Showing 3 open source projects for "squeeze"

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    PRTG Catches Network Issues Before They Cause Downtime

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    Build Agents and Models on One Platform

    Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

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  • 1
    Tensor Puzzles

    Tensor Puzzles

    Solve puzzles. Improve your pytorch

    ...Solutions must fit on one short line and use only a restricted set of indexing, arithmetic, comparison, and broadcasting tools. Standard convenience functions such as sum, view, squeeze, and take are intentionally prohibited. This constraint encourages a deeper understanding of shapes, indexing, and vectorized computation. Automated tests and visual examples help learners check their reasoning as they progress. The notebook is designed for Google Colab and is accompanied by a video walkthrough.
    Downloads: 0 This Week
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  • 2
    U-Net Fusion RFI

    U-Net Fusion RFI

    U-Net for RFI Detection based on @jakeret's implementation

    ...The primary improvements to this code base include a training and evaluation framework, along with a fusion based approach to detection, combining a number of models (currently hard coded to two trained models) along with Sum Threshold as an additional "expert." Additional work is being done to add custom layers to this model for further experimentation, including Squeeze/Excitation layers (unimplemented.) Sum Threshold (in fusion as an expert, and in testing as a comparison) requires the use of AOFlagger by Andre Offringa. You can find this code at https://gitlab.com/aroffringa/aoflagger. This project will use the aoflagger program within the code, so you may need to ensure that any environment variables are set for aoflagger before use. ...
    Downloads: 0 This Week
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  • 3
    Tensorpack

    Tensorpack

    A Neural Net Training Interface on TensorFlow, with focus on speed

    ...On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. Tensorpack squeezes the most performance out of pure Python with various auto parallelization strategies. There are too many symbolic function wrappers already. ...
    Downloads: 0 This Week
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