Author: Gabriel Bianconi
This project implements Restricted Boltzmann Machines (RBMs) using PyTorch (see rbm.py
). Our implementation includes momentum, weight decay, L2 regularization, and CD-k contrastive divergence. We also provide support for CPU and GPU (CUDA) calculations.
In addition, we provide an example file applying our model to the MNIST dataset (see mnist_dataset.py
). The example trains an RBM, uses the trained model to extract features from the images, and finally uses a SciPy-based logistic regression for classification. It achieves 92.8% classification accuracy (this is obviously not a cutting-edge model).
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。