tf-keras-vis
Neural network visualization toolkit for tf.keras (by keisen)
autokeras
AutoML library for deep learning (by keras-team)
tf-keras-vis | autokeras | |
---|---|---|
1 | 5 | |
306 | 9,066 | |
- | 0.1% | |
6.9 | 5.3 | |
about 1 month ago | about 1 month ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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.
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.
autokeras
Posts with mentions or reviews of autokeras.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-27.
- Machine Learning Algorithms Cheat Sheet
-
Ask HN: Which piece of tech is underutilized?
I think the interfaces aren't high level enough for the average programmer to adopt it. It needs what https://autokeras.com is for neural nets.
- Technical documentation that just works
- SVM training taking forever on my local machine. Will using AWS Sagemaker be faster for training SVM (Linear) models?
-
[D] [P] How do you use tools like AutoML?
AutoKeras time_series_forecaster.py
What are some alternatives?
When comparing tf-keras-vis and autokeras 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.
autogluon - Fast and Accurate ML in 3 Lines of Code