MMdnn
torchinfo
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| MMdnn | torchinfo | |
|---|---|---|
| 3 | 3 | |
| 5,780 | 2,280 | |
| 0.2% | - | |
| 0.0 | 7.1 | |
| 7 months ago | 6 days ago | |
| Python | Python | |
| MIT License | MIT License |
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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.
MMdnn
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[R] Importing TensorFlow neural networks stored as *.h5
The only way I found online is to convert the *.h5 files to MXNet files using Microsoft model management of deep neural networks (link)
- [D] Tools for converting TF code to Pytorch
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Apple’s New M1 Chip is a Machine Learning Beast
Yes. But that's missing the point. Almost everything in the space uses a framework other than Core ML. Therefore most people need good development support for PyTorch/Tensorflow etc., not Core ML. The fact that Apple has a tool to import/convert models is nice, but not relevant. Also, there is onnx as an exchange format between the frameworks, and tools like MMdnn and others.
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?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
QualityScaler - QualityScaler - image/video deeplearning upscaling for any GPU
keras-onnx - Convert tf.keras/Keras models to ONNX
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
redisai-examples - RedisAI showcase
deepo - Setup and customize deep learning environment in seconds.
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
torchSR - Super Resolution datasets and models in Pytorch
keras-ncp - PyTorch and TensorFlow implementation of NCP, LTC, and CfC wired neural models
merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models
coremltools - Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
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.