently not. The three middle blocks are apparently residual blocks.
What are some alternatives?
When comparing whitebox and tf-keras-vis you can also consider the following projects:
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
autokeras - AutoML library for deep learning
chitra - A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
livelossplot - Live training loss plot in Jupyter Notebook for Keras, PyTorch and others
explainable-cnn - 📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
easy_explain - A library that helps to explain AI models in a really quick & easy way