deepo
torchinfo
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deepo | torchinfo | |
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1 | 3 | |
6,314 | 2,266 | |
- | - | |
3.0 | 7.1 | |
about 1 year ago | 8 days ago | |
Python | Python | |
MIT License | MIT License |
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deepo
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Base image with both PyTorch and Tensorflow?
I found an image though, though there's some setup involved: https://github.com/ufoym/deepo
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?
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
QualityScaler - QualityScaler - image/video deeplearning upscaling for any GPU
netron - Visualizer for neural network, deep learning and machine learning models
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.
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
Jetson-Nano-Ubuntu-20-image - Jetson Nano with Ubuntu 20.04 image
torchSR - Super Resolution datasets and models in Pytorch
zipslicer - A library for incremental loading of large PyTorch checkpoints
merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models
optimizer - Actively maintained ONNX Optimizer
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.