Fully connected neural network for digit classification using MNIST data
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Updated
Apr 3, 2018 - Python
Fully connected neural network for digit classification using MNIST data
Generalization of Adam, AdaMax, AMSGrad algorithms for PyTorch
[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder
Reproducing the paper "PADAM: Closing The Generalization Gap of Adaptive Gradient Methods In Training Deep Neural Networks" for the ICLR 2019 Reproducibility Challenge
Custom Optimizer in TensorFlow(定义你自己的Tensorflow Optimizer)
A comparison between implementations of different gradient-based optimization algorithms (Gradient Descent, Adam, Adamax, Nadam, Amsgrad). The comparison was made on some of the most common functions used for testing optimization algorithms.
Quasi Hyperbolic Rectified DEMON Adam/Amsgrad with AdaMod, Gradient Centralization, Lookahead, iterative averaging and decorrelated Weight Decay
The implementation of the algorithm shows that OPTIMISTIC-AMSGRAD improves AMSGRAD in terms of various measures: training loss, testing loss, and classification accuracy on training/testing data over epochs.
Nadir: Cutting-edge PyTorch optimizers for simplicity & composability! 🔥🚀💻
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