install_torch
torchinfo
| install_torch | torchinfo | |
|---|---|---|
| 2 | 3 | |
| 0 | 2,307 | |
| - | - | |
| 3.1 | 6.9 | |
| about 1 year ago | 8 days ago | |
| Python | Python | |
| MIT License | MIT License |
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.
install_torch
torchinfo
-
[D] PyTorch and Tensorflow Performance Different on the same model, dataset and hyperparameters
It may be a good idea to compare the implementation of the models using Keras's Model.summary and PyTorch's torchinfo.
-
after adding a nn.Dropout() layer, the number of parameters won't change?
No they wont change. If you are interested in viewing parameters check out Torchinfo https://github.com/TylerYep/torchinfo
-
zero_grad() is supposed to be invoked every time one data point passed? How does a scalar.backward() from a loss function affect another model parameters?
You can use torchinfo, it has a # params column and it's a nice way to log and debug a NN architecture to make sure all the layers you expect are connected.
What are some alternatives?
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
QualityScaler - QualityScaler - image/video deeplearning upscaling for any GPU
Cozy-Auto-Texture - A Blender add-on for generating free textures using the Stable Diffusion AI text to image model.
MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
stable-diffusion-webui - Stable Diffusion web UI
deepo - Setup and customize deep learning environment in seconds.
torchSR - Super Resolution datasets and models in Pytorch
merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.