TorchGA
torchinfo
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TorchGA
- PyGAD 2.13.0 Released! A Python 3 Library for Building the Genetic Algorithm and Training Machine Learning Algorithms (Supports Keras and PyTorch)
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Train PyTorch Models Using Genetic Algorithm with PyGAD
Link of the GitHub project that prepares the PyTorch models for use with PyGAD: https://github.com/ahmedfgad/TorchGA
The source code of the pygad.torchga module is available at the ahmedfgad/TorchGA GitHub project.
torchinfo
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[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.
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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
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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?
koila - Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code.
QualityScaler - QualityScaler - image/video deeplearning upscaling for any GPU
de-torch - Minimal PyTorch Library for Differential Evolution
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.
PySR - High-Performance Symbolic Regression in Python and Julia
deepo - Setup and customize deep learning environment in seconds.
snntorch - Deep and online learning with spiking neural networks in Python
torchSR - Super Resolution datasets and models in Pytorch
bittensor - Internet-scale Neural Networks
merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments
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.